Search results for: microscopic object detection and tracking

Authors: Pedro Mario Gonzalez Jimenez

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The novelties introduced by the last Notice on agreements of minor importance tighten the application of the ‘De minimis’ safe harbour in the European Union. However, the undetermined legal concept of effect on trade between the Member States becomes importance at the same time. Therefore, the current analysis that the jurist should carry out in the European Union to determine if an agreement appreciably restrict competition under Article 101 of the Treaty on the Functioning of the European Union is double. Hence, it is necessary to know how to balance the significance in competition and the significance in effect on trade between the Member States. It is a crucial issue due to the negative delimitation of restriction of competition affects the positive one. The methodology of this research is rather simple. Beginning with a historical approach to the ‘De Minimis Rule’, their main problems and uncertainties will be found. So, after the analysis of normative documents and the jurisprudence of the Court of Justice of the European Union some proposals of ‘Lege ferenda’ will be offered. These proposals try to overcome the contradictions and questions that currently exist in the European Union as a consequence of the current legal regime of agreements of minor importance. The main findings of this research are the followings: Firstly, the effect on trade is another way to analyze the importance of an agreement different from the ‘De minimis rule’. In point of fact, this concept is singularly adapted to go through agreements that have as object the prevention, restriction or distortion of competition, as it is observed in the most famous European Union case-law. Thanks to the effect on trade, as long as the proper requirements are met there is no a restriction of competition under article 101 of the Treaty on the Functioning of the European Union, even if the agreement had an anti-competitive object. These requirements are an aggregate market share lower than 5% on any of the relevant markets affected by the agreement and turnover lower than 40 million of Euros. Secondly, as the Notice itself says ‘it is also intended to give guidance to the courts and competition authorities of the Member States in their application of Article 101 of the Treaty, but it has no binding force for them’. This reality makes possible the existence of different statements among the different Member States and a confusing perception of what a restriction of competition is. Ultimately, damage on trade between the Member States could be observed for this reason. The main conclusion is that the significant effect on trade between Member States is irrelevant in agreements that restrict competition because of their effects but crucial in agreements that restrict competition because of their object. Thus, the Member States should propose the incorporation of a similar concept in their legal orders in order to apply the content of the Notice. Otherwise, the significance of the restrictive agreement on competition would not be properly assessed.

Keywords: De minimis rule, effect on trade, minor importance agreements, safe harbour

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

Procedia PDF Downloads 250

Authors: Fahad Alamoudi, Yaser Miaji, Fawzy Jalalah

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The growing popularity of swimming pools and other activities in the water for sport, fitness, therapy or just enjoyable relaxation have led to the increased use of swimming pools and the establishment of a variety of specific-use pools such as spa pools, Waterslides and more recently, hydrotherapy and wave pools. In this research a few simple equipments are used for test, Detect and alert for detection of water cleanness and pollution. YSI Photometer Systems, TDSTestr High model, rio 12HF, and Electrode A1. The researchers used electrolysis as a method of separating bonded elements and compounds by passing an electric current through them. The results which use 41 experiments show the higher the salt concentration, the more efficient the electrode and the smaller the gap between the plates and The lower the electrode voltage. Furthermore, it is proved that the larger the surface area, the lower the cell voltage and the higher current used the more chlorine produced.

Keywords: photometer, electrode, electrolysis, swimming pool chlorination

Procedia PDF Downloads 344

Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Ragu Sasikumar, Palraj Ranganathan, Shen-Ming Chen, Syang-Peng Rwei

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A highly sensitive, and selective α-Lipoic acid (ALA) sensor based on a functionalized multi-walled carbon nanotubes-polyindole/Ti2O3 (f-MWCNTs-PIN/Ti2O3) nanocomposite modified glassy carbon electrode (GCE) was developed. The fabricated f-MWCNTs-PIN/Ti2O3/GCE displayed an enhanced voltammetric response for oxidation towards ALA relative to that of a f-MWCNTs/GCE, f-MWCNTs-PIN/GCE, Ti2O3/GCE, and a bare GCE. Under optimum conditions, the f-MWCNTs-PIN/Ti2O3/GCE showed a wide linear range at ALA concentrations of 0.39-115.8 µM. The limit of detection of 12 nM and sensitivity of about 6.39 µA µM-1cm-2. The developed sensor showed anti-interference, reproducibility, good repeatability, and operational stability. Applied possibility of the sensor has been confirmed in vegetable samples.

Keywords: f-MWCNT, polyindole, Ti2O3, Alzheimer’s diseases, ALA sensor

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Monisha Elumalai, Joana Guerreiro, Joana Carvalho, Marta Prado

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DNA biosensors popularity has been increasing over the past few years. Traditional analytical techniques tend to require complex steps and expensive equipment however DNA biosensors have the advantage of getting simple, fast and economic. Additionally, the combination of DNA biosensors with nanomaterials offers the opportunity to improve the selectivity, sensitivity and the overall performance of the devices. DNA biosensors are based on oligonucleotides as sensing elements. These oligonucleotides are highly specific to complementary DNA sequences resulting in the hybridization of the strands. DNA biosensors are not only an advantage in the clinical field but also applicable in numerous research areas such as food analysis or environmental control. Zebra Mussels (ZM), Dreissena polymorpha are invasive species responsible for enormous negative impacts on the environment and ecosystems. Generally, the detection of ZM is made when the observation of adult or macroscopic larvae's is made however at this stage is too late to avoid the harmful effects. Therefore, there is a need to develop an analytical tool for the early detection of ZM. Here, we present a portable plasmonic biosensor for the detection of environmental DNA (eDNA) released to the environment from this invasive species. The plasmonic DNA biosensor combines gold nanoparticles, as transducer elements, due to their great optical properties and high sensitivity. The detection strategy is based on the immobilization of a short base pair DNA sequence on the nanoparticles surface followed by specific hybridization in the presence of a complementary target DNA. The hybridization events are tracked by the optical response provided by the nanospheres and their surrounding environment. The identification of the DNA sequences (synthetic target and probes) to detect Zebra mussel were designed by using Geneious software in order to maximize the specificity. Moreover, to increase the optical response enzyme amplification of DNA might be used. The gold nanospheres were synthesized and characterized by UV-visible spectrophotometry and transmission electron microscopy (TEM). The obtained nanospheres present the maximum localized surface plasmon resonance (LSPR) peak position are found to be around 519 nm and a diameter of 17nm. The DNA probes modified with a sulfur group at one end of the sequence were then loaded on the gold nanospheres at different ionic strengths and DNA probe concentrations. The optimal DNA probe loading will be selected based on the stability of the optical signal followed by the hybridization study. Hybridization process leads to either nanoparticle dispersion or aggregation based on the presence or absence of the target DNA. Finally, this detection system will be integrated into an optical sensing platform. Considering that the developed device will be used in the field, it should fulfill the inexpensive and portability requirements. The sensing devices based on specific DNA detection holds great potential and can be exploited for sensing applications in-loco.

Keywords: ZM DNA, DNA probes, nicking enzyme, gold nanoparticles

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Authors: Andreea Campu, Ilinica Muresan, Simona Cainap, Simion Astilean, Monica Focsan

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Acute myocardial infarction (AMI) is the most severe cardiovascular disease, which has threatened human lives for decades, thus a continuous interest is directed towards the detection of cardiac biomarkers such as cardiac troponin I (cTnI) in order to predict risk and, implicitly, fulfill the early diagnosis requirements in AMI settings. Microfluidics is a major technology involved in the development of efficient sensing devices with real-time fast responses and on-site applicability. Microfluidic devices have gathered a lot of attention recently due to their advantageous features such as high sensitivity and specificity, miniaturization and portability, ease-of-use, low-cost, facile fabrication, and reduced sample manipulation. The integration of gold nanoparticles into the structure of microfluidic sensors has led to the development of highly effective detection systems, considering the unique properties of the metallic nanostructures, specifically the Localized Surface Plasmon Resonance (LSPR), which makes them highly sensitive to their microenvironment. In this scientific context, herein, we propose the implementation of a novel detection device, which successfully combines the efficiency of gold bipyramids (AuBPs) as signal transducers and thermal generators with the sample-driven advantages of the microfluidic channels into a miniaturized, portable, low-cost, specific, and sensitive test for the dual LSPR-thermographic cTnI detection. Specifically, AuBPs with longitudinal LSPR response at 830 nm were chemically synthesized using the seed-mediated growth approach and characterized in terms of optical and morphological properties. Further, the colloidal AuBPs were deposited onto pre-treated silanized glass substrates thus, a uniform nanoparticle coverage of the substrate was obtained and confirmed by extinction measurements showing a 43 nm blue-shift of the LSPR response as a consequence of the refractive index change. The as-obtained plasmonic substrate was then integrated into a microfluidic “Y”-shaped polydimethylsiloxane (PDMS) channel, fabricated using a Laser Cutter system. Both plasmonic and microfluidic elements were plasma treated in order to achieve a permanent bond. The as-developed microfluidic plasmonic chip was further coupled to an automated syringe pump system. The proposed biosensing protocol implicates the successive injection inside the microfluidic channel as follows: p-aminothiophenol and glutaraldehyde, to achieve a covalent bond between the metallic surface and cTnI antibody, anti-cTnI, as a recognition element, and target cTnI biomarker. The successful functionalization and capture of cTnI was monitored by LSPR detection thus, after each step, a red-shift of the optical response was recorded. Furthermore, as an innovative detection technique, thermal determinations were made after each injection by exposing the microfluidic plasmonic chip to 785 nm laser excitation, considering that the AuBPs exhibit high light-to-heat conversion performances. By the analysis of the thermographic images, thermal curves were obtained, showing a decrease in the thermal efficiency after the anti-cTnI-cTnI reaction was realized. Thus, we developed a microfluidic plasmonic chip able to operate as both LSPR and thermal sensor for the detection of the cardiac troponin I biomarker, leading thus to the progress of diagnostic devices.

Keywords: gold nanobipyramids, microfluidic device, localized surface plasmon resonance detection, thermographic detection

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Authors: H. Fuad Usman, M. Rafay Khan Sial, Shahzaib Hamid

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The trend of renewable energy resources has been amplified due to global warming and other environmental related complications in the 21st century. Recent research has very much emphasized on the generation of electrical power through renewable resources like solar, wind, hydro, geothermal, etc. The use of the photovoltaic cell has become very public as it is very useful for the domestic and commercial purpose overall the world. Although a single cell gives the low voltage output but connecting a number of cells in a series formed a complete module of the photovoltaic cells, it is becoming a financial investment as the use of it fetching popular. This also reduced the prices of the photovoltaic cell which gives the customers a confident of using this source for their electrical use. Photovoltaic cell gives the MPPT at single specific point of operation at a given temperature and level of solar intensity received at a given surface whereas the focal point changes over a large range depending upon the manufacturing factor, temperature conditions, intensity for insolation, instantaneous conditions for shading and aging factor for the photovoltaic cells. Two improved algorithms have been proposed in this article for the MPPT. The widely used algorithms are the ‘Incremental Conductance’ and ‘Perturb and Observe’ algorithms. To extract the maximum power from the source to the load, the duty cycle of the convertor will be effectively controlled. After assessing the previous techniques, this paper presents the improved and reformed idea of harvesting maximum power point from the photovoltaic cells. A thoroughly go through of the previous ideas has been observed before constructing the improvement in the traditional technique of MPP. Each technique has its own importance and boundaries at various weather conditions. An improved technique of implementing the use of both ‘Perturb and Observe’ and ‘Incremental Conductance’ is introduced.

Keywords: duty cycle, MPPT (Maximum Power Point Tracking), perturb and observe (P&O), photovoltaic module

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Authors: Seyed Mehdi Seyed Alizadeh, Christoph Arns, Shane Latham

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Geomechanical characterization of rocks in detail and its possible implications on flow properties is an important aspect of reservoir characterization workflow. In order to gain more understanding of the microstructure evolution of reservoir rocks under stress a series of axisymmetric triaxial tests were performed on two different analogue rock samples. In-situ compression tests were coupled with high resolution micro-Computed Tomography to elucidate the changes in the pore/grain network of the rocks under pressurized conditions. Two outcrop sandstones were chosen in the current study representing a various cementation status of well-consolidated and weakly-consolidated granular system respectively. High resolution images were acquired while the rocks deformed in a purpose-built compression cell. A detailed analysis of the 3D images in each series of step-wise compression tests (up to the failure point) was conducted which includes the registration of the deformed specimen images with the reference pristine dry rock image. Digital Image Correlation (DIC) technique based on the intensity of the registered 3D subsets and particle tracking are utilized to map the displacement fields in each sample. The results suggest the complex architecture of the localized shear zone in well-cemented Bentheimer sandstone whereas for the weakly-consolidated Castlegate sandstone no discernible shear band could be observed even after macroscopic failure. Post-mortem imaging a sister plug from the friable rock upon undergoing continuous compression reveals signs of a shear band pattern. This suggests that for friable sandstones at small scales loading mode may affect the pattern of deformation. Prior to mechanical failure, the continuum digital image correlation approach can reasonably capture the kinematics of deformation. As failure occurs, however, discrete image correlation (i.e. particle tracking) reveals superiority in both tracking the grains as well as quantifying their kinematics (in terms of translations/rotations) with respect to any stage of compaction. An attempt was made to quantify the displacement field in compression using continuum Digital Image Correlation which is based on the reference and secondary image intensity correlation. Such approach has only been previously applied to unconsolidated granular systems under pressure. We are applying this technique to sandstones with various degrees of consolidation. Such element of novelty will set the results of this study apart from previous attempts to characterize the deformation pattern in consolidated sands.

Keywords: deformation mechanism, displacement field, shear behavior, triaxial compression, X-ray micro-CT

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Authors: Hiroshi Nakayama, Yuji Ito

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Synthetic cannabinoids have attracted much public attention recently in Japan. 1-pentyl-3-(1-naphthoyl)-indole (JWH-018), 1-pentyl-2-methyl-3-(1-naphthoyl) indole (JWH-015), 1-(5-fluoropentyl)-3- (1-(2,2,3,3- tetramethylcyclopropyl)) indole (XLR-11) and 1-methyl-3- (1-admantyl) indole (JWH-018 adamantyl analog) are known as synthetic cannabinoids and are also considered dangerous illegal drugs in Japan. It has become necessary to develop sensitive and useful methods for detection of synthetic cannabinoids. We produced two monoclonal antibodies (MAb) against synthetic cannabinoids, named NT1 (IgG1) and NT2 (IgG1), using Hybridoma technology. The cross-reactivity of these produced MAbs was evaluated using a competitive enzyme-linked immunosorbent assay (ELISA). In the results, we found both of these antibodies recognize many kinds of synthetic cannabinoids analog. However, neither of these antibodies recognizes naphtoic acid, 1-methyl-indole and indole known as a raw material of synthetic cannabinoid. Thus, the MAbs produced in this study could be a useful tool for the detection of synthetic cannabinoids.

Keywords: ELISA, monoclonal antibody, sensor, synthetic cannabinoid

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Authors: Sathwik Sarvadevabhatla Kasyap, Kostas Senetakis

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The mechanical behavior of geological materials is very complex, and this complexity is related to the discrete nature of soils and rocks. Characteristics of a material at the grain scale such as particle size and shape, surface roughness and morphology, and particle contact interface are critical to evaluate and better understand the behavior of discrete materials. This study investigates experimentally the micro-mechanical behavior of quartz sand grains with emphasis on the influence of the presence of microparticles in their contact region. The outputs of the study provide some fundamental insights on the contact mechanics behavior of artificially coated grains and can provide useful input parameters in the discrete element modeling (DEM) of soils. In nature, the contact interfaces between real soil grains are commonly observed with microparticles. This is usually the case of sand-silt and sand-clay mixtures, where the finer particles may create a coating on the surface of the coarser grains, altering in this way the micro-, and thus the macro-scale response of geological materials. In this study, the micro-mechanical behavior of Leighton Buzzard Sand (LBS) quartz grains, with interference of different microparticles at their contact interfaces is studied in the laboratory using an advanced custom-built inter-particle loading apparatus. Special techniques were adopted to develop the coating on the surfaces of the quartz sand grains so that to establish repeatability of the coating technique. The characterization of the microstructure of coated particles on their surfaces was based on element composition analyses, microscopic images, surface roughness measurements, and single particle crushing strength tests. The mechanical responses such as normal and tangential load – displacement behavior, tangential stiffness behavior, and normal contact behavior under cyclic loading were studied. The behavior of coated LBS particles is compared among different classes of them and with pure LBS (i.e. surface cleaned to remove any microparticles). The damage on the surface of the particles was analyzed using microscopic images. Extended displacements in both normal and tangential directions were observed for coated LBS particles due to the plastic nature of the coating material and this varied with the variation of the amount of coating. The tangential displacement required to reach steady state was delayed due to the presence of microparticles in the contact region of grains under shearing. Increased tangential loads and coefficient of friction were observed for the coated grains in comparison to the uncoated quartz grains.

Keywords: contact interface, microparticles, micro-mechanical behavior, quartz sand

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Authors: Xinglu Nie, Feifei Tang, Chuntao Wei, Zhimin Ruan, Qianhong Zhu

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Current method of manual-style inspection can not fully meet the requirement of the urban rail transit security in China. In this paper, an intelligent inspection method using unmanned aerial vehicle (UAV) is utilized. A series of orthophoto of rail transit monitored area was collected by UAV, image correction and registration were operated among multi-phase images, then the change detection was used to detect the changes, judging the engineering activities and human activities that may become potential threats to the security of urban rail. Not only qualitative judgment, but also quantitative judgment of changes in the security control area can be provided by this method, which improves the objectives and efficiency of the patrol results. The No.6 line of Chongqing Municipality was taken as an example to verify the validation of this method.

Keywords: rail transit, control of protected areas, intelligent inspection, UAV, change detection

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Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Parvin Samadi Pakchin, Reza Saber, Hossein Ghanbari, Yadollah Omidi

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Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.

Keywords: signal-off electrochemical biosensor, CA125, ovarian cancer, chitosan-gold nanoparticles

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Authors: Ni̇sa Yeni̇kalayci, Türkan Aybi̇ke Akarca

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The ability of correlation the number and space relations, one of the basic scientific process skills, is being used in the transformation of a two-dimensional object into a three-dimensional image or in the expression of symmetry axes of the object. With this research, it is aimed to determine the ability of science students to establish number and space relations. The research was carried out with a total of 90 students studying in the first semester of the Science Education program of a state university located in the Turkey’s Black Sea Region in the fall semester of 2017-2018 academic year. An ‘Integrative Perception Test (IPT)’ was designed by the researchers to collect the data. Within the scope of IPT, the courses and workbooks specific to the field of science were scanned and the ones without symmetrical structure from the visual items belonging to the ‘Physics - Chemistry – Biology’ sub-fields were selected and listed. During the application, it was expected that students would imagine and draw images of the missing half of the visual items that were given incomplete in the first place. The data obtained from the test in which there are 30 images or pictures in total (f Physics = 10, f Chemistry = 10, f Biology = 10) were analyzed descriptively based on the drawings created by the students as ‘complete (2 points), incomplete/wrong (1 point), empty (0 point)’. For the teaching of new concepts in small aged groups, images or pictures showing symmetrical structures and similar applications can also be used.

Keywords: integrative perception, number and space relations, science education, scientific process skills

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

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This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

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Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

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Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

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Authors: Samuele Ghignoli, Valentina de Lorenzi, Gianmarco Ferri, Stefano Luin, Francesco Cardarelli

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Insulin and glucagon are the two essential hormones for maintaining proper blood glucose homeostasis, which is disrupted in Diabetes. A constantly growing research interest has been focused on the study of the subcellular structures involved in hormone secretion, namely insulin- and glucagon-containing granules, and on the mechanisms regulating their behaviour. Yet, while several successful attempts were reported describing the dynamic properties of insulin granules, little is known about their counterparts in α cells, the glucagon-containing granules. To fill this gap, we used αTC1 clone 9 cells as a model of α cells and ZIGIR as a fluorescent Zinc chelator for granule labelling. We started by using spatiotemporal fluorescence correlation spectroscopy in the form of imaging-derived mean square displacement (iMSD) analysis. This afforded quantitative information on the average dynamical and structural properties of glucagon granules having insulin granules as a benchmark. Interestingly, the iMSD sensitivity to average granule size allowed us to confirm that glucagon granules are smaller than insulin ones (~1.4 folds, further validated by STORM imaging). To investigate possible heterogeneities in granule dynamic properties, we moved from correlation spectroscopy to single particle tracking (SPT). We developed a MATLAB script to localize and track single granules with high spatial resolution. This enabled us to classify the glucagon granules, based on their dynamic properties, as ‘blocked’ (i.e., trajectories corresponding to immobile granules), ‘confined/diffusive’ (i.e., trajectories corresponding to slowly moving granules in a defined region of the cell), or ‘drifted’ (i.e., trajectories corresponding to fast-moving granules). In cell-culturing control conditions, results show this average distribution: 32.9 ± 9.3% blocked, 59.6 ± 9.3% conf/diff, and 7.4 ± 3.2% drifted. This benchmarking provided us with a foundation for investigating selected experimental conditions of interest, such as the glucagon-granule relationship with the cytoskeleton. For instance, if Nocodazole (10 μM) is used for microtubule depolymerization, the percentage of drifted motion collapses to 3.5 ± 1.7% while immobile granules increase to 56.0 ± 10.7% (remaining 40.4 ± 10.2% of conf/diff). This result confirms the clear link between glucagon-granule motion and cytoskeleton structures, a first step towards understanding the intracellular behaviour of this subcellular compartment. The information collected might now serve to support future investigations on glucagon granules in physiology and disease. Acknowledgment: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 866127, project CAPTUR3D).

Keywords: glucagon granules, single particle tracking, correlation spectroscopy, ZIGIR

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Mehwish Jamil Noor

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Morphological features of pollen (sculpturing) were useful for identification of different floral taxa. In this study 49 pollen grains, types belonging to 25 families were studied using Scanning Electron Microscope. Shape and sculpturing of pollen ranging from Psilate, scabrate to reticulate, bireticulate and echinolophate. Honey pollen was identified using morphological features, number and arrangement of pore and colpi, size and shape. It presents the first attempt from Pakistan involving extraction of pollen from honey, its identification and taxonomic analysis. Among pollen studied diversity in shape and sculpturing has been observed ranging from Psilate, scabrate to reticulate to bireticulate and echinolophate condition. Pollen has been identified with the help of morphological feature, number and arrangement of pore and colpi, size and shape, reference slides, light microscopic data and previous literature have been consulted for pollen identification. Pollen of closely related species resemble each other therefore pollen identification of airborne and honey pollen is not possible till species level. Survey of flora was carried in parallel to keep the record about the allergenic and melliferous preference of specific sites through surveys and interviews. Their pollination season and geographical distribution were recorded. Two hundred and five including wild and cultivated taxa were identified belonging to sixty-seven families. Major bee attracting wild shrub and trees includes Justicia adhatoda, Acacia nilotica, Ziziphus jujuba, Taraxicum officinalis, Artemisia dubia, Casuarina sp., Ulmus sp., Broussonetia papyrifera, Cupressus sp. or Pinus roxburghii etc. Cultivated crops like Pennisetum typhoides, Nigella sativa, Triticum sativum along with fruit trees of Pyrus, Prunus, Eryobotria, Citrus etc. are popular melliferous floras. Exotic/ introduced species like Eucalyptus or Parthenium hysterophorus, are also frequently visited by bees indicating the significance of those plants in the honey industry. It is concluded that different microscopic analysis techniques give more clear and authentic pictures of and melliferous pollen identification which is well supported by the floral calendar. The diversity of pollen are observed in case of melliferous pollen, and most of the windborne pollen were found less sculptured or psilate expressing the adaptation to the specific mode of pollination. Pollen morphology and sculpturing would serve as a reference for future studies.

Keywords: pollen, allergenic flora, sem, pollen key, Scanning Electron Microscopy (SEM)

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Authors: Y. Kourd, D. Lefebvre

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The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults.

Keywords: decision tree, residuals analysis, ANFIS, fault diagnosis

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Authors: Jia-Chao Wang

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A model of the universe without invoking space expansion is proposed to explain the observed redshift-distance relation and the cosmic microwave background radiation (CMB). The main hypothesized feature of the model is that photons traveling in space interact with the CMB photon gas. This interaction causes the photons to gradually lose energy through dissipation and, therefore, experience redshift. The interaction also causes some of the photons to be scattered off their track toward an observer and, therefore, results in beam intensity attenuation. As observed, the CMB exists everywhere in space and its photon density is relatively high (about 410 per cm³). The small average energy of the CMB photons (about 6.3×10⁻⁴ eV) can reduce the energies of traveling photons gradually and will not alter their momenta drastically as in, for example, Compton scattering, to totally blur the images of distant objects. An object moving through a thermalized photon gas, such as the CMB, experiences a drag. The cause is that the object sees a blue shifted photon gas along the direction of motion and a redshifted one in the opposite direction. An example of this effect can be the observed CMB dipole: The earth travels at about 368 km/s (600 km/s) relative to the CMB. In the all-sky map from the COBE satellite, radiation in the Earth's direction of motion appears 0.35 mK hotter than the average temperature, 2.725 K, while radiation on the opposite side of the sky is 0.35 mK colder. The pressure of a thermalized photon gas is given by Pγ = Eγ/3 = αT⁴/3, where Eγ is the energy density of the photon gas and α is the Stefan-Boltzmann constant. The observed CMB dipole, therefore, implies a pressure difference between the two sides of the earth and results in a CMB drag on the earth. By plugging in suitable estimates of quantities involved, such as the cross section of the earth and the temperatures on the two sides, this drag can be estimated to be tiny. But for a photon traveling at the speed of light, 300,000 km/s, the drag can be significant. In the present model, for the dissipation part, it is assumed that a photon traveling from a distant object toward an observer has an effective interaction cross section pushing against the pressure of the CMB photon gas. For the attenuation part, the coefficient of the typical attenuation equation is used as a parameter. The values of these two parameters are determined by fitting the 748 µ vs. z data points compiled from 643 supernova and 105 γ-ray burst observations with z values up to 8.1. The fit is as good as that obtained from the lambda cold dark matter (ΛCDM) model using online cosmological calculators and Planck 2015 results. The model can be used to interpret Hubble's constant, Olbers' paradox, the origin and blackbody nature of the CMB radiation, the broadening of supernova light curves, and the size of the observable universe.

Keywords: CMB as the lowest energy state, model of the universe, origin of CMB in a static universe, photon-CMB photon gas interaction

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Authors: Tien S. H. Pham, Peter J. Mahon, Aimin Yu

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The detection of drug molecules by voltammetry has attracted great interest over the past years. However, many drug molecules exhibit poor electrochemical signals at common electrodes which result in low sensitivity in detection. An efficient way to overcome this problem is to modify electrodes with functional materials. Since discovered in 2004, graphene (or reduced graphene oxide) has emerged as one of the most studied two-dimensional carbon materials in condensed matter physics, electrochemistry, and so on due to its exceptional physicochemical properties. Additionally, the continuous development of technology has opened the new window for the successful fabrications of many novel graphene-based nanomaterials to serve in electrochemical analysis. This research aims to synthesize and characterize gold nanoparticle coated beta-cyclodextrin functionalized reduced graphene oxide (Au NP–β-CD–RGO) nanocomposites with highly conductive and strongly electro-catalytic properties as well as excellent supramolecular recognition abilities for the modification of electrodes. The electrochemical responses of ciprofloxacin at the as-prepared nanocomposite modified electrode was effectively amplified was much higher in comparison with that at the bare electrode. The linear concentration range was from 0.01 to 120 µM, with a detection limit of 2.7 nM using differential pulse voltammetry. Thus, Au NP–β-CD–RGO nanocomposite has great potential as an ideal material to construct sensitive sensors for the electrochemical determination of ciprofloxacin or similar antibacterial drugs in the future based on its excellent stability, selectivity, and reproducibility.

Keywords: Au nanoparticles, β-CD, ciprofloxacin, electrochemical determination, graphene based nanomaterials

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Authors: Antonella Cartoni, Mattea Carmen Castrovilli

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A biosensor for lactate detection has been developed using an environmentally friendly approach. The biosensor is based on lactate oxidase (LOX) and has remarkable capabilities for reuse and storage at room temperature. The manufacturing technique employed is ambient electrospray deposition (ESD), which enables efficient and sustainable immobilization of the LOX enzyme on a cost-effective com-mercial screen-printed Prussian blue/carbon electrode (PB/C-SPE). The study demonstrates that the ESD technology allows the biosensor to be stored at ambient pressure and temperature for extended periods without affecting the enzymatic activity. The biosensor can be stored for up to 90 days without requiring specific storage conditions, and it can be reused for up to 24 measurements on both freshly prepared electrodes and electrodes that are three months old. The LOX-based biosensor exhibits a lin-ear range of lactate detection between 0.1 and 1 mM, with a limit of detection of 0.07±0.02 mM. Ad-ditionally, it does not exhibit any memory effects. The immobilization process does not involve the use of entrapment matrices or hazardous chemicals, making it environmentally sustainable and non-toxic compared to current methods. Furthermore, the application of a electrospray deposition cycle on previously used biosensors rejuvenates their performance, making them comparable to freshly made biosensors. This highlights the excellent recycling potential of the technique, eliminating the waste as-sociated with disposable devices.

Keywords: green friendly, reuse, storage performance, immobilization, matrix-free, electrospray deposition, biosensor, lactate oxidase, enzyme

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Ahmed Noor Al-qayyim, Barlas Özden Çağlayan

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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures.

Keywords: damage detection, optimization, signals processing, structural health monitoring, two points–condensation

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

Procedia PDF Downloads 397