Search results for: input processing

Authors: Z. Masoumi, B. Moaveni

Abstract:

This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Kalman filter (KF), least square estimation (LSE), structural health monitoring (SHM), structural system identification

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Khaled M. Alhawiti

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This paper aims to analyze the role of natural language processing (NLP). The paper will discuss the role in the context of automated data retrieval, automated question answer, and text structuring. NLP techniques are gaining wider acceptance in real life applications and industrial concerns. There are various complexities involved in processing the text of natural language that could satisfy the need of decision makers. This paper begins with the description of the qualities of NLP practices. The paper then focuses on the challenges in natural language processing. The paper also discusses major techniques of NLP. The last section describes opportunities and challenges for future research.

Keywords: data retrieval, information retrieval, natural language processing, text structuring

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Authors: Vishweshwar V. Dixit

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Kannudi is a reference editor introducing a method of input for Kannada, called OHOK!, that is, Ottu Hāku Ottu Koḍu!. This is especially suited for pressure-sensitive input devices, though the current online implementation uses the regular mechanical keyboard. OHOK! has three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see), and andante (as you say). It may be noted that kandante mode does not follow the phonetic order. However, this model may work well for those who are inclined to visualize as they type rather than vocalize the sounds. Kannudi also demonstrates how domain knowledge can be effectively used to potentially increase speed, accuracy, and user-friendliness. For example, selection of a default vowel, automatic shunyification, and arkification. Also implemented are four types of Deletes that are necessary for phono-syllabic languages like Kannada.

Keywords: kannada, conjunct, reference editor, pressure input

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Nileshkumar Vishnav, Aditya Tatu

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A recent approach of representing graph signals and graph filters as polynomials is useful for graph signal processing. In this approach, the adjacency matrix plays pivotal role; instead of the more common approach involving graph-Laplacian. In this work, we follow the adjacency matrix based approach and corresponding algebraic signal model. We further expand the theory and introduce the concept of similarity of two graphs. The similarity of graphs is useful in that key properties (such as filter-response, algebra related to graph) get transferred from one graph to another. We demonstrate potential applications of the relation between two similar graphs, such as nonuniform filter design, DTMF detection and signal reconstruction.

Keywords: graph signal processing, algebraic signal processing, graph similarity, isospectral graphs, nonuniform signal processing

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Authors: Suk-Hyun Yu, Heeyong Kwon

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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: artificial intelligence, air quality prediction, neural networks, pattern recognition, PM-10

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: Javeed Akhtar, R. K. Pandey, Z. R. Azaz Ahmad Azad

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For the purpose of making different uses of food material for the development of extruded foods are produced using single and twin extruders. Extrusion cooking is a useful and economical tool for processing of novel food. This high temperature, short time processing technology causes chemical and physical changes that alter the nutritional and physical quality of the product. Extrusion processing of food ingredients characteristically depends on associating process conditions that influence the product qualities. The process parameters are optimized for extrusion of food material in order to obtain the maximum nutritive value by inactivating the anti-nutritional factors. The processing conditions such as moisture content, temperature and time are controlled to avoid over heating or under heating which otherwise would result in a product of lower nutritional quality.

Keywords: extrusion processing, single and twin extruder, operating condition of extruders and extruded novel foods, food and agricultural engineering

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Authors: A. A. Adekunle, A. M. Omoare, W. O. Oyediran

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This study was carried out to assess rural women’s skill acquisition in the processing of locust bean in Ipokia Local Government Area of Ogun State, Nigeria. Simple random sampling technique was used to select 90 women locust bean processors for this study. Data were analyzed with descriptive statistics and Pearson Product Moment Correlation. The result showed that the mean age of respondents was 40.72 years. Most (70.00%) of the respondents were married. The mean processing experience was 8.63 years. 93.30% of the respondents relied on information from fellow locust beans processors and friends. All (100%) the respondents did not acquire improved processing skill through trainings and workshops. It can be concluded that the rural women’s skill acquisition on modernized processing techniques was generally low. It is hereby recommend that the rural women processors should be trained by extension service providers through series of workshops and seminars on improved processing techniques.

Keywords: locust bean, processing, skill acquisition, rural women

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Authors: Hafez Fouad

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Telemedicine applications have very low voltage which required High Precision Rectifier Design with high Sensitivity to operate at minimum input Voltage. In this work, we targeted 0.2V input voltage using 65 nm CMOS rectifier for Energy Harvesting Telemedicine application. The proposed rectifier which designed at 2.4GHz using two-stage structure found to perform in a better case where minimum operation voltage is lower than previous published paper and the rectifier can work at a wide range of low input voltage amplitude. The Performance Summary of Full-wave fully gate cross-coupled rectifiers (FWFR) CMOS Rectifier at F = 2.4 GHz: The minimum and maximum output voltages generated using an input voltage amplitude of 2 V are 490.9 mV and 1.997 V, maximum VCE = 99.85 % and maximum PCE = 46.86 %. The Performance Summary of Differential drive CMOS rectifier with external bootstrapping circuit rectifier at F = 2.4 GHz: The minimum and maximum output voltages generated using an input voltage amplitude of 2V are 265.5 mV (0.265V) and 1.467 V respectively, maximum VCE = 93.9 % and maximum PCE= 15.8 %.

Keywords: energy harvesting, embedded system, IoT telemedicine system, threshold voltage minimization, differential drive cmos rectifier, full-wave fully gate cross-coupled rectifiers CMOS rectifier

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Authors: Yeimy L. Quintana, Juan G. Zuluaga, Sandra S. Arango

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The comet assay is a method based on electrophoresis that is used to measure DNA damage in cells and has shown important results in the identification of substances with a potential risk to the human population as innumerable physical, chemical and biological agents. With this technique is possible to obtain images like a comet, in which the tail of these refers to damaged fragments of the DNA. One of the main problems is that the image has unequal luminosity caused by the fluorescence microscope and requires different processing to condition it as well as to know how many optimal comets there are per sample and finally to perform the measurements and determine the percentage of DNA damage. In this paper, we propose the design and implementation of software using Image Processing Toolbox-MATLAB that allows the automation of image processing. The software chooses the optimum comets and measuring the necessary parameters to detect the damage.

Keywords: artificial vision, comet assay, DNA damage, image processing

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Authors: A. M. Omoare, O. O. Sofowora, W. O. Oyediran

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The study was carried out to assess the factors affecting the sustainability of rice processing and marketing in Ogun State, Nigeria. Multi-stage sampling technique was used to select one hundred and twenty (120) respondents for the study. Descriptive statistics was used to describe the objectives while hypotheses were analyzed with Pearson Product Moment Correlation. The result showed that most (85%) of the respondents was less than 50 years old and had been in rice business for more than 6 years. The majority (66.67%) of the respondents got their capitals from cooperative societies. All (100%) the respondents used rice as household food security and source of income. However, efficient rice processing and marketing were affected by inadequate manpower capacity development and inputs. There was a positive and significant relationship between socio-economic characteristics and processing techniques (p < 0.05). It is hereby recommended that extension service providers should introduce improved rice processing systems to the rice millers traders in the study area.

Keywords: sustainability, rice processing, marketing, constraints, millers traders

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Authors: Alexander Hempfing

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The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

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Authors: Hyeong-Gyu Park, Hak-Gon Noh, Beom-Soo Kang, Jeong Kim

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Electromagnetic forming (EMF) process is one of a high-speed forming process, which uses an electromagnetic body (Lorentz) force to deform work-piece. Advantages of EMF are summarized as improvement of formability, reduction in wrinkling, non-contact forming. In this study, the spiral coil is considered to evaluate formability in terms of pressure distribution of the forming process. It also is represented forming results of numerical analysis using ANSYS code. In the numerical simulation, RLC circuit coupled with spiral coil was made to consider the design parameters such as system input current and electromagnetic force. The simulation results show that even though input peak currents level are same level in each case, forming condition is certainly different because of frequency of input current and magnitude of current density and magnetic flux density. Finally, the simulation results appear that electromagnetic forming force apparently affected by input current frequency which determines magnitude of current density and magnetic flux density.

Keywords: electromagnetic forming, high-speed forming, RLC circuit, Lorentz force

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Authors: C. Bunsanit

Abstract:

This paper presents the refinement method for two beams formation of wideband smart antenna. The refinement method for weighting coefficients is based on Fully Spatial Signal Processing by taking Inverse Discrete Fourier Transform (IDFT), and its simulation results are presented using MATLAB. The radiation pattern is created by multiplying the incoming signal with real weights and then summing them together. These real weighting coefficients are computed by IDFT method; however, the range of weight values is relatively wide. Therefore, for reducing this range, the refinement method is used. The radiation pattern concerns with five input parameters to control. These parameters are maximum weighting coefficient, wideband signal, direction of mainbeam, beamwidth, and maximum of minor lobe level. Comparison of the obtained simulation results between using refinement method and taking only IDFT shows that the refinement method works well for wideband two beams formation.

Keywords: fully spatial signal processing, beam forming, refinement method, smart antenna, weighting coefficient, wideband

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Authors: Edwin Forero-Garcia, Jaime Vitola, Brayan Cardenas, Johan Casagua

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This research article presents the integration of electronic and computer systems, which developed an ultrasonic signal processing system that performs the capture, adaptation, and analog-digital conversion to later carry out its processing and visualization. The capture and adaptation of the signal were carried out from the design and implementation of an analog electronic system distributed in stages: 1. Coupling of impedances; 2. Analog filter; 3. Signal amplifier. After the signal conditioning was carried out, the ultrasonic information was digitized using a digital microcontroller to carry out its respective processing. The digital processing of the signals was carried out in MATLAB software for the elaboration of A-Scan, B and D-Scan types of ultrasonic images. Then, advanced processing was performed using the SAFT technique to improve the resolution of the Scan-B-type images. Thus, the information from the ultrasonic images was displayed in a user interface developed in .Net with Visual Studio. For the validation of the system, ultrasonic signals were acquired, and in this way, the non-invasive inspection of the structures was carried out and thus able to identify the existing pathologies in them.

Keywords: acquisition, signal processing, ultrasound, SAFT, HMI

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Authors: Elif Unveren, Ozlem Bozkurt

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Emotion processing happens when someone has a negative, stressful experience and gets over it in time, and it is a different experience for every person. As to look into emotion processing can be categorised by intensity, awareness, coordination, speed, accuracy and response. It may vary depending on people’s age, sex and conditions. Each emotion processing shows different activation patterns in different brain regions. Activation is significantly higher in the right frontal areas. The highest activation happens in extended frontotemporal areas during the processing of happiness, sadness and disgust. Those emotions also show widely disturbed differences and get produced earlier than anger and fear. For different occasions, listed variables may have less or more importance. A borderline personality disorder is a condition that creates an unstable personality, sudden mood swings and unpredictability of actions. According to a study that was made with healthy people and people who had BPD, there were significant differences in some categories of emotion processing, such as intensity, awareness and accuracy. According to another study that was made to show the emotional processing differences between puberty and was made for only females who were between the ages of 11 and 17, it was perceived that for different ages and hormone levels, different parts of the brain are used to understand the given task. Also, in the different study that was made for kids that were between the age of 4 and 15, it was observed that the older kids were processing emotion more intensely and expressing it to a greater extent. There was a significant increase in fear and disgust in those matters. To sum up, we can say that the activity of undertaking negative experiences is a unique thing for everybody for many different reasons.

Keywords: age, sex, conditions, brain regions, emotion processing

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Authors: Shreya Borthakur, Aastha Vartak

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This Electroencephalogram (EEG) project on Rapid Visual Serial Processing (RSVP) paradigm explores the temporal dynamics of visual attention processing in response to rapidly presented visual stimuli. The study builds upon previous research that used real-world images in RSVP tasks to understand the emergence of object representations in the human brain. The objectives of the research include investigating the differences in accuracy and reaction times between 5 Hz and 20 Hz presentation rates, as well as examining the prominent brain waves, particularly alpha and beta waves, associated with the attention task. The pre-processing and data analysis involves filtering EEG data, creating epochs for target stimuli, and conducting statistical tests using MATLAB, EEGLAB, Chronux toolboxes, and R. The results support the hypotheses, revealing higher accuracy at a slower presentation rate, faster reaction times for less complex targets, and the involvement of alpha and beta waves in attention and cognitive processing. This research sheds light on how short-term memory and cognitive control affect visual processing and could have practical implications in fields like education.

Keywords: RSVP, attention, visual processing, attentional blink, EEG

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Authors: Sang Ho Lee, Tae Heon Moon, Youn Taik Leem, Kwang Woo Nam

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Knowledge and IT inputs to other industrial production have become more important as a key factor for the competitiveness of national and regional economies, such as knowledge economies in smart cities. Knowledge and IT industries lead the industrial innovation and technical (r)evolution through low cost, high efficiency in production, and by creating a new value chain and new production path chains, which is referred as knowledge and IT dynamics. This study aims to investigate the knowledge and IT dynamics in Korea, which are analyzed through the input-output model and structural path analysis. Twenty-eight industries were reclassified into seven categories; Agriculture and Mining, IT manufacture, Non-IT manufacture, Construction, IT-service, Knowledge service, Non-knowledge service to take close look at the knowledge and IT dynamics. Knowledge and IT dynamics were analyzed through the change of input output coefficient and multiplier indices in terms of technical innovation, as well as the changes of the structural paths of the knowledge and IT to other industries in terms of new production value creation from 1985 and 2010. The structural paths of knowledge and IT explain not only that IT foster the generation, circulation and use of knowledge through IT industries and IT-based service, but also that knowledge encourages IT use through creating, sharing and managing knowledge. As a result, this paper found the empirical investigation on the knowledge and IT dynamics of the Korean economy. Knowledge and IT has played an important role regarding the inter-industrial transactional input for production, as well as new industrial creation. The birth of the input-output production path has mostly originated from the knowledge and IT industries, while the death of the input-output production path took place in the traditional industries from 1985 and 2010. The Korean economy has been in transition to a knowledge economy in the Smart City.

Keywords: knowledge and IT industries, input-output model, structural path analysis, dynamics of knowledge and it, knowledge economy, knowledge city and smart city

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Authors: Abbas Nesayan

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Background: In recent years, autism has been under consideration in public and research area. Autistic children have dysfunction in communication, socialization, repetitive and stereotyped behaviors. In addition, they clinically suffer from difficulty in attention, challenge with familiar behaviors and sensory processing problems. Several variables are linked to sensory processing problems in autism, one of these variables is working memory. Working memory is part of the executive function which provides the necessary ability to completing multiple stages tasks. Method: This study has categorized in correlational research methods. After determining of entry criteria, according to purposive sampling method, 50 children were selected. Dunn’s sensory profile school companion was used for assessment of sensory processing patterns; behavioral rating inventory of executive functions was used (BRIEF) for assessment of working memory. Pearson correlation coefficient and linear regression were used for data analyzing. Results: The results showed the significant relationship between sensory processing patterns (low registration, sensory seeking, sensory sensitivity and sensory avoiding) with working memory in autistic children. Conclusion: According to the findings, there is the significant relationship between the patterns of sensory processing and working memory. So, in order to improve the working memory could be used some interventions based on the sensory processing.

Keywords: sensory processing patterns, working memory, autism, autistic children

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Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

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Authors: Daniel Benson, Yundan Gong, Hannah Kirk

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Advancement in national language processing techniques has led to increased data processing speeds, and reduced the need for cumbersome, manual data processing that is often required when processing data from multilateral organizations for specific purposes. As such, using named entity recognition (NER) modeling and the Organisation of Economically Developed Countries (OECD) Creditor Reporting System database, we present the first geotagged dataset of OECD donor Official Development Assistance (ODA) projects on a global, subnational basis. Our resulting data contains 52,086 ODA projects geocoded to subnational locations across 115 countries, worth a combined $87.9bn. This represents the first global, OECD donor ODA project database with geocoded projects. We use this new data to revisit old questions of how ‘well’ donors allocate ODA to the developing world. This understanding is imperative for policymakers seeking to improve ODA effectiveness.

Keywords: international aid, geocoding, subnational data, natural language processing, machine learning

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Authors: Edgar Gasafi, Robert Pardemann, Linus Perander

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For the production of lithium carbonate or hydroxide out of lithium bearing ores, a thermal activation (calcination/decrepitation) is required for the phase transition in the mineral to enable an acid respectively soda leaching in the downstream hydrometallurgical section. In this paper, traditional processing in Lithium industry is reviewed, and opportunities to reduce energy consumption and improve product quality and recovery rate will be discussed. The conventional process approach is still based on rotary kiln calcination, a technology in use since the early days of lithium ore processing, albeit not significantly further developed since. A new technology, at least for the Lithium industry, is fluidized bed calcination. Decrepitation of lithium ore was investigated at Outotec’s Frankfurt Research Centre. Focusing on fluidized bed technology, a study of major process parameters (temperature and residence time) was performed at laboratory and larger bench scale aiming for optimal product quality for subsequent processing. The technical feasibility was confirmed for optimal process conditions on pilot scale (400 kg/h feed input) providing the basis for industrial process design. Based on experimental results, a comprehensive Aspen Plus flow sheet simulation was developed to quantify mass and energy flow for the rotary kiln and fluidized bed system. Results show a significant reduction in energy consumption and improved process performance in terms of temperature profile, product quality and plant footprint. The major conclusion is that a substantial reduction of energy consumption can be achieved in processing Lithium bearing ores by using fluidized bed based systems. At the same time and different from rotary kiln process, an accurate temperature and residence time control is ensured in fluidized-bed systems leading to a homogenous temperature profile in the reactor which prevents overheating and sintering of the solids and results in uniform product quality.

Keywords: calcination, decrepitation, fluidized bed, lithium, spodumene

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Authors: Juan Jimenez, Erika Yambay, Dayana Pilco, Brayan Parra

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This paper presents an investigation of voice signal processing and coding using MATLAB, with the objective of generating a plasma signal on a Tesla coil within a security system. The approach focuses on using advanced voice signal processing techniques to encode and modulate the audio signal, which is then amplified and applied to a Tesla coil. The result is the creation of a striking visual effect of voice-controlled plasma with specific applications in security systems. The article explores the technical aspects of voice signal processing, the generation of the plasma signal, and its relationship to security. The implications and creative potential of this technology are discussed, highlighting its relevance at the forefront of research in signal processing and visual effect generation in the field of security systems.

Keywords: voice signal processing, voice signal coding, MATLAB, plasma signal, Tesla coil, security system, visual effects, audiovisual interaction

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Authors: D. J. Dahigaonkar, D. G. Wakde

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The performance analysis of low voltage low power CMOS OTA is presented in this paper. The differential input single output OTA is simulated in 180nm CMOS process technology. The simulation results indicate high bandwidth of the order of 7.04GHz with 0.766mW power consumption and transconductance of -71.20dB. The total harmonic distortion for 100mV input at a frequency of 1MHz is found to be 2.3603%. In addition to this, to establish comparative analysis of designed OTA and analyze effect of technology scaling, the differential input single output OTA is further simulated using 350nm CMOS process technology and the comparative analysis is presented in this paper.

Keywords: Operational Transconductance Amplifier, Total Harmonic Distortions, low voltage/low power, power dissipation

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Authors: Ow Tze Weng, Suhaila Isaak, Yusmeeraz Yusof

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The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG sensors are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a high performance CMOS amplifier for ECG sensors that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV/√Hz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC.

Keywords: CMOS, ECG, amplifier, low power

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Authors: Timir Baran Roy, Luis Guerreiro, Ashutosh Bagchi

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Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as bridges, dams, high-rise buildings etc. There had been a substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as natural frequency, modal damping, and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototypes of such walls have been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated, and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.

Keywords: frequency domain decomposition (fdd), modal parameters, signal processing, stochastic subspace identification (ssi), time domain decomposition

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Authors: M. F. Wu, F. T. Cheng

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Leather industry is the most important traditional industry to provide the leather products in the world for thousand years. The fierce global competitive environment and common awareness of global carbon reduction make livestock supply quantities falling, salt and wet blue leather material reduces and the price skyrockets significantly. Exchange rate fluctuation led sales revenue decreasing which due to the differences of export exchanges and compresses the overall profitability of leather industry. This paper applies activity-based benefit assessment approach to build up fitness fur input mixed model, fur is Wet Blue, which concerned with four key factors: the output rate of wet blue, unit cost of wet blue, yield rate and grade level of Wet Blue to achieve the low cost strategy under given unit price of leather product condition of the company. The research findings indicate that applying this model may improve the input cost structure, decrease numbers of leather product inventories and to raise the competitive advantages of the enterprise in the future.

Keywords: activity-based benefit assessment approach, input mixed, output rate, wet blue

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

Abstract:

Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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