Search results for: urea deep placement

Authors: Abdelkader Hadjoudja, Hajar Bouazza

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The PLD/CPLD devices are widely used for logic synthesis since several decades. Based on sum of product terms (PTs) architecture, the PLD/CPLD offer a high degree of flexibility to support various application requirements. They are suitable for large combinational logic, finite state machines as well as intensive I/O designs. CPLDs offer very predictable timing characteristics and are therefore ideal for critical control applications. This paper describes how the logic synthesis techniques, such as 1) XOR detection, 2) logic doubling, 3) complement of a Boolean function are combined, applied and used to optimize the CPLDs devices architecture that is based on PAL-like macrocells. Our goal is to use these techniques for minimizing the number of macrocells required to implement a circuit and minimize the delay of mapped circuit.

Keywords: CPLD, doubling, optimization, XOR

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Authors: A. Beatti, L. Chipchase, A. Rayner, T. Souvlis

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The aims of this study were to investigate the depth of interferential current (IFC) penetration through soft tissue and to investigate the area over which IFC spreads during clinical application. Premodulated IFC and ‘true’ IFC at beat frequencies of 4, 40 and 90Hz were applied via four electrodes to the distal medial thigh of 15 healthy subjects. The current was measured via three Teflon coated fine needle electrodes that were inserted into the superficial layer of skin, then into the subcutaneous tissue (≈1 cm deep) and then into muscle tissue (≈2 cm deep). The needle electrodes were placed in the middle of the four IFC electrodes, between two channels and outside the four electrodes. Readings were taken at each tissue depth from each electrode during each treatment frequency then digitized and stored for analysis. All voltages were greater at all depths and locations than baseline (p < 0.01) and voltages decreased with depth (P=0.039). Lower voltages of all currents were recorded in the middle of the four electrodes with the highest voltage being recorded outside the four electrodes in all depths (P=0.000).For each frequency of ‘true’ IFC, the voltage was higher in the superficial layer outside the electrodes (P ≤ 0.01).Premodulated had higher voltages along the line of one circuit (P ≤ 0.01). Clinically, IFC appears to pass through skin layers to depth and is more efficient than premodulated IFC when targeting muscle tissue.

Keywords: electrotherapy, interferential current, interferential therapy, medium frequency current

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Authors: Abuzar Baloach

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Spinal cord stimulators (SCS) are increasingly used for managing chronic pain, but their presence requires careful anesthetic planning. This review explores critical anesthetic considerations for patients with SCS, encompassing preoperative, intraoperative, and acute pain management, as well as specific considerations for obstetric and out-of-operating-room procedures. Preoperative Evaluation: Thorough assessment is essential, including a detailed medical history of the SCS device, such as type, manufacturer, and settings. Additionally, a complete pain history and a physical exam are necessary to understand the patient’s baseline neurological function and assess mobility, which can impact anesthesia management. Intraoperative Considerations: Electrocautery poses a risk for patients with SCS due to potential interference. Monopolar electrocautery is discouraged, but if needed, the grounding pad should be positioned away from the device, and the device itself should be turned off. The SCS device can introduce ECG artifacts and potentially interfere with pacemakers and defibrillators (ICD), which may result in inappropriate pacing or shocks. Precautions, including baseline ECG and interrogation, are recommended if both devices are present. Furthermore, lithotripsy, though generally avoided, can be performed under certain conditions with caution. Obstetric Anesthesia: While SCS devices are generally turned off during pregnancy, they have shown no interference with fetal cardiotocography, and epidural placement can be safely achieved with a sterile technique below the SCS leads. Acute Pain Considerations: SCS placement is taken into account in pain management plans, especially with neuraxial anesthesia, as potential risks include infection, limited spread due to fibrous sheaths, and damage to the SCS leads. Out-of-Operating Room Procedures: MRI, previously contraindicated, is now conditionally safe with SCS devices, depending on manufacturer specifications. CT scans are generally safe, though radiation should be minimized to prevent device malfunction. For radiation therapy, specific safety measures are recommended, such as keeping the beam at least 1 cm away from the device and limiting the dose to prevent damage. In conclusion, anesthetic management for SCS patients requires meticulous planning across all stages of care. By understanding the unique interactions and potential risks associated with SCS and other devices, healthcare providers can enhance patient safety and improve outcomes. Further research and the establishment of standardized guidelines are essential to optimize perioperative care for this growing patient population.

Keywords: anesthesia, chronic pain, spinal cord stimulator, SCS

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Authors: Merina Islam

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There are many environmental problems. Various counter measures have been taken for environmental problems. Philosophy is an important contributor to environmental studies as it takes deep interest in meaning analysis of the concept environment and other related concepts. The Buddhist frame, which is virtue ethical, remains a better alternative to the traditional environmental outlook. Granting the unique role of man in immoral deliberations, the Buddhist approach, however, maintains a holistic concept of ecological harmony. Buddhist environmental ethics is more concerned about the complete moral community, the total ecosystem, than any particular species within the community. The moral reorientation proposed here has resemblance to the concept of 'deep ecology. Given the present day prominence of virtue ethics, we need to explore further into the Buddhist virtue theory, so that a better framework to treat the natural world would be ensured. Environment has turned out to be one of the most widely discussed issues in the recent times. Buddhist concepts such as Pratityasamutpadavada, Samvrit Satya, Paramartha Satya, Shunyata, Sanghatvada, Bodhisattva, Santanvada and others deal with interdependence in terms of both internal as well external ecology. The internal ecology aims at mental well-being whereas external ecology deals with physical well-being. The fundamental Buddhist concepts for dealing with environmental Problems are where the environment has the same value as humans as from the two Buddhist doctrines of the Non-duality of Life and its Environment and the Origination in Dependence; and the inevitability of overcoming environmental problems through the practice of the way of the Bodhisattva, because environmental problems are evil for people and nature. Buddhism establishes that there is a relationship among all the constituents of the world. There is nothing in the world which is independent from any other thing. Everything is dependent on others. The realization that everything in the universe is mutually interdependent also shows that the man cannot keep itself unaffected from ecology. This paper would like to focus how the Buddhist’s identification of nature and the Dhamma can contribute toward transforming our understanding, attitudes, and actions regarding the care of the earth. Environmental Ethics in Buddhism presents a logical and thorough examination of the metaphysical and ethical dimensions of early Buddhist literature. From the Buddhist viewpoint, humans are not in a category that is distinct and separate from other sentient beings, nor are they intrinsically superior. All sentient beings are considered to have the Buddha-nature, that is, the potential to become fully enlightened. Buddhists do not believe in treating of non-human sentient beings as objects for human consumption. The significance of Buddhist theory of interdependence can be understood from the fact that it shows that one’s happiness or suffering originates from ones realization or non-realization respectively of the dependent nature of everything. It is obvious, even without emphasis, which in the context of deep ecological crisis of today there is a need to infuse the consciousness of interdependence.

Keywords: Buddhism, deep ecology, environmental problems, Pratityasamutpadavada

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Authors: Vahid Rashtchi, Ashkan Pirooz

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This paper applies the Imperialist Competitive Algorithm (ICA) to find the optimal place and size of Static Compensator (STATCOM) in power systems. The output of the algorithm is a two dimensional array which indicates the best bus number and STATCOM's optimal size that minimizes all bus voltage deviations from their nominal value. Simulations are performed on IEEE 5, 14, and 30 bus test systems. Also some comparisons have been done between ICA and the famous Particle Swarm Optimization (PSO) algorithm. Results show that how this method can be considered as one of the most precise evolutionary methods for the use of optimum compensator placement in electrical grids.

Keywords: evolutionary computation, imperialist competitive algorithm, power systems compensation, static compensators, voltage profile

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Authors: Sana Ansari, Sirus Mohammadi

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In distribution systems, shunt capacitors are used to reduce power losses, to improve voltage profile, and to increase the maximum flow through cables and transformers. This paper presents a new method to determine the optimal locations and economical sizing of fixed and/or switched shunt capacitors with a view to power losses reduction and voltage stability enhancement. General Algebraic Modeling System (GAMS) has been used to solve the maximization modules using the MINOS optimization software with Linear Programming (LP). The proposed method is tested on 33 node distribution system and the results show that the algorithm suitable for practical implementation on real systems with any size.

Keywords: power losses, voltage stability, radial distribution systems, capacitor

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Authors: Mardiati Zain, Jurnida Rahman, Khasrad, Erpomen

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The aim of this experiment was to study the use of palm oil by products (oil palm fronds (OPF), palm oil sludge (POS) and palm kernel cake (PKC)), that supplemented with growth factor rumen microbes (Sapindus rarak and Sacharomyces cerevisiae) on digestibility and fermentation in vitro. Oil Palm Fronds was previously treated with 3% urea. The treatments consist of 50% OPF+ 30% POS+ 20% PKC as a control diet (A), B = A + 4% Sapindus rarak, C = A + 0.5 % Sacharomyces cerevisiae and D = A + 4% Sapindus rarak + 0.5% Sacharomyces cerevisiae. Digestibility of DM, OM, ADF, NDF, cellulose and rumen parameters (NH3 and VFA) of all treatments were significantly different (P < 0.05). Fermentation and digestibility treatment A were significantly lower than treatments B, C, and D. The result indicated that supplementation Sapindus rarak and S. cerevisiae were able to improve fermentability and digestibility of palm oil by product.

Keywords: palm oil by product, Sapindus rarak, Sacharomyces rerevisiae, fermentability, OPF ammoniated

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Authors: Zakia Alioua, Amira Soumia, Zerouali-Khodja Fatiha

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In spite of a wide variety of contaminants such as heavy metals and organic compounds in addition to the importance of extended pollution, the deep-sea and its species are not in haven and being affected through contaminants exposure. This investigation is performed in order to provide data on the presence of pathological changes in the liver and gonads of the greater forkbeard. A total of 998 specimens of the teleost fish Phycis blennoides Brünnich, 1768 ranged from 5,7 to 62,7 cm in total length, were obtained from the commercial fisheries of Algerian ports. The sampling has been carried out monthly from December 2013 to June 2015 and from January to June 2016 caught by trawlers and longlines between 75 and 600 fathoms in the coast of Algeria. Individuals were sexed their gonads, and their livers were removed and processed for light microscopy and one case of atresia was identified. In whole, overall 0,002% of the specimens presented some degree of liver steatosis. For the gastric section, 442 selected stomachs contents were observed looking for parasitic infestation and enumerate 212 nematodes. A prospecting survey for metal contaminant was performed on the liver by atomic absorption spectrophotometry analysis.

Keywords: atresia, coast of Algeria, histopathology, nematode, Phycis blennoides, steatosis

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Authors: Lenka Hurtalová, Eva Tillová, Mária Chalupová, Juraj Belan, Milan Uhríčik

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The secondary alloy A226 is used for many automotive casting produced by mould casting and high pressure die-casting. This alloy has excellent castability, good mechanical properties and cost-effectiveness. Production of primary aluminium alloys belong to heavy source fouling of life environs. The European Union calls for the emission reduction and reduction in energy consumption, therefore, increase production of recycled (secondary) aluminium cast alloys. The contribution is deal with influence of recycling on the quality of the casting made from A226 in automotive industry. The properties of the casting made from secondary aluminium alloys were compared with the required properties of primary aluminium alloys. The effect of recycling on microstructure was observed using combination different analytical techniques (light microscopy upon black-white etching, scanning electron microscopy-SEM upon deep etching and energy dispersive X-ray analysis-EDX). These techniques were used for the identification of the various structure parameters, which was used to compare secondary alloy microstructure with primary alloy microstructure.

Keywords: A226 secondary aluminium alloy, deep etching, mechanical properties, recycling foundry aluminium alloy

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Catherine Omole

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Examination malpractice includes acts that temper with collecting accurate results during the conduct of an examination, thereby giving undue advantage to a student over his colleagues. Even though examination malpractice has been a lingering problem, examinations may not be easy to do away with completely as it is an important feedback tool in the learning process with several other functions e.g for the purpose of selection, placement, certification and promotion. Examination malpractice has created a lot of problems such as a relying on a weak work force based on false assessment results. The question is why is this problem still persisting, despite measures that have been taken to curb this ugly trend over the years? This opinion paper has identified modifications that could help relieve the student of the examination stress and thus increase the student’s effort towards effective learning and discourage examination malpractice in the long run.

Keywords: assessment, examination malpractice, learning, science classroom

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: Agata Chelminska, Joanna Goscianska

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The rapid growth of the world’s population and the resulting economic development have had an enormous influence on the environment. Multiple industrial processes generate huge amounts of wastewater containing dangerous substances, most of which are discharged into water bodies. These contaminants include pharmaceuticals and synthetic dyes. Regardless of the presence of wastewater treatment plants, a lot of pollutants cannot be easily eliminated by well-known technologies. Hence, more effective methods of removing resistant chemicals are being developed. Due to cost-effectiveness as well as the availability of a wide range of adsorbents, a large interest in the adsorption process as an alternative way of water purification has been observed. Clay minerals, e.g., halloysite, are one of the most researched natural adsorbents because of their availability, non-toxicity, high specific surface area, porosity, layered structure, and low cost. The negatively charged surface makes them ideal for binding cations and organic compounds. Halloysite can be subjected to modifications which enhance its adsorptive properties. The aim of the presented research was to apply pure and modified halloysite in removing particular pollutants (tetracycline, tartrazine, and phosphates) from aqueous solutions. Halloysite was modified with alcoholic and aqueous solutions of hexadecyltrimethylammonium bromide (CTAB) and urea in different concentrations and subsequently impregnated with lanthanum(III) chloride. Acidic and basic oxygen groups located on the surface of all materials were determined. Moreover, the adsorbents obtained were characterized by X-ray diffraction, low-temperature nitrogen adsorption, scanning, and transmission electron microscopy. The effectiveness of samples in tetracycline, tartrazine, and phosphates adsorption from the liquid phase was then studied in order to determine their potential application in eliminating contaminants from water reservoirs. Modifiers’ employment enabled obtaining materials that possess better adsorption properties, which makes them useful for removing various pollutants from water. Modifying the pure halloysite with CTAB and urea solutions and impregnating LaCl₃ led to the formation of acidic and basic oxygen functional groups on the surface. Their amount increases with an increasing percentage of lanthanum content. The acid-base properties of materials, as well as the type of functional groups that appear on their surface, have a significant influence on their sorption capacities towards antibiotics, dyes, and phosphate(V) anions. The selected contaminants adsorb onto the halloysite studied following the Langmuir type isotherm. The thermodynamic study indicated that the adsorption was a spontaneous and exothermic process. The adsorption equilibrium was rapidly attained after 120 min of contact time. Research showed that synthesized materials based on halloysite may be applied as adsorbents for antibiotics, organic dyes, and PO₄³- ions which are difficult to eliminate.

Keywords: adsorption processes, halloysite, minerals, water reservoirs pollutants

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Authors: Ghazi Faisal Najmuldeen, Wahida Amat Fadzila

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The aim of this study is to investigate physical and mechanical properties of experimental particleboards manufactured from mixing the oil palm fronds particles with empty fruit bunch fibers. Variables were two blending ratios (100:0 and 70:30), press temperature (160°C and 180°C) and press time (180 and 300 s). Experimental boards with a target density of 750 kg m-3 were manufactured from these two particles and fibers blended with urea formaldehyde resin and compressed into targeted thickness. The effect of these manufacturing conditions on bending strength, internal bonding, water absorption and thickness swelling were determined. From this research, it can be concluded that hybridization of fibers with fronds particles improved some properties of particleboard. Empty fruit bunch fibers and fronds particleboard showed better modulus of rupture and internal bonding than fronds particleboards.

Keywords: oil palm fronds, empty fruit bunch, particleboards, chemistry, environment

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Dalia Ambrazaitienė, Monika Vilkienė, Danute Karcauskienė, Gintaras Siaudinis

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The soil is a complex ecosystem that is part of our biosphere. The ability of soil to provide ecosystem services is dependent on microbial diversity. T Tillage is one of the major factors that affect soil properties. The no-till systems or shallow ploughless tillage are opposite of traditional deep ploughing, no-tillage systems, for instance, increase soil organic matter by reducing mineralization rates and stimulating litter concentrations of the top soil layer, whereas deep ploughing increases the biological activity of arable soil layer and reduces the incidence of weeds. The role of soil organisms is central to soil processes. Although the number of microbial species in soil is still being debated, the metagenomic approach to estimate microbial diversity predicted about 2000 – 18 000 bacterial genomes in 1 g of soil. Despite the key role of bacteria in soil processes, there is still lack of information about the bacterial diversity of soils as affected by tillage practices. This study focused on metagenomic analysis of bacterial diversity in long-term experimental plots of Dystric Epihypogleyic Albeluvisols in western part of Lithuania. The experiment was set up in 2013 and had a split-plot design where the whole-plot treatments were laid out in a randomized design with three replicates. The whole-plot treatments consisted of two tillage methods - deep ploughing (22-25 cm) (DP), ploughless tillage (7-10 cm) (PT). Three subsamples (0-20 cm) were collected on October 22, 2015 for each of the three replicates. Subsamples from the DP and PT systems were pooled together wise to make two composition samples, one representing deep ploughing (DP) and the other ploughless tillage (PT). Genomic DNA from soil sample was extracted from approximately 200 mg field-moist soil by using the D6005 Fungal/Bacterial Miniprep set (Zymo Research®) following the manufacturer’s instructions. To determine bacterial diversity and community composition, we employed a culture – independent approach of high-throughput pyrosequencing of the 16S rRNA gene. Metagenomic sequencing was made with Illumina MiSeq platform in Base Clear Company. The microbial component of soil plays a crucial role in cycling of nutrients in biosphere. Our study was a preliminary attempt at observing bacterial diversity in soil under two common but contrasting tillage practices. The number of sequenced reads obtained for PT (161 917) was higher than DP (131 194). The 10 most abundant genus in soil sample were the same (Arthrobacter, Candidatus Saccharibacteria, Actinobacteria, Acidobacterium, Mycobacterium, Bacillus, Alphaproteobacteria, Longilinea, Gemmatimonas, Solirubrobacter), just the percent of community part was different. In DP the Arthrobacter and Acidobacterium consist respectively 8.4 % and 2.5%, meanwhile in PT just 5.8% and 2.1% of all community. The Nocardioides and Terrabacter were observed just in PT. This work was supported by the project VP1-3.1-ŠMM-01-V-03-001 NKPDOKT and National Science Program: The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: deep ploughing, metagenomics, ploughless tillage, soil community analysis

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Authors: Kritsada Moonpa, Kannipa Motanated, Weerapan Srichan

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Hong Hoi Formation is a Middle Triassic deep marine succession presented in outcrops throughout the Lampang Basin of northern Thailand. The primary goal of this research is to diagnose the paleoenvironment, petrographic compositions, and sedimentary sources of the Hong Hoi Formation in Ban Huat, Ngao District. The Triassic Hong Hoi Formation is chosen because the outcrops are continuous and fossils are greatly exposed and abundant. Depositional environment is reconstructed through sedimentological studies along with facies analysis. The Hong Hoi Formation is petrographically divided into two major facies, they are: sandstones with mudstone interbeds, and mudstones or shale with sandstone interbeds. Sandstone beds are lithic arenite and lithic greywacke, volcanic lithic fragments are dominated. Sedimentary structures, paleocurrent data and lithofacies arrangement indicate that the formation deposited in a part of deep marine abyssal plain environment. The sedimentological and petrographic features suggest that during the deposition the Hong Hoi Formation received sediment supply from nearby volcanic arc. This suggested that the intensive volcanic activity within the Sukhothai Arc during the Middle Triassic is the main sediment source.

Keywords: Sukhothai zone, petrography, Hong Hoi formation, Lampang, Triassic

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Authors: Areej R. Banjar, Abraham G. Campbell

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The use of Mixed Reality (MR) in teaching and training is growing popular and can improve students’ ability to perform technical procedures. This short paper outlines the creation of an interactive educational MR 3D application that aims to improve the quality of instruction for dentistry students. This application is called MRImplantology and aims to teach the fundamentals and preoperative planning of dental implant placement. MRImplantology uses cone-beam computed tomography (CBCT) images as the source for 3D dental models that dentistry students will be able to freely manipulate within a 3D MR world to aid their learning process.

Keywords: augmented reality, education, dentistry, cone-beam computed tomography CBCT, head mounted display HMD, mixed reality

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Lianzhong Zhang, Chao Huang

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Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

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Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Deawan Rakin Ahamed Remal, Sinthia Chowdhury, Sharun Akter Khushbu, Sheak Rashed Haider Noori

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Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent.

Keywords: TTR, NSTTR, text to text recognition, deep learning, natural language processing

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Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

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Authors: V. Ivanov, J. Chu, V. Stabnikov

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Low cost, sustainable, and environmentally friendly microbial cements, grouts, polysaccharides and bioplastics are useful in construction and geotechnical engineering. Construction-related biotechnologies are based on activity of different microorganisms: urease-producing, acidogenic, halophilic, alkaliphilic, denitrifying, iron- and sulphate-reducing bacteria, cyanobacteria, algae, microscopic fungi. The bio-related materials and processes can be used for the bioaggregation, soil biogrouting and bioclogging, biocementation, biodesaturation of water-satured soil, bioencapsulation of soft clay, biocoating, and biorepair of the concrete surface. Altogether with the most popular calcium- and urea based biocementation, there are possible and often are more effective such methods of ground improvement as calcium- and magnesium based biocementation, calcium phosphate strengthening of soil, calcium bicarbonate biocementation, and iron- or polysaccharide based bioclogging. The construction-related microbial biotechnologies have a lot of advantages over conventional construction materials and processes.

Keywords: ground improvement, biocementation, biogrouting, microorganisms

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Dimassi Wissem

Abstract:

In this work, we investigate the effect of combination between the porous silicon (PS) layer passivized with Lithium Bromide (LiBr) and grooving of grain boundaries (GB) in multi crystalline silicon. The grain boundaries were grooved in order to reduce the area of these highly recombining regions. Using optimized conditions, grooved GB's enable deep phosphorus diffusion and deep metallic contacts. We have evaluated the effects of LiBr on the surface properties of porous silicon on the performance of silicon solar cells. The results show a significant improvement of the internal quantum efficiency, which is strongly related to the photo-generated current. We have also shown a reduction of the surface recombination velocity and an improvement of the diffusion length after the LiBr process. As a result, the I–V characteristics under the dark and AM1.5 illumination were improved. It was also observed a reduction of the GB recombination velocity, which was deduced from light-beam-induced-current (LBIC) measurements. Such grooving in multi crystalline silicon enables passivization of GB-related defects. These results are discussed and compared to solar cells based on untreated multi crystalline silicon wafers.

Keywords: Multicrystalline silicon, LiBr, porous silicon, passivation

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Authors: Hiroyuki Aoki

Abstract:

Molecular imaging has attracted much attention to visualize a tumor site in a living body on the basis of biological functions. A fluorescence in vivo imaging technique has been widely employed as a useful modality for small animals in pre-clinical researches. However, it is difficult to observe a site deep inside a body because of a short penetration depth of light. A photo-acoustic effect is a generation of a sound wave following light absorption. Because the sound wave is less susceptible to the absorption of tissues, an in vivo imaging method based on the photoacoustic effect can observe deep inside a living body. The current study developed an in vivo imaging agent for a photoacoustic imaging method. Nano-particles of poly(lactic acid) including indocyanine dye were developed as bio-compatible imaging agent with strong light absorption. A tumor site inside a mouse body was successfully observed in a photo-acoustic image. A photo-acoustic imaging with polymer nano-particle agent would be a powerful method to visualize a tumor.

Keywords: nano-particle, photo-acoustic effect, polymer, dye, in vivo imaging

Procedia PDF Downloads 155

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

Procedia PDF Downloads 425