Search results for: sensors networks

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: T. Alghamdi, G. Alaghband

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In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

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Authors: Demet Tatar, Bahattin Düzgün

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In this study, we have newly developed titanium-tin oxide (TiSnO) thin films as the transparent conducting oxides materials by the spray pyrolysis technique. Tin oxide thin films doped with different Ti content were successfully grown by spray pyrolysis and they were characterized as a function of Ti content. The effect of Ti contents on the crystalline structure and optical properties of the as-deposited SnO2:Ti films was systematically investigated by X-ray diffraction (XRD), scanning electronic microscopy (SEM), atomic force microscopy (AFM), UV-vis spectrometer and photoluminecenc spectrophotometer. The X-ray diffraction patterns taken at room temperature showed that the films are polycrystalline. The preferred directions of crystal growth appeared in the difractogram of SnO2: Ti (TiTO) films were correspond to the reflections from the (110), (200), (211) and (301) planes. The grain size varies from 21.8 to 27.8 nm for (110) preferred plane. SEM and AFM study reveals the surface of TiTO to be made of nanocrystalline particles. The highest visible transmittance (570 nm) of the deposited films is 80 % for 20 wt % titanium doped tin oxide films. The obtained results revealed that the structures and optical properties of the films were greatly affected by doping levels. These films are useful as conducting layers in electro chromic and photovoltaic devices.

Keywords: transparent conducting oxide, gas sensors, SnO2, Ti, optoelectronic, spray pyrolysis

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Authors: Sharanya C. H., Pragathi M., Vathsala R. S., Theja K. V., Yashaswini S.

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Identifying counterfeit products have become increasingly important in the product manufacturing industries in recent decades. This current ongoing product issue of counterfeiting has an impact on company sales and profits. To address the aforementioned issue, a functional blockchain technology was implemented, which effectively prevents the product from being counterfeited. By utilizing the blockchain technology, consumers are no longer required to rely on third parties to determine the authenticity of the product being purchased. Blockchain is a distributed database that stores data records known as blocks and several databases known as chains across various networks. Counterfeit products are identified using a QR code reader, and the product's QR code is linked to the blockchain management system. It compares the unique code obtained from the customer to the stored unique code to determine whether or not the product is original.

Keywords: blockchain, ethereum, QR code

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Authors: Ouafa Benaida, Abdelhamid Loukil, Adda Ali Pacha

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In recent years, the growing interest of scientists in the field of image processing and analysis of underwater images and videos has been strengthened following the emergence of new underwater exploration techniques, such as the emergence of autonomous underwater vehicles and the use of underwater image sensors facilitating the exploration of underwater mineral resources as well as the search for new species of aquatic life by biologists. Indeed, underwater images and videos have several defects and must be preprocessed before their analysis. Underwater landscapes are usually darkened due to the interaction of light with the marine environment: light is absorbed as it travels through deep waters depending on its wavelength. Additionally, light does not follow a linear direction but is scattered due to its interaction with microparticles in water, resulting in low contrast, low brightness, color distortion, and restricted visibility. The improvement of the underwater image is, therefore, more than necessary in order to facilitate its analysis. The research presented in this paper aims to implement and evaluate a set of classical techniques used in the field of improving the quality of underwater images in several color representation spaces. These methods have the particularity of being simple to implement and do not require prior knowledge of the physical model at the origin of the degradation.

Keywords: underwater image enhancement, histogram normalization, histogram equalization, contrast limited adaptive histogram equalization, single-scale retinex

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Authors: Carolina Gouveia, José Vieira, Pedro Pinho

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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.

Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR

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Authors: Juan Carlos Montalvo-Rodriguez, Juan Felipe Espinosa-Cristia, Pablo Islas Madariaga, Jorge Cifuentes Valenzuela

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This article presents different relationships between top management and innovative companies, based on the developments of creativity and innovation management. First of all, it contextualizes the innovative company in relation to management, creativity, and innovation. Secondly, it delves into the vision of top management of innovative companies, from the perspectives of the management of creativity and innovation. Thirdly, their commonalities are highlighted, bearing in mind the importance that both approaches attribute to aspects such as leadership, networks, strategy, culture, technology, environment, and complexity in the top management of innovative companies. Based on the above, an integration of both fields of study is proposed, as an alternative to deepen the relationship between senior management and the innovative company.

Keywords: top management, creativity, innovation, innovative firm, leadership, strategy

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Riddhi Jangid, Gajendra Pratap Singh

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Petri nets, a mathematical tool, is used for modeling in different areas of computer sciences, biological networks, chemical systems and many other disciplines. A Petri net model of a given system is created by the graphical representation that describes the properties and behavior of the system. While looking for the behavior of any system, 1-safe Petri nets are of particular interest to many in the application part. Boolean Petri nets correspond to those class in 1- safe Petri nets that generate all the binary n-vectors in their reachability analysis. We study the class by changing different parameters like the token counts in the places and how the structure of the tree changes in the reachability analysis. We discuss here an extended class of Boolean Petri nets that generates n-ary trees in their reachability-based analysis.

Keywords: marking vector, n-vector, petri nets, reachability

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Authors: İkbal Eski

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This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

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Authors: Sangeeta Palekar, Mahima Rana, Jayu Kalambe

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In this paper, a microfluidic-based platform for the quantification of contaminants in the water is proposed. The proposed system uses microfluidic channels with an embedded environment for contaminants detection in water. Microfluidics-based platforms present an evident stage of innovation for fluid analysis, with different applications advancing minimal efforts and simplicity of fabrication. Polydimethylsiloxane (PDMS)-based microfluidics channel is fabricated using a soft lithography technique. Vertical and horizontal connections for fluid dispensing with the microfluidic channel are explored. The principle of colorimetry, which incorporates the use of Griess reagent for the detection of nitrite, has been adopted. Nitrite has high water solubility and water retention, due to which it has a greater potential to stay in groundwater, endangering aquatic life along with human health, hence taken as a case study in this work. The developed platform also compares the detection methodology, containing photodetectors for measuring absorbance and image sensors for measuring color change for quantification of contaminants like nitrite in water. The utilization of image processing techniques offers the advantage of operational flexibility, as the same system can be used to identify other contaminants present in water by introducing minor software changes.

Keywords: colorimetric, fluid contaminants, nitrite detection, microfluidics

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Authors: Sangguk Ahn, Youngyu Kim, Jaheon Gu, Gyoutae Park

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In this paper, we developed a dual gas leak instrument to detect Hydrocarbon (HC) and Monoxide (CO) gases. To two kinds of gases, it is necessary to design compact structure for sensors. And then it is important to draw sensing circuits such as measuring, amplifying and filtering. After that, it should be well programmed with robust, systematic and module coding methods. In center of them, improvement of accuracy and initial response time are a matter of vital importance. To manufacture distinguished gas leak detector, we applied intrinsically safe explosion-proof structure to lithium ion battery, main circuits, a pump with motor, color LCD interfaces and sensing circuits. On software, to enhance measuring accuracy we used numerical analysis such as Lagrange and Neville interpolation. Performance test result is conducted by using standard Methane with seven different concentrations with three other products. We want raise risk prevention and efficiency of gas safe management through distributing to the field of gas safety. Acknowledgment: This study was supported by Small and Medium Business Administration under the research theme of ‘Commercialized Development of a portable intrinsically safe explosion-proof type dual gas leak detector’, (task number S2456036).

Keywords: gas leak, dual gas detector, intrinsically safe, explosion proof

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Anwar Jarndal

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In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.

Keywords: GaN HEMT, computer-aided design and modeling, neural networks, genetic optimization

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Authors: Younghyun Jeon, Seungjoo Maeng

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In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point.

Keywords: ad-hoc network, MIMO, cooperative beamforming, transmit power

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Authors: Arunas Stirke, Neringa Bakute, Gatis Mozolevskis

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A technology of microfluidics is an emerging tool in the field of biology, medicine and chemistry. Microfluidic device is also known as ‘lab-on-a-chip’ technology [1]. In moving from macro- to microscale, there is unprecedented control over spatial and temporal gradients and patterns that cannot be captured in conventional Petri dishes and well plates [2]. However, there is not a single standard microfluidic chip designated for all purposes – every different field of studies needs a specific microchip with certain geometries, inlet/outlet, channel depth and other parameters to precisely regulate the required function. Since our group is studying an effect of pulsed electric field (PEF) to the cells, we have manufactured a microfluidic chip designated for high-throughput electroporation of cells. In our microchip, a cell culture chamber is divided into two parallel channels by a membrane, meanwhile electrodes for electroporation are attached to the wall of the channels. Both microchannels have their own inlet and outlet, enabling injection of transfection material separately. Our perspective is to perform electroporation of mammalian cells in two different ways: (1) plasmid and cells are injected in the same microchannel and (2) injected into separate microchannels. Moreover, oxygen and pH sensors are integrated on order to analyse cell viability parameters after PEF treatment.

Keywords: microfluidics, chip, fabrication, electroporation

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Authors: Reira Kinoshita, Shin'ichi Ishimaru

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Vapochromism is a type of chromism in which the color of a substance changes when it is exposed to the vapor of volatile materials, and has been investigated for the application of chemical sensors for volatile organic compounds causing sick building syndrome and health hazards in workspaces. We synthesized intercalation compounds of 3,3',5,5'-tetramethylbenzidine (TMB), and tetrasilisicfluormica (TSFM) by the commonly used cation-exchange method with the cation ratio TMB²⁺/CEC of TSFM = 1.0, 2.0, 2.7 and 5.4 to investigate the vapochromism of these materials. The obtained samples were characterized by powder XRD, XRF, TG-DTA, N₂ adsorption, and SEM. Vapochromism was measured for each sample under a controlled atmosphere by a handy reflectance spectrometer directly from the outside of the glass sample tubes. The color was yellow for all specimens vacuum-dried at 50 °C, but it turned green under H₂O vapor exposure for the samples with TMB²⁺/CEC = 2.0, 2.7, and 5.4 and blue under acetonitrile vapor for all cation ratios. Especially the sample TMB²⁺/CEC = 2.0 showed clear chromism both for water and acetonitrile. On the other hand, no clear color change was observed for vapors of alcohols, acetone, and non-polar solvents. From these results, this material can be expected to apply for easy detection of humidity and acetonitrile vapor in the environment.

Keywords: chemical sensor, intercalation compound, tetramethylbenzidine, tetrasilisicfluormica, vapochromism, volatile organic compounds

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Authors: Daniel Hyun

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Dental implants are one of the top 3 reasons to sue a dentist for malpractice. It involves dental implant complications, usually because of the angle of the implant from the surgery. At present, surgeons usually use a 3D-printed navigator that is customized for the patient’s teeth. However, those can’t be reused for other patients as they require time. Therefore, I made a guiding device to assist the surgeon in implant operations. The surgeon can input the objective of the operation, and the device constantly checks if the surgery is heading towards the objective within the set range, telling the surgeon by manipulating the LED. We tested the prototypes’ consistency and accuracy by checking the graph, average standard deviation, and the average change of the calculated angles. The accuracy of performance was also acquired by running the device and checking the outputs. My first prototype used accelerometer and gyroscope sensors from the Arduino MPU6050 sensor, getting a changeable graph, achieving 0.0295 of standard deviations, 0.25 of average change, and 66.6% accuracy of performance. The second prototype used only the gyroscope, and it got a constant graph, achieved 0.0062 of standard deviation, 0.075 of average change, and 100% accuracy of performance, indicating that the accelerometer sensor aggravated the functionality of the device. Using the gyroscope sensor allowed it to measure the orientations of separate axes without affecting each other and also increased the stability and accuracy of the measurements.

Keywords: implant, guide, accelerometer, gyroscope, handpiece

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Authors: Lucia Antonela Mitidieri

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This research analyzes the application of a model of settlements management based on networks of territorial integration that advocates planning as a cyclical and participatory process that engages early on with civic society, the private sector and the state. Through qualitative methods such as participant observation, interviews with snowball technique and an active research on territories, concrete results of community empowerment are obtained from the promotion of productive enterprises and community spaces of encounter and exchange. Studying the cultural and organizational dimensions of empowerment allows building indicators such as increase of capacities or community cohesion that can lead to support local governments in achieving sustainable urban development for a reduction of urban poverty.

Keywords: community spaces, empowerment, network urbanism, participatory process

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Authors: Rafael Castilho Carvalho, Edjair de Souza Mota

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As the largest contributor to the biogeochemical functioning of the Earth system, the Amazon Rainforest has the greatest biodiversity on the planet, harboring about 15% of all the world's flora. Recognition and preservation are the focus of research that seeks to mitigate drastic changes, especially anthropic ones, which irreversibly affect this biome. Functional and low-cost monitoring alternatives to reduce these impacts are a priority, such as those using technologies such as Low Power Wide Area Networks (LPWAN). Promising, reliable, secure and with low energy consumption, LPWAN can connect thousands of IoT devices, and in particular, LoRa is considered one of the most successful solutions to facilitate forest monitoring applications. Despite this, the forest environment, in particular the Amazon Rainforest, is a challenge for these technologies, requiring work to identify and validate the use of technology in a real environment. To investigate the feasibility of deploying LPWAN in remote water quality monitoring of rivers in the Amazon Region, a LoRa-based test bed consisting of a Lora transmitter and a LoRa receiver was set up, both parts were implemented with Arduino and the LoRa chip SX1276. The experiment was carried out at the Federal University of Amazonas, which contains one of the largest urban forests in Brazil. There are several springs inside the forest, and the main goal is to collect water quality parameters and transmit the data through the forest in real time to the gateway at the uni. In all, there are nine water quality parameters of interest. Even with a high collection frequency, the amount of information that must be sent to the gateway is small. However, for this application, the battery of the transmitter device is a concern since, in the real application, the device must run without maintenance for long periods of time. With these constraints in mind, parameters such as Spreading Factor (SF) and Coding Rate (CR), different antenna heights, and distances were tuned to better the connectivity quality, measured with RSSI and loss rate. A handheld spectrum analyzer RF Explorer was used to get the RSSI values. Distances exceeding 200 m have soon proven difficult to establish communication due to the dense foliage and high humidity. The optimal combinations of SF-CR values were 8-5 and 9-5, showing the lowest packet loss rates, 5% and 17%, respectively, with a signal strength of approximately -120 dBm, these being the best settings for this study so far. The rains and climate changes imposed limitations on the equipment, and more tests are already being conducted. Subsequently, the range of the LoRa configuration must be extended using a mesh topology, especially because at least three different collection points in the same water body are required.

Keywords: IoT, LPWAN, LoRa, coverage, loss rate, forest

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Authors: Shuo Liu

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In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Qiaoyun Yang

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Organizational scholars have paid enormous attention on how local governments manage their knowledge during the past two decades. Government knowledge management (KM) research recognizes that the management of knowledge flows and networks is critical to reforms on government service efficiency and the effect of administration. When dealing with complex affairs, all the limitations resulting from a lack of KM concept, processes and technologies among all the involved organizations begin to be exposed and further compound the processing difficulty of the affair. As a result, the challenges for individual or group knowledge sharing, knowledge digging and organizations’ collaboration in government's activities are diverse and immense. This analysis presents recent situation of government KM in China drawing from a total of more than 300 questionnaires and highlights important challenges that remain. The causes of the lapses in KM processes within and across the government agencies are discussed.

Keywords: KM processes, KM technologies, government, KM situation

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Authors: Amanpreet Kaur, Ravinder Agarwal, Amod Kumar

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Surface electromyography is a strategy to measure the muscle activity of the skin. Sensors placed on the skin recognize the electrical current or signal generated by active muscles. A lot of the research has focussed on the detection of signal from upper limb amputee with activity of triceps and biceps muscles. The purpose of this study was to correlate phantom movement and sEMG activity in residual stump muscles of transhumeral amputee from the shoulder muscles. Eight non- amputee and seven right hand amputees were recruited for this study. sEMG data were collected for the trapezius, pectoralis and teres muscles for elevation, protraction and retraction of shoulder. Contrast between the amputees and non-amputees muscles action have been investigated. Subsequently, to investigate the impact of class separability for different motions of shoulder, analysis of variance for experimental recorded data was carried out. Results were analyzed to recognize different shoulder movements and represent a step towards the surface electromyography controlled system for amputees. Difference in F ratio (p < 0.05) values indicates the distinction in mean therefore these analysis helps to determine the independent motion. The identified signal would be used to design more accurate and efficient controllers for the upper-limb amputee for researchers.

Keywords: around shoulder amputation, surface electromyography, analysis of variance, features

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Authors: Alireza Saidi

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Aside from injuries due to direct contact with hot or cold substances or objects, exposure to extreme temperatures in the workplace involves physical hazards to workers. On the other hand, a poorly acclimatized worker may have reduced performance and alertness and may, therefore, be more vulnerable to the risk of accidents and injuries. Due to the incompatibility of the standards put in place with certain workplaces and the lack of thermoregulation in many protective equipments, thermal strains remain among the physical risks most present in many work sectors. However, many of these problems can be overcome thanks to the potential of intelligent textile technologies allowing intelligent thermoregulation in protective equipment. Nowadays, technologies such as heating elements, cooling elements are applied in products intended for sport and leisure, and research work has been carried out in the integration of temperature sensors and thermal stress detectors in personal protective equipment. However, the usage of all of these technologies in personal protective equipment remains very marginal. This article presents a portrait of the current state of intelligent thermoregulation systems by carrying out a synthesis of technical developments, which is accompanied by a gap analysis of current developments. Thus, the research work necessary for the adaptation and integration of intelligent thermoregulation systems with personal protective equipment is discussed in order to offer a perspective of future developments.

Keywords: personal protective equipment, smart textiles, thermoregulation, thermal strain

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Authors: Allamula Ashok, Peela Lasya, Daljin Jacob, P. Muhammed Razi, Satyesh Kumar Yadav

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We report zinc (Zn) as a seed layer material and a need for a specific disposition sequence to grow ultrathin silver (Ag) films on quartz (SiO₂). Ag films of thickness 4, 6, 8 and 10 nm were deposited by DC magnetron sputtering without and with Zn seed layer thickness of 1, 2 and 4 nm. The effect of Zn seed layer thickness and its annealing on the surface morphology, sheet resistance, and stability of ultrathin Ag films is investigated. We show that by increasing Zn seed layer thickness from 1 to 2 nm, there is a 5-order reduction in sheet resistance of 6 nm Ag films. We find that annealing of the seed layer is crucial to achieving stability of ultrathin Ag films. 6 nm Ag film with 2 nm Zn is unstable to 100 oC annealing, while the 6 nm Ag film with annealed 2 nm Zn seed layer is stable. 2 nm Zn seeded 8 nm Ag film maintained a constant sheet resistance of 7 Ω/□ for all 6 months of exposure to ambient conditions. Among the ultrathin film grown, 8nm Ag film with 2nm Zn seed layer had the best figure of merit with sheet resistance of 7 Ω/□, mean absolute surface roughness (Ra) ~1 nm, and optical transparency of 61 %. Such stable exposed ultrathin Ag films can find applications as catalysts, sensors, and transparent and conductive electrodes for solar cells, LEDs and plasmonic devices.

Keywords: ultrathin Ag films, magnetron sputtering, thermal stability, seed layer, exposed silver, zinc.

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Authors: Gunasekaran Raja, Rajakumar Arul, Kottilingam Kottursamy, Ramkumar Jayaraman, Sathya Pavithra, Swaminathan Venkatraman

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Wireless Interoperability for Microwave Access (WiMAX) utilizes the IEEE 802.1X mechanism for authentication. However, this mechanism incurs considerable delay during handoffs. This delay during handoffs results in service disruption which becomes a severe bottleneck. To overcome this delay, our article proposes a key caching mechanism based on user path prediction. If the user mobility follows that path, the user bypasses the normal IEEE 802.1X mechanism and establishes the necessary authentication keys directly. Through analytical and simulation modeling, we have proved that our mechanism effectively decreases the handoff delay thereby achieving fast authentication.

Keywords: authentication, authorization, and accounting (AAA), handoff, mobile, user path prediction (UPP) and user pattern

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Authors: Ondrej Lufinka, Jan Kaderabek, Juraj Prstek, Jiri Skala, Kamil Kosturik

Abstract:

This paper deals with the problem of using Autonomous Robotic Platforms (ARP) for the ADAS (Advanced Driver Assistance Systems) testing in automotive. There are different possibilities of the testing already in development, and lately, the autonomous robotic platforms are beginning to be used more and more widely. Autonomous Robotic Platform discussed in this paper explores the hardware and software design possibilities related to the field of embedded systems. The paper focuses on its chapters on the introduction of the problem in general; then, it describes the proposed prototype concept and its principles from the embedded HW and SW point of view. It talks about the key features that can be used for the innovation of these platforms (e.g., modularity, omnidirectional movement, common and non-traditional sensors used for localization, synchronization of more platforms and cars together, or safety mechanisms). In the end, the future possible development of the project is discussed as well.

Keywords: advanced driver assistance systems, ADAS, autonomous robotic platform, embedded systems, hardware, localization, modularity, multiple robots synchronization, omnidirectional movement, safety mechanisms, software

Procedia PDF Downloads 126

Authors: Mohsen Azarmjoo, Yasaman Azarmjoo, Zahra Alikhani Koopaei

Abstract:

This article introduces devices that can transfer sunlight to buildings that do not have access to direct sunlight during the day. The transmission and reflection of sunlight are done through the movement of movable mirrors. The focus of this article is on two models of sun tracker systems designed and built by the Macad team. In fact, this article will reveal the distinction between the two Macad devices and the previously built competitor device. What distinguishes the devices built by the Macad team from the competitor's device is the different mode of operation and the difference in the location of the sensors. Given that the devices have the same results, the Macad team has tried to reduce the defects of the competitor's device as much as possible. The special feature of the second type of device built by the Macad team has enabled buildings with different construction positions to use sun tracking systems. This article will also discuss diagrams of the path of sunlight transmission and more details of the device. It is worth mentioning that fixed mirrors are also placed next to the main devices. So that the light shining on the first device is reflected to these mirrors, this light is guided within the light receiver space and is transferred to the different parts around by steel sheets built in the light receiver space, and finally, these spaces benefit from sunlight.

Keywords: design, construction, mechatronic device, sun tracker system, sun tracker, sunlight

Procedia PDF Downloads 56

Authors: Sadiq J. Abou-Loukh

Abstract:

The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

Procedia PDF Downloads 451