Search results for: magnetic noise

Authors: Nebojsa Stefanovic, Sasa Milijic, Natasa Danilovic Hristic

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Spatial plan of the special purpose area constitutes a basic tool in the planning of infrastructure corridor of a highway. The aim of the plan is to define the planning basis and provision of spatial conditions for the construction and operation of the highway, as well as for developing other infrastructure systems in the corridor. This paper presents a methodology and approach to the preparation of the Spatial Plan for the special purpose area for the infrastructure corridor of the highway E-80, Section Niš-Merdare in Serbia. The applied methodological approach is based on the combined application of the integrative and participatory method in the decision-making process on the sustainable development of the highway corridor. It was found that, for the planning and management of the infrastructure corridor, a key problem is coordination of spatial and urban planning, strategic environmental assessment and sectoral traffic planning and designing. Through the development of the plan, special attention is focused on increasing the accessibility of the local and regional surrounding, reducing the adverse impacts on the development of settlements and the economy, protection of natural resources, natural and cultural heritage, and the development of other infrastructure systems in the corridor of the highway. As a result of the applied methodology, this paper analyzes the basic features such as coverage, the concept, protected zones, service facilities and objects, the rules of development and construction, etc. Special emphasis is placed to methodology and results of the Strategic Environmental Assessment of the Spatial Plan, and to the importance of protection measures, with the special significance of air and noise protection measures. For evaluation in the Strategic Environmental Assessment, a multicriteria expert evaluation (semi-quantitative method) of planned solutions was used in relation to the set of goals and relevant indicators, based on the basic set of indicators of sustainable development. Evaluation of planned solutions encompassed the significance and size, spatial conditions and probability of the impact of planned solutions on the environment, and the defined goals of strategic assessment. The framework of the implementation of the Spatial Plan is presented, which is determined for the simultaneous elaboration of planning solutions at two levels: the strategic level of the spatial plan and detailed urban plan level. It is also analyzed the relationship of the Spatial Plan to other applicable planning documents for the planning area. The effects of this methodological approach relate to enabling integrated planning of the sustainable development of the infrastructure corridor of the highway and its surrounding area, through coordination of spatial, urban and sectoral traffic planning and design, as well as the participation of all key actors in the adoption and implementation of planned decisions. By the conclusions of the paper, it is pointed to the direction for further research, particularly in terms of harmonizing methodology of planning documentation and preparation of technical-design documentation.

Keywords: corridor, environment, highway, impact, methodology, spatial plan, urban

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Authors: Oscar Nieto-Garzón, Angélica Lozano

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In urban areas, on-road freight transportation creates several social and environmental externalities. Then, it is crucial that freight transport considers not only economic aspects, like retailer distribution cost reduction and service improvement, but also environmental effects such as global CO2 and local emissions (e.g. Particulate Matter, NOX, CO) and noise. Inadequate infrastructure development, high rate of urbanization, the increase of motorization, and the lack of transportation planning are characteristics that urban areas from developing countries share. The Metropolitan Area of Mexico City (MAMC), the Metropolitan Area of São Paulo (MASP), and Bogota are three of the largest urban areas in Latin America where air pollution is often a problem associated with emissions from mobile sources. The effect of the lockdown due to COVID-19 was analyzedfor these urban areas, comparing the same period (January to August) of years 2016 – 2019 with 2020. A strong reduction in the concentration of primary criteria pollutants emitted by road traffic were observed at the beginning of 2020 and after the lockdown measures.Daily mean concentration of NOx decreased 40% in the MAMC, 34% in the MASP, and 62% in Bogota. Daily mean ozone levels increased after the lockdown measures in the three urban areas, 25% in MAMC, 30% in the MASP and 60% in Bogota. These changes in emission patterns from mobile sources drastically changed the ambient atmospheric concentrations of CO and NOX. The CO/NOX ratioat the morning hours is often used as an indicator of mobile sources emissions. In 2020, traffic from cars and light vehicles was significantly reduced due to the first lockdown, but buses and trucks had not restrictions. In theory, it implies a decrease in CO and NOX from cars or light vehicles, maintaining the levels of NOX by trucks(or lower levels due to the congestion reduction). At rush hours, traffic was reduced between 50% and 75%, so trucks could get higher speeds, which would reduce their emissions. By means an emission model, it was found that an increase in the average speed (75%) would reduce the emissions (CO, NOX, and PM) from diesel trucks by up to 30%. It was expected that the value of CO/NOXratio could change due to thelockdownrestrictions. However, although there was asignificant reduction of traffic, CO/NOX kept its trend, decreasing to 8-9 in 2020. Hence, traffic restrictions had no impact on the CO/NOX ratio, although they did reduce vehicle emissions of CO and NOX. Therefore, these emissions may not adequately represent the change in the vehicle emission patterns, or this ratio may not be a good indicator of emissions generated by vehicles. From the comparison of the theoretical data and those observed during the lockdown, results that the real NOX reduction was lower than the theoretical reduction. The reasons could be that there are other sources of NOX emissions, so there would be an over-representation of NOX emissions generated by diesel vehicles, or there is an underestimation of CO emissions. Further analysis needs to consider this ratioto evaluate the emission inventories and then to extend these results forthe determination of emission control policies to non-mobile sources.

Keywords: COVID-19, emissions, freight transport, latin American metropolis

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Authors: Stefano Zaninotto, Geoffrey Farrugia, Johan Debattista, Jason Gauci

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The need to reduce fuel and noise during taxi operations in the airports with a scenario of constantly increasing air traffic has resulted in an effort by the aerospace industry to move towards electric taxiing. In fact, this is one of the problems that is currently being addressed by SESAR JU and two main solutions are being proposed. With the first solution, electric motors are installed in the main (or nose) landing gear of the aircraft. With the second solution, manned or unmanned electric tow trucks are used to tow aircraft from the gate to the runway (or vice-versa). The presence of the tow trucks results in an increase in vehicle traffic inside the airport. Therefore, it is important to design the system in a way that the workload of Air Traffic Control (ATC) is not increased and the system assists ATC in managing all ground operations. The aim of this work is to develop an electric taxiing system, based on the use of autonomous tow trucks, which optimizes aircraft ground operations while keeping ATC in the loop. This system will consist of two components: an optimization tool and a Graphical User Interface (GUI). The optimization tool will be responsible for determining the optimal path for arriving and departing aircraft; allocating a tow truck to each taxiing aircraft; detecting conflicts between aircraft and/or tow trucks; and proposing solutions to resolve any conflicts. There are two main optimization strategies proposed in the literature. With centralized optimization, a central authority coordinates and makes the decision for all ground movements, in order to find a global optimum. With the second strategy, called decentralized optimization or multi-agent system, the decision authority is distributed among several agents. These agents could be the aircraft, the tow trucks, and taxiway or runway intersections. This approach finds local optima; however, it scales better with the number of ground movements and is more robust to external disturbances (such as taxi delays or unscheduled events). The strategy proposed in this work is a hybrid system combining aspects of these two approaches. The GUI will provide information on the movement and status of each aircraft and tow truck, and alert ATC about any impending conflicts. It will also enable ATC to give taxi clearances and to modify the routes proposed by the system. The complete system will be tested via computer simulation of various taxi scenarios at multiple airports, including Malta International Airport, a major international airport, and a fictitious airport. These tests will involve actual Air Traffic Controllers in order to evaluate the GUI and assess the impact of the system on ATC workload and situation awareness. It is expected that the proposed system will increase the efficiency of taxi operations while reducing their environmental impact. Furthermore, it is envisaged that the system will facilitate various controller tasks and improve ATC situation awareness.

Keywords: air traffic control, electric taxiing, autonomous tow trucks, graphical user interface, ground operations, multi-agent, route optimization

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Rachel M. Edelstein, John D. Van Horn, Karen M. Schmidt, Sydney N. Cushing

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As a consequence of sports-related concussions, female athletes have been documented as reporting more symptoms than their male counterparts, in addition to incurring longer periods of recovery. However, the role of sex and its potential influence on symptom reporting and recovery outcomes in concussion management has not been completely explored. The present aims to investigate the relationship between female concussion symptom severity and the presence of assessment bias. The Sport Concussion Assessment Tool 3 (SCAT3), collected by the NCAA and DoD CARE Consortium, was quantified at five different time points post-concussion. N= 1,258 NCAA athletes, n= 473 female (soccer, rugby, lacrosse, ice hockey) and n=785 male athletes (football, rugby, lacrosse, ice hockey). A polytomous Item Response Theory (IRT) Graded Response Model (GRM) was used to assess the relationship between sex and symptom reporting. Differential Item Functioning (DIF) and Differential Group Functioning (DGF) were used to examine potential group-level bias. Interactions for DIF were utilized to explore the impact of sex on symptom reporting among NCAA male and female athletes throughout and after their concussion recovery. DIF was significantly detected after B-H corrections displayed in limited items; however, one symptom, “Pressure in Head” (-0.29, p=0.04 vs -0.20, p =0.04), was statistically significant at both < 6 hours and 24-48 hours. Thus, implies that at < 6 hours, males were 29% less likely to indicate “Pressure in the Head” compared to female athletes and 20% less likely at 24-48 hours. Overall, the DGF suggested significant group differences, suggesting that male athletes might be at a higher risk for returning to play prematurely (logits = -0.38, p < 0.001). However, after analyzing the SCAT 3, a clinically relevant trend was discovered. Twelve out of the twenty-two symptoms suggest higher difficulty in female athletes within three or more of the five-time points. These symptoms include Balance Problems, Blurry Vision, Confusion, Dizziness, Don’t Feel Right, Feel in Fog, Feel Slow Down, Low Energy, Neck Pain, Sensitivity to Light, Sensitivity to Noise, Trouble Falling Asleep. Despite a lack of statistical significance, this tendency is contrary to current literature stating that males may be unclear on symptoms, but females may be more honest in reporting symptoms. Further research, which includes possible modifying socioecological factors, is needed to determine whether females may consistently experience more symptoms and require longer recovery times or if, parsimoniously, males tend to present their symptoms and readiness for play differently than females. Such research will help to improve the validity of current assumptions concerning male as compared to female head injuries and optimize individualized treatments for sports-related head injuries.

Keywords: female athlete, sports-related concussion, item response theory, concussion assessment

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Authors: Maya de Vries Kedem, Abir Jubran, Diana Baron

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Employment in Israel offers Palestinian workers an income double what they can earn in the West Bank. The need to support their families leads many educated Palestinians to forgo finding work in their profession in the Palestinian Authority and instead look for employment in those sectors open to them in Israel, particularly the construction, agriculture, and industry sectors. The International Labor Organization estimated that about 1,200 workers in Israel die every year because of occupational diseases (diseases caused by working conditions). Construction workers in Israel are constantly exposed to dust, noise, chemical materials, and work in awkward postures, which require prolonged bending, repetitive motion, and other risk factors that can lead to illnesses and death. Occupational health is vastly neglected in Israel and construction workers are particularly at risk . As of June 2022, the Israeli quota in the construction sector for Palestinian workers stood at 80,000. Kav LaOved released a new study on the state of occupational health among Palestinian workers employed in construction in Israel. The study Roadmap to Health: Palestinian Workers in Israel's Construction Sector reviews the extent to which the health of Palestinian workers is protected at work in Israel. The report includes analysis of a survey administered to 256 workers as well as interviews with 10 workers and with 5 Israeli occupational health experts. Report highlights: • Among survey respondents, 63.9% stated that safety procedures to protect their health are rarely followed in their workplace (e.g., taking breaks, using protective gear, following restrictions on lifting heavy items, and having inspectors regularly on site to monitor safety). • All 256 Palestinian workers who participated to the survey said that their health has been directly or indirectly harmed by working in Israel and reported suffering from the following problems: orthopedic problems such as joint, hand, leg or knee problems (100%); headaches (75%); back problems (36.3%); eye problems (23.8%); breathing problems (17.6%); chronic pain (14.8%); heart problems (7.8%); and skin problems (3.5%). • Workers who are injured or do not feel well often continue working for fear of losing their payment for that day. About half of the 256 survey respondents reported that they pay brokerage fees to find an employer with a work permit, often paying between 2,000 and 3,000 NIS per month. “I have an obligation—I pay about NIS 120 a day for my permit, [and] I have to pay for it whether I work or not" a worker said. • Most Palestinian construction workers suffer from stress and mental health problems. Workers pointed to several issues that greatly affect their mood and mental state: daily crossings at crowded checkpoints where workers stand for hours; lack of sleep due to leaving home daily at 3:00-3:30 am; commuting two to four hours to work in each direction; and abusive work environments. A worker told KLO that the sight of thousands of workers standing together at the checkpoint causes “high blood pressure and the feeling that you are going to be squeezed.” Another said, “I felt that my bones would break.” In the survey workers reported suffering from insomnia (70.1%), breathing difficulties (35.8%), chest pressure (27.6%), or rapid pulse rate (12.2%).

Keywords: construction sector, palestinian workers, occupational health, Israel, occupation

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Authors: Darren A. Whitaker, Dan Palmer, Jens Wesholowski, James Flaherty, John Mack, Ahmad B. Albadarin, Gavin Walker

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Traditional mechanosynthesis has been performed by either ball milling or manual grinding. However, neither of these techniques allow the easy application of process control. The temperature may change unpredictably due to friction in the process. Hence the amount of energy transferred to the reactants is intrinsically non-uniform. Recently, it has been shown that the use of Twin-Screw extrusion (TSE) can overcome these limitations. Additionally, TSE enables a platform for continuous synthesis or manufacturing as it is an open-ended process, with feedstocks at one end and product at the other. Several materials including metal-organic frameworks (MOFs), co-crystals and small organic molecules have been produced mechanochemically using TSE. The described advantages of TSE are offset by drawbacks such as increased process complexity (a large number of process parameters) and variation in feedstock flow impacting on product quality. To handle the above-mentioned drawbacks, this study utilizes UV-Vis spectrophotometry (InSpectroX, ColVisTec) as an online tool to gain real-time information about the quality of the product. Additionally, this is combined with real-time process information in an Advanced Process Control system (PharmaMV, Perceptive Engineering) allowing full supervision and control of the TSE process. Further, by characterizing the dynamic behavior of the TSE, a model predictive controller (MPC) can be employed to ensure the process remains under control when perturbed by external disturbances. Two reactions were studied; a Knoevenagel condensation reaction of barbituric acid and vanillin and, the direct amidation of hydroquinone by ammonium acetate to form N-Acetyl-para-aminophenol (APAP) commonly known as paracetamol. Both reactions could be carried out continuously using TSE, nuclear magnetic resonance (NMR) spectroscopy was used to confirm the percentage conversion of starting materials to product. This information was used to construct partial least squares (PLS) calibration models within the PharmaMV development system, which relates the percent conversion to product to the acquired UV-Vis spectrum. Once this was complete, the model was deployed within the PharmaMV Real-Time System to carry out automated optimization experiments to maximize the percentage conversion based on a set of process parameters in a design of experiments (DoE) style methodology. With the optimum set of process parameters established, a series of PRBS process response tests (i.e. Pseudo-Random Binary Sequences) around the optimum were conducted. The resultant dataset was used to build a statistical model and associated MPC. The controller maximizes product quality whilst ensuring the process remains at the optimum even as disturbances such as raw material variability are introduced into the system. To summarize, a combination of online spectral monitoring and advanced process control was used to develop a robust system for optimization and control of two TSE based mechanosynthetic processes.

Keywords: continuous synthesis, pharmaceutical, spectroscopy, advanced process control

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Authors: Katherine Dropiewski, Michael Yakimov, Vadim Tokranov, Allan Minns, Pavel Murat, Serge Oktyabrsky

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InAs Quantum Dots (QDs) in a GaAs matrix is a well-documented luminescent material with high light yield, as well as thermal and ionizing radiation tolerance due to quantum confinement. These benefits can be leveraged for high-efficiency, room temperature scintillation detectors. The proposed scintillator is composed of InAs QDs acting as luminescence centers in a GaAs stopping medium, which also acts as a waveguide. This system has appealing potential properties, including high light yield (~240,000 photons/MeV) and fast capture of photoelectrons (2-5ps), orders of magnitude better than currently used inorganic scintillators, such as LYSO or BaF2. The high refractive index of the GaAs matrix (n=3.4) ensures light emitted by the QDs is waveguided, which can be collected by an integrated photodiode (PD). Scintillation structures were grown using Molecular Beam Epitaxy (MBE) and consist of thick GaAs waveguiding layers with embedded sheets of modulation p-type doped InAs QDs. An AlAs sacrificial layer is grown between the waveguide and the GaAs substrate for epitaxial lift-off to separate the scintillator film and transfer it to a low-index substrate for waveguiding measurements. One consideration when using a low-density material like GaAs (~5.32 g/cm³) as a stopping medium is the matrix thickness in the dimension of radiation collection. Therefore, luminescence properties of very thick (4-20 microns) waveguides with up to 100 QD layers were studied. The optimization of the medium included QD shape, density, doping, and AlGaAs barriers at the waveguide surfaces to prevent non-radiative recombination. To characterize the efficiency of QD luminescence, low temperature photoluminescence (PL) (77-450 K) was measured and fitted using a kinetic model. The PL intensity degrades by only 40% at RT, with an activation energy for electron escape from QDs to the barrier of ~60 meV. Attenuation within the waveguide (WG) is a limiting factor for the lateral size of a scintillation detector, so PL spectroscopy in the waveguiding configuration was studied. Spectra were measured while the laser (630 nm) excitation point was scanned away from the collecting fiber coupled to the edge of the WG. The QD ground state PL peak at 1.04 eV (1190 nm) was inhomogeneously broadened with FWHM of 28 meV (33 nm) and showed a distinct red-shift due to self-absorption in the QDs. Attenuation stabilized after traveling over 1 mm through the WG, at about 3 cm⁻¹. Finally, a scintillator sample was used to test detection and evaluate timing characteristics using 5.5 MeV alpha particles. With a 2D waveguide and a small area of integrated PD, the collected charge averaged 8.4 x10⁴ electrons, corresponding to a collection efficiency of about 7%. The scintillation response had 80 ps noise-limited time resolution and a QD decay time of 0.6 ns. The data confirms unique properties of this scintillation detector which can be potentially much faster than any currently used inorganic scintillator.

Keywords: GaAs, InAs, molecular beam epitaxy, quantum dots, III-V semiconductor

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Authors: Safaa M. Hassan; Safwat S. Gabr, Mohamed F. Sadek

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This study is proposed for the lithological and iron oxides detection in the old mine areas of El-Bahariya Depression, Western Desert, using ASTER and Landsat-8 remote sensing data. Four old iron ore occurrences, namely; El-Gedida, El-Haraa, Ghurabi, and Nasir mine areas found in the El-Bahariya area. This study aims to find new high potential areas for iron mineralization around El-Baharyia depression. Image processing methods such as principle component analysis (PCA) and band ratios (b4/b5, b5/b6, b6/b7, and 4/2, 6/7, band 6) images were used for lithological identification/mapping that includes the iron content in the investigated area. ASTER and Landsat-8 visible and short-wave infrared data found to help mapping the ferruginous sandstones, iron oxides as well as the clay minerals in and around the old mines area of El-Bahariya depression. Landsat-8 band ratio and the principle component of this study showed well distribution of the lithological units, especially ferruginous sandstones and iron zones (hematite and limonite) along with detection of probable high potential areas for iron mineralization which can be used in the future and proved the ability of Landsat-8 and ASTER data in mapping these features. Minimum Noise Fraction (MNF), Mixture Tuned Matched Filtering (MTMF), pixel purity index methods as well as Spectral Ange Mapper classifier algorithm have been successfully discriminated the hematite and limonite content within the iron zones in the study area. Various ASTER image spectra and ASD field spectra of hematite and limonite and the surrounding rocks are compared and found to be consistent in terms of the presence of absorption features at range from 1.95 to 2.3 μm for hematite and limonite. Pixel purity index algorithm and two sub-pixel spectral methods, namely Mixture Tuned Matched Filtering (MTMF) and matched filtering (MF) methods, are applied to ASTER bands to delineate iron oxides (hematite and limonite) rich zones within the rock units. The results are validated in the field by comparing image spectra of spectrally anomalous zone with the USGS resampled laboratory spectra of hematite and limonite samples using ASD measurements. A number of iron oxides rich zones in addition to the main surface exposures of the El-Gadidah Mine, are confirmed in the field. The proposed method is a successful application of spectral mapping of iron oxides deposits in the exposed rock units (i.e., ferruginous sandstone) and present approach of both ASTER and ASD hyperspectral data processing can be used to delineate iron-rich zones occurring within similar geological provinces in any parts of the world.

Keywords: Landsat-8, ASTER, lithological mapping, iron exploration, western desert

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: K. Mohon Singh

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Cambro-Ordovician (512.5 ± 8.7 Ma) felsic magmatism in the Kyrdem region of Meghalaya plateau, herewith referred to as Kyrdem granitoids (KG), intrudes the low-grade Shillong Group of metasediments and Precambrian Basement Gneissic complex forming an oval-shaped plutonic body with longer axis almost trending N-S. Thermal aureole is poorly developed or covered under the alluvium. KG exhibit very coarse grained porphyritic texture with abundant K-feldspar megacrysts (up to 9cm long) and subordinate amount of amphibole, biotite, plagioclase, and quartz. The size of K-feldspar megacrysts increases from margin (Dwarksuid) to the interior (Kyrdem) of the KG pluton. Late felsic pulses as fine grained granite, leucocratic (aplite), and pegmatite veins intrude the KG at several places. Grey and pink varieties of KG can be recognized, but pink colour of KG is the result of post-magmatic fluids, which have not affected the magnetic properties of KG. Modal composition of KG corresponds to quartz monzonite, monzogranite, and granodiorite. KG has been geochemically characterized as metaluminous (I-type) to peraluminous (S-type) granitoids. The KG is characterized by development of variable attitude of primary foliations mostly marked along the margin of the pluton and is located at the proximity of Tyrsad-Barapani lineament. The KG contains country rock xenoliths (amphibolite, gneiss, schist, etc.) which are mostly confined to the margin of the pluton, and microgranular enclaves (ME) are hosted in the porphyritic variety of KG. Microgranular Enclaves (ME) in Kyrdem Granitoids are fine- to medium grained, mesocratic to melanocratic, phenocryst bearing or phenocryst-free, rounded to ellipsoidal showing typical magmatic textures. Mafic-felsic phenocrysts in ME are partially corroded and dissolved because of their involvement in magma-mixing event, and thus represent xenocrysts. Sharp to diffused contacts of ME with host Kyrdem Granitoids, fine grained nature and presence of acicular apatite in ME suggest comingling and undercooling of coeval, semi-solidified ME magma within partly crystalline felsic host magma. Geochemical features recognize the nature of ME (molar A/CNK=0.76-1.42) and KG (molar A/CNK =0.41-1.75) similar to hybrid-type formed by mixing of mantle-derived mafic and crustal-derived felsic magmas. Major and trace including rare earth elements variations of ME suggest the involvement of combined processes such as magma mixing, mingling and crystallization differentiation in the evolution of ME but KG variations appear primarily controlled by fractionation of plagioclase, hornblende biotite, and accessory phases. Most ME are partially to nearly re-equilibrate chemically with felsic host KG during magma mixing and mingling processes.

Keywords: geochemistry, Kyrdem Granitoids, microgranular enclaves, Northeast India

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Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

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Authors: B. K. Kanungo, Monika Thakur, Minati Baral

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8-hydroxyquinoline, (8HQ), experiences a renaissance due to its utility as a building block in metallosupramolecular chemistry and its versatile use of its derivatives in various fields of analytical chemistry, materials science, and pharmaceutics. It forms stable complexes with a variety of metal ions. Assembly of more than one such unit to form a polydentate chelator enhances its coordinating ability and the related properties due to the chelate effect resulting in high stability constant. Keeping in view the above, a nonadentate chelator N-[3,5-bis(8-hydroxyquinoline-2-amido)cyclohexyl]-8-hydroxyquinoline-2-carboxamide, (TACH2OX), containing a central cis,cis-1,3,5-triaminocyclohexane appended to three 8-hydroxyquinoline at 2-position through amide linkage is developed, and its solution thermodynamics, photophysical and Density Functional Theory (DFT) studies were undertaken. The synthesis of TACH2OX was carried out by condensation of cis,cis-1,3,5-triaminocyclohexane, (TACH) with 8‐hydroxyquinoline‐2‐carboxylic acid. The brown colored solid has been fully characterized through melting point, infrared, nuclear magnetic resonance, electrospray ionization mass and electronic spectroscopy. In solution, TACH2OX forms protonated complexes below pH 3.4, which consecutively deprotonates to generate trinegative ion with the rise of pH. Nine protonation constants for the ligand were obtained that ranges between 2.26 to 7.28. The interaction of the chelator with two trivalent metal ion Fe3+ and Al3+ were studied in aqueous solution at 298 K. The metal-ligand formation constants (ML) obtained by potentiometric and spectrophotometric method agree with each other. The protonated and hydrolyzed species were also detected in the system. The in-silico studies of the ligand, as well as the complexes including their protonated and deprotonated species assessed by density functional theory technique, gave an accurate correlation with each observed properties such as the protonation constants, stability constants, infra-red, nmr, electronic absorption and emission spectral bands. The nature of electronic and emission spectral bands in terms of number and type were ascertained from time-dependent density functional theory study and the natural transition orbitals (NTO). The global reactivity indices parameters were used for comparison of the reactivity of the ligand and the complex molecules. The natural bonding orbital (NBO) analysis could successfully describe the structure and bonding of the metal-ligand complexes specifying the percentage of contribution in atomic orbitals in the creation of molecular orbitals. The obtained high value of metal-ligand formation constants indicates that the newly synthesized chelator is a very powerful synthetic chelator. The minimum energy molecular modeling structure of the ligand suggests that the ligand, TACH2OX, in a tripodal fashion firmly coordinates to the metal ion as hexa-coordinated chelate displaying distorted octahedral geometry by binding through three sets of N, O- donor atoms, present in each pendant arm of the central tris-cyclohexaneamine tripod.

Keywords: complexes, DFT, formation constant, TACH2OX

Procedia PDF Downloads 117

Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

Procedia PDF Downloads 108

Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

Procedia PDF Downloads 49

Authors: Stanislavs Gendelis, Maris Sinka, Andris Jakovics

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Insulation materials made from natural sources have become more popular for the ecologisation of buildings, meaning wide use of such renewable materials. Such natural materials replace synthetic products which consume a large quantity of energy. The most common and the cheapest natural materials in Latvia are cellulose-based (wood and agricultural plants). The ecological aspects of such materials are well known, but experimental data about physical properties remains lacking. In this study, six different samples of wood wool panels and a mixture of hemp shives and lime (hempcrete) are analysed. Thermal conductivity and heat capacity measurements were carried out for wood wool and cement panels using the calibrated hot plate device. Water vapor permeability was tested for hempcrete material by using the gravimetric dry cup method. Studied wood wool panels are eco-friendly and harmless material, which is widely used in the interior design of public and residential buildings, where noise absorption and sound insulation is of importance. They are also suitable for high humidity facilities (e.g., swimming pools). The difference in panels was the width of used wood wool, which is linked to their density. The results of measured thermal conductivity are in a wide range, showing the worsening of properties with the increasing of the wool width (for the least dense 0.066, for the densest 0.091 W/(m·K)). Comparison with mineral insulation materials shows that thermal conductivity for such materials are 2-3 times higher and are comparable to plywood and fibreboard. Measured heat capacity was in a narrower range; here, the dependence on the wool width was not so strong due to the fact that heat capacity value is related to mass, not volume. The resulting heat capacity is a combination of two main components. A comparison of results for different panels allows to select the most suitable sample for a specific application because the dependencies of the thermal insulation and heat capacity properties on the wool width are not the same. Hempcrete is a much denser material compared to conventional thermal insulating materials. Therefore, its use helps to reinforce the structural capacity of the constructional framework, at the same time, it is lightweight. By altering the proportions of the ingredients, hempcrete can be produced as a structural, thermal, or moisture absorbent component. The water absorption and water vapor permeability are the most important properties of these materials. Information about absorption can be found in the literature, but there are no data about water vapor transmission properties. Water vapor permeability was tested for a sample of locally made hempcrete using different air humidity values to evaluate the possible difference. The results show only the slight influence of the air humidity on the water vapor permeability value. The absolute ‘sd value’ measured is similar to mineral wool and wood fiberboard, meaning that due to very low resistance, water vapor passes easily through the material. At the same time, other properties – structural and thermal of the hempcrete is totally different. As a result, an experimentally-based knowledge of thermal and water vapor transmission properties for cellulose-based materials was significantly improved.

Keywords: heat capacity, hemp concrete, thermal conductivity, water vapor transmission, wood wool

Procedia PDF Downloads 199

Authors: Bruno RamaëL, GwenaëL Page, Catherine Knopf-Lenoir, Olivier Baledent, Anne-Virginie Salsac

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No routine technique currently exists for clinicians to measure the mechanical properties of vascular walls non-invasively. Most of the data available in the literature come from traction or dilatation tests conducted ex vivo on native blood vessels. The objective of the study is to develop a non-invasive characterization technique based on Magnetic Resonance Imaging (MRI) measurements of the deformation of vascular walls under pulsating blood flow conditions. The goal is to determine the mechanical properties of the vessels by inverse analysis, coupling imaging measurements and numerical simulations of the fluid-structure interactions. The hyperelastic properties are identified using Solidworks and Ansys workbench (ANSYS Inc.) solving an optimization technique. The vessel of interest targeted in the study is the common carotid artery. In vivo MRI measurements of the vessel anatomy and inlet velocity profiles was acquired along the facial vascular network on a cohort of 30 healthy volunteers: - The time-evolution of the blood vessel contours and, thus, of the cross-section surface area was measured by 3D imaging angiography sequences of phase-contrast MRI. - The blood flow velocity was measured using a 2D CINE MRI phase contrast (PC-MRI) method. Reference arterial pressure waveforms were simultaneously measured in the brachial artery using a sphygmomanometer. The three-dimensional (3D) geometry of the arterial network was reconstructed by first creating an STL file from the raw MRI data using the open source imaging software ITK-SNAP. The resulting geometry was then transformed with Solidworks into volumes that are compatible with Ansys softwares. Tetrahedral meshes of the wall and fluid domains were built using the ANSYS Meshing software, with a near-wall mesh refinement method in the case of the fluid domain to improve the accuracy of the fluid flow calculations. Ansys Structural was used for the numerical simulation of the vessel deformation and Ansys CFX for the simulation of the blood flow. The fluid structure interaction simulations showed that the systolic and diastolic blood pressures of the common carotid artery could be taken as reference pressures to identify the mechanical properties of the different arteries of the network. The coefficients of the hyperelastic law were identified using Ansys Design model for the common carotid. Under large deformations, a stiffness of 800 kPa is measured, which is of the same order of magnitude as the Young modulus of collagen fibers. Areas of maximum deformations were highlighted near bifurcations. This study is a first step towards patient-specific characterization of the mechanical properties of the facial vessels. The method is currently applied on patients suffering from facial vascular malformations and on patients scheduled for facial reconstruction. Information on the blood flow velocity as well as on the vessel anatomy and deformability will be key to improve surgical planning in the case of such vascular pathologies.

Keywords: identification, mechanical properties, arterial walls, MRI measurements, numerical simulations

Procedia PDF Downloads 292

Authors: Marta Malo, Carlota Soto, Carmen García-Rosales, Teresa Hernández

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SiC based composites are candidates for possible use as structural and functional materials in the future fusion reactors, the main role is intended for the blanket modules. In the blanket, the neutrons produced in the fusion reaction slow down and their energy is transformed into heat in order to finally generate electrical power. In the blanket design named Dual Coolant Lead Lithium (DCLL), a PbLi alloy for power conversion and tritium breeding circulates inside hollow channels called Flow Channel Inserts (FCIs). These FCI must protect the steel structures against the highly corrosive PbLi liquid and the high temperatures, but also provide electrical insulation in order to minimize magnetohydrodynamic interactions of the flowing liquid metal with the high magnetic field present in a magnetically confined fusion environment. Due to their nominally high temperature and radiation stability as well as corrosion resistance, SiC is the main choice for the flow channel inserts. The significantly lower manufacturing cost presents porous SiC (dense coating is required in order to assure protection against corrosion and as a tritium barrier) as a firm alternative to SiC/SiC composites for this purpose. This application requires the materials to be exposed to high radiation levels and extreme temperatures, conditions for which previous studies have shown noticeable changes in both the microstructure and the electrical properties of different types of silicon carbide. Both initial properties and radiation/temperature induced damage strongly depend on the crystal structure, polytype, impurities/additives that are determined by the fabrication process, so the development of a suitable material requires full control of these variables. For this work, several SiC samples with different percentage of porosity and sintering additives have been manufactured by the so-called sacrificial template method at the Ceit-IK4 Technology Center (San Sebastián, Spain), and characterized at Ciemat (Madrid, Spain). Electrical conductivity was measured as a function of temperature before and after irradiation with 1.8 MeV electrons in the Ciemat HVEC Van de Graaff accelerator up to 140 MGy (~ 2·10 -5 dpa). Radiation-induced conductivity (RIC) was also examined during irradiation at 550 ºC for different dose rates (from 0.5 to 5 kGy/s). Although no significant RIC was found in general for any of the samples, electrical conductivity increase with irradiation dose was observed to occur for some compositions with a linear tendency. However, first results indicate enhanced radiation resistance for coated samples. Preliminary thermogravimetric tests of selected samples, together with posterior XRD analysis allowed interpret radiation-induced modification of the electrical conductivity in terms of changes in the SiC crystalline structure. Further analysis is needed in order to confirm this.

Keywords: DCLL blanket, electrical conductivity, flow channel insert, porous SiC, radiation damage, thermal stability

Procedia PDF Downloads 174

Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

Procedia PDF Downloads 96

Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

Procedia PDF Downloads 114

Authors: D. Ficai, A. Ficai, D. Gudovan, I. A. Gudovan, I. Ardelean, R. Trusca, E. Andronescu, V. Mitran, A. Cimpean

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In the last years, scientists struggled hardly to mimic bone structures to develop implants and biostructures which present higher biocompatibility and reduced rejection rate. One way to obtain this goal is to use similar materials as that of bone, namely collagen/hydroxyapatite composite materials. However, it is very important to tailor both compositions but also the microstructure of the bone that would ensure both the optimal osteointegartion and the mechanical properties required by the application. In this study, new collagen/hydroxyapatites composite materials doped with Cu, Li, Mn, Zn were successfully prepared. The synthesis method is described below: weight the Ca(OH)₂ mass, i.e., 7,3067g, and ZnCl₂ (0.134g), CuSO₄ (0.159g), LiCO₃ (0.133g), MnCl₂.4H₂O (0.1971g), and suspend in 100ml distilled water under magnetic stirring. The solution thus obtained is added a solution of NaH₂PO₄*H2O (8.247g dissolved in 50ml distilled water) under slow dropping of 1 ml/min followed by adjusting the pH to 9.5 with HCl and finally filter and wash until neutral pH. The as-obtained slurry was dried in the oven at 80°C and then calcined at 600°C in order to ensure a proper purification of the final product of organic phases, also inducing a proper sterilization of the mixture before insertion into the collagen matrix. The collagen/hydroxyapatite composite materials are tailored from morphological point of view to optimize their biocompatibility and bio-integration against mechanical properties whereas the addition of the dopants is aimed to improve the biological activity of the samples. The addition of transitional metals can improve the biocompatibility and especially the osteoblasts adhesion (Mn²⁺) or to induce slightly better osteoblast differentiation of the osteoblast, Zn²⁺ being a cofactor for many enzymes including those responsible for cell differentiation. If the amount is too high, the final material can become toxic and lose all of its biocompatibility. In order to achieve a good biocompatibility and not reach the cytotoxic effect, the amount of transitional metals added has to be maintained at low levels (0.5% molar). The amount of transitional metals entering into the elemental cell of HA will be verified using inductively-coupled plasma mass spectrometric system. This highly sensitive technique is necessary, because, at such low levels of transitional metals, the difference between biocompatible and cytotoxic is a very thin line, thus requiring proper and thorough investigation using a precise technique. In order to determine the structure and morphology of the obtained composite materials, IR spectroscopy, X-Ray diffraction (XRD), scanning electron microscopy (SEM), and Energy Dispersive X-Ray Spectrometry (EDS) were used. Acknowledgment: The present work was possible due to the EU-funding grant POSCCE-A2O2.2.1-2013-1, Project No. 638/12.03.2014, code SMIS-CSNR 48652. The financial contribution received from the national project “Biomimetic porous structures obtained by 3D printing developed for bone tissue engineering (BIOGRAFTPRINT), No. 127PED/2017 is also highly acknowledged.

Keywords: collagen, composite materials, hydroxyapatite, bone tissue engineering

Procedia PDF Downloads 172

Authors: Lesly Y Carmona-Sarabia, Luisa Barraza-Vergara, Vilmalí López-Mejías, Wandaliz Torres-García, Maribella Domenech-Garcia, Madeline Torres-Lugo

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Biosensors are used in many applications providing real-time monitoring to treat long-term conditions. Thus, understanding the physicochemical properties and biological side effects on the skin of polymers (e. g., poly(methyl methacrylate), PMMA) employed in the fabrication of wearable biosensors is crucial for the selection of manufacturing materials within this field. The PMMA (hydrophobic and thermoplastic polymer) is commonly employed as a coating material or substrate in the fabrication of wearable devices. The cytotoxicityof PMMA (including residual monomers or degradation products) on the skin, in terms of cells and tissue, is required to prevent possible adverse effects (cell death, skin reactions, sensitization) on human health. Within this work, accelerated aging of PMMA (Mw ~ 15000) through thermal and photochemical degradation was under-taken. The accelerated aging of PMMA was carried out by thermal (200°C, 1h) and photochemical degradation (UV-Vis, 8-15d) adapted employing ISO protocols (ISO-10993-12, ISO-4892-1:2016, ISO-877-1:2009, ISO-188: 2011). In addition, in vitro cytotoxicity evaluation of PMMA degradation products was performed using NIH3T3 fibroblast cells to assess the response of skin tissues (in terms of cell viability) exposed with polymers utilized to manufacture wearable biosensors, such as PMMA. The PMMA (Mw ~ 15000) before and after accelerated aging experiments was characterized by thermal gravimetric analysis (TGA), differential scanning calorimetric (DSC), powder X-ray diffractogram (PXRD), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) to determine and verify the successful degradation of this polymer under the specific conditions previously mention. The degradation products were characterized through nuclear magnetic resonance (NMR) to identify possible byproducts generated after the accelerated aging. Results demonstrated a percentage (%) weight loss between 1.5-2.2% (TGA thermographs) for PMMA after accelerated aging. The EDS elemental analysis reveals a 1.32 wt.% loss of carbon for PMMA after thermal degradation. These results might be associated with the amount (%) of PMMA degrade after the accelerated aging experiments. Furthermore, from the thermal degradation products was detected the presence of the monomer and methyl formate (low concentrations) and a low molecular weight radical (·COOCH3) in higher concentrations by NMR. In the photodegradation products, methyl formate was detected in higher concentrations. These results agree with the proposed thermal or photochemical degradation mechanisms found in the literature.1,2 Finally, significant cytotoxicity on the NIH3T3 cells was obtained for the thermal and photochemical degradation products. A decrease in cell viability by > 90% (stock solutions) was observed. It is proposed that the presence of byproducts (e.g. methyl formate or radicals such as ·COOCH₃) from the PMMA degradation might be responsible for the cytotoxicity observed in the NIH3T3 fibroblast cells. Additionally, experiments using skin models will be employed to compare with the NIH3T3 fibroblast cells model.

Keywords: biosensors, polymer, skin irritation, degradation products, cell viability

Procedia PDF Downloads 109

Authors: Scott M. Black, Craig Maclean, Pauline Hall Barrientos, Konstantinos Ritos, Asimina Kazakidi

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Aortic aneurysms and dissections are leading causes of death in cardiovascular disease. Both inevitably lead to hemodynamic instability without surgical intervention in the form of vascular stent-graft deployment. An accurate description of the aortic geometry and blood flow in patient-specific cases is vital for treatment planning and long-term success of such grafts, as they must generate physiological branch perfusion and in-stent hemodynamics. The aim of this study was to create patient-specific computational fluid dynamics (CFD) models through a multi-modality, multi-dimensional approach with boundary condition optimization to predict branch flow rates and in-stent hemodynamics in custom stent-graft configurations. Three-dimensional (3D) thoracoabdominal aortae were reconstructed from four-dimensional flow-magnetic resonance imaging (4D Flow-MRI) and computed tomography (CT) medical images. The former employed a novel approach to generate and enhance vessel lumen contrast via through-plane velocity at discrete, user defined cardiac time steps post-hoc. To produce patient-specific boundary conditions (BCs), the aortic geometry was reduced to a one-dimensional (1D) model. Thereafter, a zero-dimensional (0D) 3-Element Windkessel model (3EWM) was coupled to each terminal branch to represent the distal vasculature. In this coupled 0D-1D model, the 3EWM parameters were optimized to yield branch flow waveforms which are representative of the 4D Flow-MRI-derived in-vivo data. Thereafter, a 0D-3D CFD model was created, utilizing the optimized 3EWM BCs and a 4D Flow-MRI-obtained inlet velocity profile. A sensitivity analysis on the effects of stent-graft configuration and BC parameters was then undertaken using multiple stent-graft configurations and a range of distal vasculature conditions. 4D Flow-MRI granted unparalleled visualization of blood flow throughout the cardiac cycle in both the pre- and postsurgical states. Segmentation and reconstruction of healthy and stented regions from retrospective 4D Flow-MRI images also generated 3D models with geometries which were successfully validated against their CT-derived counterparts. 0D-1D coupling efficiently captured branch flow and pressure waveforms, while 0D-3D models also enabled 3D flow visualization and quantification of clinically relevant hemodynamic parameters for in-stent thrombosis and graft limb occlusion. It was apparent that changes in 3EWM BC parameters had a pronounced effect on perfusion distribution and near-wall hemodynamics. Results show that the 3EWM parameters could be iteratively changed to simulate a range of graft limb diameters and distal vasculature conditions for a given stent-graft to determine the optimal configuration prior to surgery. To conclude, this study outlined a methodology to aid in the prediction post-surgical branch perfusion and in-stent hemodynamics in patient specific cases for the implementation of custom stent-grafts.

Keywords: 4D flow-MRI, computational fluid dynamics, vascular stent-grafts, windkessel

Procedia PDF Downloads 149

Authors: Tamar Trop

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Bike sharing systems can be important elements of smart cities as they have the potential for impact on multiple levels. These systems can add a significant alternative to other modes of mass transit in cities that are continuously looking for measures to become more livable and maintain their attractiveness for citizens, businesses and tourism. Bike-sharing began in Europe in 1965, and a viable format emerged in the mid-2000s thanks to the introduction of information technology. The rate of growth in bike-sharing schemes and fleets has been very rapid since 2008 and has probably outstripped growth in every other form of urban transport. Today, public bike-sharing systems are available on five continents, including over 700 cities, operating more than 800,000 bicycles at approximately 40,000 docking stations. Since modern bike sharing systems have become prevalent only in the last decade, the existing literature analyzing these systems and their sustainability is relatively new. The purpose of the presented study is to assess the sustainability of these newly emerging transportation systems, by using the Envision™ rating system as a methodological framework and the Israeli 'Tel -O-Fun' – bike sharing project as a case study. The assessment was conducted by project team members. Envision™ is a new guidance and rating system used to assess and improve the sustainability of all types and sizes of infrastructure projects. This tool provides a holistic framework for evaluating and rating the community, environmental, and economic benefits of infrastructure projects over the course of their life cycle. This evaluation method has 60 sustainability criteria divided into five categories: Quality of life, leadership, resource allocation, natural world, and climate and risk. 'Tel -O-Fun' project was launched in Tel Aviv-Yafo on 2011 and today provides about 1,800 bikes for rent, at 180 rental stations across the city. The system is based on a complex computer terminal that is located in the docking stations. The highest-rated sustainable features that the project scored include: (a) Improving quality of life by: offering a low cost and efficient form of public transit, improving community mobility and access, enabling the flexibility of travel within a multimodal transportation system, saving commuters time and money, enhancing public health and reducing air and noise pollution; (b) improving resource allocation by: offering inexpensive and flexible last-mile connectivity, reducing space, materials and energy consumption, reducing wear and tear on public roads, and maximizing the utility of existing infrastructure, and (c) reducing of greenhouse gas emissions from transportation. Overall, 'Tel -O-Fun' project was highly scored as an environmentally sustainable and socially equitable infrastructure. The use of this practical framework for evaluation also yielded various interesting insights on the shortcoming of the system and the characteristics of good solutions. This can contribute to the improvement of the project and may assist planners and operators of bike sharing systems to develop a sustainable, efficient and reliable transportation infrastructure within smart cities.

Keywords: bike sharing, Envision™, sustainability rating system, sustainable infrastructure

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Authors: Zhuang Ma, Zihan Zhang, Yu Li

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Environmental concerns from stakeholders (e.g., governments & customers) have pushed firms to integrate environmental management systems into business processes such as R&D, manufacturing, and marketing. Environmental systems include managing environmental risks and pollution control (e.g., air pollution control, waste-water treatment, noise control, energy recycling & solid waste treatment) through raw material management, the elimination and reduction of contaminants, recycling, and reuse in firms' operational processes. Despite increasing studies on firms' proactive adoption of environmental management, their focus is primarily on large corporations operating in developed economies. Investigations in the environmental management efforts of small and medium-sized enterprises (SMEs) are scarce. This is problematic for SMEs because, unlike large corporations, SMEs have limited awareness, resources, capabilities to adapt their operational routines to address environmental impacts. The purpose of this study is to explore how SMEs develop organizational capabilities through interactions with business partners (e.g., environmental management specialists & customers). Drawing on the resource-based view (RBV) and an organizational capabilities perspective, this study investigates the interactively developed capabilities that allow SMEs to adopt environmental management systems. Using an exploratory approach, the study includes 12 semi-structured interviews with senior managers from four SMEs, two environmental management specialists, and two customers in the pharmaceutical sector in Chongqing, China. Findings of this study include four key organizational capabilities: 1) ‘dynamic marketing’ capability, which allows SMEs to recoup the investments in environmental management systems by developing environmentally friendly products to address customers' ever-changing needs; 2) ‘process improvement’ capability, which allows SMEs to select and adopt the latest technologies from biology, chemistry, new material, and new energy sectors into the production system for improved environmental performance and cost-reductions; and 3) ‘relationship management’ capability which allows SMEs to improve corporate image among the public, social media, government agencies, and customers, who in turn help SMEs to overcome their competitive disadvantages. These interactively developed capabilities help SMEs to address larger competitors' foothold in the local market, reduce market constraints, and exploit competitive advantages in other regions (e.g., Guangdong & Jiangsu) of China. These findings extend the RBV and organizational capabilities perspective; that is, SMEs can develop the essential resources and capabilities required for environmental management through interactions with upstream and downstream business partners. While a limited number of studies did highlight the importance of interactions among SMEs, customers, suppliers, NGOs, industrial associations, and consulting firms, they failed to explore the specific capabilities developed through these interactions. Additionally, the findings can explain how a proactive adoption of environmental management systems could help some SMEs to overcome the institutional and market restraints on their products, thereby springboarding into larger, more environmentally demanding, yet more profitable markets compared with their existing market.

Keywords: capabilities, environmental management systems, interactions, SMEs

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Authors: Sonia Boscenco, Zihan Wang, Euclides José de Mendoça Filho, João Paulo Hoppe, Irina Pokhvisneva, Geoffrey B.C. Hall, Michael J. Meaney, Patricia Pelufo Silveira

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Entropy can be described as a measure of the number of states of a system, and when used in the context of physiological time-based signals, it serves as a measure of complexity. In functional connectivity data, entropy can account for the moment-to-moment variability that is neglected in traditional functional magnetic resonance imaging (fMRI) analyses. While brain fMRI resting state entropy has been associated with some pathological conditions like schizophrenia, no investigations have explored the association between brain entropy measures and individual differences in child behavior in healthy children. We describe a novel exploratory approach to evaluate brain fMRI resting state data in two child cohorts, and MAVAN (N=54, 4.5 years, 48% males) and GUSTO (N = 206, 4.5 years, 48% males) and its associations to child behavior, that can be used in future research in the context of child exposures and long-term health. Following rs-fMRI data pre-processing and Shannon entropy calculation across 32 network regions of interest to acquire 496 unique functional connections, partial correlation coefficient analysis adjusted for sex was performed to identify associations between entropy data and Strengths and Difficulties questionnaire in MAVAN and Child Behavior Checklist domains in GUSTO. Significance was set at p < 0.01, and we found eight significant associations in GUSTO. Negative associations were found between two frontoparietal regions and cerebellar posterior and oppositional defiant problems, (r = -0.212, p = 0.006) and (r = -0.200, p = 0.009). Positive associations were identified between somatic complaints and four default mode connections: salience insula (r = 0.202, p < 0.01), dorsal attention intraparietal sulcus (r = 0.231, p = 0.003), language inferior frontal gyrus (r = 0.207, p = 0.008) and language posterior superior temporal gyrus (r = 0.210, p = 0.008). Positive associations were also found between insula and frontoparietal connection and attention deficit / hyperactivity problems (r = 0.200, p < 0.01), and insula – default mode connection and pervasive developmental problems (r = 0.210, p = 0.007). In MAVAN, ten significant associations were identified. Two positive associations were found = with prosocial scores: the salience prefrontal cortex and dorsal attention connection (r = 0.474, p = 0.005) and the salience supramarginal gyrus and dorsal attention intraparietal sulcus (r = 0.447, p = 0.008). The insula and prefrontal connection were negatively associated with peer problems (r = -0.437, p < 0.01). Conduct problems were negatively associated with six separate connections, the left salience insula and right salience insula (r = -0.449, p = 0.008), left salience insula and right salience supramarginal gyrus (r = -0.512, p = 0.002), the default mode and visual network (r = -0.444, p = 0.009), dorsal attention and language network (r = -0.490, p = 0.003), and default mode and posterior parietal cortex (r = -0.546, p = 0.001). Entropy measures of resting state functional connectivity can be used to identify individual differences in brain function that are correlated with variation in behavioral problems in healthy children. Further studies applying this marker into the context of environmental exposures are warranted.

Keywords: child behaviour, functional connectivity, imaging, Shannon entropy

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Authors: Desmond Appiah, Fan Zhang, Shouqi Yuan, Wei Xueyuan, Stephen N. Asomani

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The side channel pump has distinctive hydraulic performance characteristics over other vane pumps because of its generation of high pressure heads in only one impeller revolution. Hence, there is soaring utilization and application in the fields of petrochemical, food processing fields, automotive and aerospace fuel pumping where high heads are required at low flows. The side channel pump is characterized by unstable flow because after fluid flows into the impeller passage, it moves into the side channel and comes back to the impeller again and then moves to the next circulation. Consequently, the flow leaves the side channel pump following a helical path. However, the pressure fluctuation exhibited in the flow greatly contributes to the unwanted noise and vibration which is associated with the flow. In this paper, a side channel pump prototype was examined thoroughly through numerical calculations based on SST k-ω turbulence model to ascertain the pressure fluctuation behavior. The pressure fluctuation intensity of the 3D unstable flow dynamics were carefully investigated under different working conditions 0.8QBEP, 1.0 QBEP and 1.2QBEP. The results showed that the pressure fluctuation distribution around the pressure side of the blade is greater than the suction side at the impeller and side channel interface (z=0) for all three operating conditions. Part-load condition 0.8QBEP recorded the highest pressure fluctuation distribution because of the high circulation velocity thus causing an intense exchanged flow between the impeller and side channel. Time and frequency domains spectra of the pressure fluctuation patterns in the impeller and the side channel were also analyzed under the best efficiency point value, QBEP using the solution from the numerical calculations. It was observed from the time-domain analysis that the pressure fluctuation characteristics in the impeller flow passage increased steadily until the flow reached the interrupter which separates low-pressure at the inflow from high pressure at the outflow. The pressure fluctuation amplitudes in the frequency domain spectrum at the different monitoring points depicted a gentle decreasing trend of the pressure amplitudes which was common among the operating conditions. The frequency domain also revealed that the main excitation frequencies occurred at 600Hz, 1200Hz, and 1800Hz and continued in the integers of the rotating shaft frequency. Also, the mass flow exchange plots indicated that the side channel pump is characterized with many vortex flows. Operating conditions 0.8QBEP, 1.0 QBEP depicted less and similar vortex flow while 1.2Q recorded many vortex flows around the inflow, middle and outflow regions. The results of the numerical calculations were finally verified experimentally. The performance characteristics curves from the simulated results showed that 0.8QBEP working condition recorded a head increase of 43.03% and efficiency decrease of 6.73% compared to 1.0QBEP. It can be concluded that for industrial applications where the high heads are mostly required, the side channel pump can be designed to operate at part-load conditions. This paper can serve as a source of information in order to optimize a reliable performance and widen the applications of the side channel pumps.

Keywords: exchanged flow, pressure fluctuation, numerical simulation, side channel pump

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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