Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Depression Detection from Social Network Using Deep Learning Techniques

Depression is a psychological disorder that affects over three hundred million humans worldwide. A person who is depressed suffers from anxiety in day-to-day life, which affects that person in the relationship with their family and friends, leading to different diseases and in the worst-case death by suicide. With the growth of the social network, most of the people share their emotion, their feelings, their thoughts in social media. If their depression can be detected early by analyzing their post, then by taking necessary steps, a person can be saved from depression-related diseases or in the best case he can be saved from committing suicide. In this research work, a hybrid model has been proposed that can detect depression by analyzing user's textual posts. Deep learning algorithms were trained using the training data and then performance has been evaluated on the test data of the dataset of reddit which was published for the pilot piece of work, Early Detection of Depression in CLEF eRisk 2017. In particular, Bidirectional Long Short Term Memory (BiLSTM) with different word embedding techniques and metadata features were proposed which gave good results.
User Attitudes On Direct-to-Consumer Genetic Testing

Advances in biotechnology now allow users to obtain their genetic information, including ancestry and predisposition to various diseases and health issues, with relative ease. With these new commercial services come a host of privacy concerns with respect to data sharing and access. User data is being sold to third parties, including pharmaceutical and biotechnology companies, and may be accessed by law enforcement in accordance with proper legal procedures. Moreover, many users of these services go on to deposit the data they obtain into online, public repositories that are fully accessible to anyone with an internet connection. The full extent of the risks they face may not be apparent to users. This paper reports on a semistructured interview study ($n=24$) examining user concerns regarding these tests, what information they believe they are revealing, and what they think companies are doing with their data. We find that users are concerned with privacy, and understand at a basic level the nature of the data they are revealing. However, their privacy concerns are often insufficient to deter them from taking such a test, and many have difficulty grasping some of the implications of sharing their genetic information with commercial entities.
Generation of STDP With Non-Volatile Tunnel-FET Memory for Large-Scale and Low-Power Spiking Neural Networks

Spiking neural networks (SNNs) have attracted considerable attention as next-generation neural networks. As SNNs consist of devices that have spike-timing-dependent plasticity (STDP) characteristics, STDP is one of the critical characteristics we need to consider to implement an SNN. In this study, we generated the STDP of a biological synapse with non-volatile tunnel-field-effect-transistor (tunnel FET) memory that has a charge-storage layer and a tunnel FET structure. Tunnel FET is a promising structure to reduce the operation voltage owing to its steep sub-threshold slope. Therefore, the non-volatile tunnel-FET memory we propose enables the implementation of low-operation-voltage SNNs. This article reports the ${I-V}$ , programming, and both symmetric and asymmetric STDP characteristics of a non-volatile tunnel-FET memory with p-channel-MOS-like operation.
A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

The work reported in this article explores a novel Particle Swarm Optimization (PSO) tuned Support Vector Regression (SVR) based technique to develop the small-signal behavioral model for GaN High Electron Mobility Transistor (HEMT). The proposed technique investigates issues such as kernel selection and model optimization usually encountered in the application of SVR to model the GaN based HEMT devices. Here, the PSO algorithm is utilized to find the optimal hyperparameters to minimize the fitness function. To enumerate the efficiency and the generalization capability of the predictors, the performance of the model is investigated in terms of mean square error (MSE) and mean relative error (MRE). A very good agreement is found between the measured S-parameters and the proposed model for multi-biasing sets over the complete frequency range of 1GHz-18GHz. The proposed technique is even used to test the frequency extrapolation capability of the model. A comparative analysis indicates that the proposed PSO-SVR predictor achieves significantly improved computational efficiency and the overall prediction accuracy. To demonstrate the ready usefulness of the modeling approach, the developed model has been incorporated in CAD environment using MATLAB Cosimulation in ADS Ptolemy. Subsequently, the small-signal stability analysis is performed and gain of a power amplifier configuration designed using the proposed GaN HEMT model is determined.
Transfer Learning based Tomato Leaf Disease Detection for mobile applications

An estimated 15-25% of potential crop production in India is lost to pests, diseases and weeds. Advanced technologies for the early detection of crop diseases are required to achieve food security. Convolutional Neural Networks have found large success in vision problems such as classification and object detection. They have been used extensively in a plethora of fields in the recent years including robotics, healthcare and agriculture. However such deep learning approaches are computationally expensive and have large memory and power requirements. The paper aims to use transfer learning to obtain effective results for use on mobile devices at reduced costs using pre-trained EfficientNetB0, MobileNetV2 and VGG 19 models as feature extractors. 15,000 images from 9 types of diseases and one healthy class from the tomato plant are used to show the potential use of such approaches in agricultural applications.
Design and Testing of a Broadband Microstrip-SIW Transition

In this paper, a new broadband transition between microstrip and substrate integrated waveguide (SIW) by using three quarter-wave stepped transformer (QWST) is proposed. In order to achieve a good reflection coefficient, better in-phase response monitoring, low loss, wide bandwidth, and acceptable insertion loss, three SQW transformers are implemented in this proposed structure. The designed structures are simulated and optimized by ANSOFT HFSS ver.19 and cross-verified by an Advanced designed system (ADS). The experimental results are sufficiently close to the simulation results over the entire frequency band (4-8 GHz). Within the considered frequency range, the parameters’ optimum values are achieved as follows: return loss (RL) 18.23 dB, insertion loss (IL) below 0.76 dB, and overall loss variation of 15-20% in C-band frequency range. This transition is suitably used for C-band applications such as antenna, filter, amplifier, and other microwave devices.
Virtualized Software Development Infrastructure for Classrooms

Software development infrastructure allows communication between developers and it consists of a wide range of interconnected application specific tools which react events and communicate with themselves. These tools and their automation capabilities realize an intelligent system for software engineers which may be overlooked. Therefore, this infrastructure can be considered as a cognitive info-communication system. Teaching good communication practices and efficient usage of these tools is exceptionally important in higher education. Hence, software engineering capstone courses are essential part of education which are usually focused on methodologies, programming, communication or project management. Infrastructure is often assumed to be already established. On the other hand, development infrastructure is vital for many projects, evolve with time and contribute to the long term goals of software companies. Deployment of an efficient development infrastructure requires the integration of wide range of systems so it may not be available for recently started projects. Virtualization and cloud providers facilitates the deployment of complex software systems. This paper presents a virtual software development infrastructure that can be used in a classroom environment for mimic real development infrastructures.
Decentralized Control for Grid-interactive Hybrid dc/ac Ring Microgrid under Input Source Fluctuations

Photovoltaic distributed energy resources (DERs) are extensively increasing because of secure, abundant, broadly available, and essentially exhaustible resources as a future grid energy supply. The paper focuses on decentralized control of hybrid DC/AC ring-type microgrid feeding to the primary grid and load have to use PQ control and an enthusiastic synchronization to obtain the reference voltage of microgrid. Two PV arrays incorporating the maximum power point tracking (MPPT) and battery storage system in a DC ring bus are considered for the grid-interactive mode of operation. LCL filters are employed at the AC ring bus side to reject high-frequency harmonics created during switching of voltage source inverter (VSI) and DERs interfacing converters. Constant dc ring voltage control, reactive power control, maximum power control, and active power control are applied in DC/AC ring bus to regulate variation of power and voltage due to the input source fluctuations. The proposed system model developed in Matlab/Simulink SimPowerSystem and system performance simulation results are obtained.
State of Charge Estimation of Lithium-Ion Battery Using State Augmented Cubature Kalman Filter in Presence of Uncharacterized Coloured Noise

This paper addresses the State of Charge (SOC) estimation problem for Li-ion battery with the measurement perturbed with uncharacterised colour noise. Accurate estimation of the state of charge is a crucial task for the Battery Management System (BMS) to monitor the health of the battery. Often, SOC is estimated by Bayesian estimators using noisy measurements with the assumption of Gaussian white noise. For this assumption, the estimation results would degrade substantially in the presence of colour noise in the measurement and the situation worsens when the auto correlation coefficient of the noise remains unknown. This paper presents a solution method based on Cubature Kalman filter (CKF) with the noise augmented state vector which demonstrates superior estimation performance over the standard CKF. The augmented CKF is also validated in simulation using the current signals from realistic cell tests consisting of urban dynamometer driving schedule (UDDS) cycles. The results demonstrate the suitability of the proposed state augmented CKF where the measurement is perturbed with uncharacterised colour noise.
Encoded Chaos Shift Keying Communication System

The concept of chaos-based communication system, employing chaos shift keying and encoding feature – encoded chaos shift keying (ECSK) - is proposed in this article. Moreover, offered systems performance has been investigated via simulation. As communication systems develop rapidly, more secure ways of information transmission are needed. By using a chaos generator in the transmitter and receiver circuits it is possible to encrypt or encode data series into the chaotic signal. Two identical chaos generators employed in the current study, are set up in a master-to-slave configuration transmitting signals in a noisy environment. The security aspect of the proposed system is enhanced by encoding the input bit sequence by a binarized chaotic signal. The bit error rate performance evaluation in equivalent of the baseband transmission line with AWGN is made to characterize the proposed system. In the current study, LTspice software for baseband chaotic generator simulation is used, while MATLAB is employed for the analysis of results.
Optimizing GPU Memory Transactions for Convolution Operations

Convolution computation is a common operation in deep neural networks (DNNs) and is often responsible for performance bottlenecks during training and inferencing. Existing approaches for accelerating convolution operations aim to reduce computational complexity. However, these strategies often increase the memory footprint with extra memory accesses, thereby leaving much room for performance improvement. This paper presents a novel approach to optimize memory access for convolution operations, specifically targeting GPU execution. Our approach leverages two optimization techniques to reduce the number of memory operations for convolution operations performed on the width and height dimensions. For convolution computations on the width dimension, we exploit shuffle instructions to exchange the overlapped columns of the input for reducing the number of memory transactions. For convolution operations on the height dimension, we multiply each overlapped row of the input with multiple rows of a filter to compute multiple output elements to improve the data locality of row elements. We apply our approach to 2D and multi-channel 2D convolutions on an NVIDIA 2080Ti GPU. For 2D convolution, our approach delivers over faster performance than the state-of-the-art image processing libraries. For multi-channel 2D convolutions, we obtain up to speedups over the quickest algorithm of cuDNN. We apply our approach to 2D and multi-channel 2D convolutions on an NVIDIA 2080Ti GPU. For 2D convolution, our approach delivers over $\mathbf{2}\times$ faster performance than the state-of-the-art image processing libraries. For multi-channel 2D convolutions, we obtain up to $\mathbf{1.3}\times$ speedups over the quickest algorithm of cuDNN.
Modelization and Characterization of a CMOS Camera as an Optical Real-Time Oscilloscope

Complementary Metal-Oxide Semiconductor (CMOS) camera sensors are embedded in many consumer electronics products: thanks to the Rolling Shutter (RS) readout mode, they can detect a time-varying light intensity, which is the key to realize Optical Camera Communication (OCC). To this aim, we introduce here a model describing the camera as a Real-Time Oscilloscope (RTO) detecting optical signals; by means of this approach, we can now characterize the Complementary Metal-Oxide Semiconductor (CMOS) camera by means of parameters that correspond to common oscilloscope specifications, such as the frequency response, the noise, the Signal to Noise Ratio (SNR), the total harmonic distortion (THD), etc.; all of these are introduced and measured in terms of the camera parameters. This approach provides for the first time a set of quantitative tools that should be used to maximize the OCC transmission performance by allowing the optimal selection of the camera settings.
Parking Management by Means of Computer Vision

Car park management currently relies on parking attendants to show where empty parking lots are by monitoring all available parking areas. In some shopping centers sometimes do not have information about parking conditions, whether parking is full or empty. This condition causes the driver to have difficulty finding a parking space. When drivers are looking for a parking space, many cars are milling about causing congestion and wasting time. This paper presents a breakthrough to make it easier for drivers to find an empty parking space with builds a car parking monitoring and management system based on computer vision. The system input is a camera that is installed in the center of the parking area. The HAAR Cascade Classifier method detects and counts the cars that are parked and then compared with the available parking slots using. If the number of cars currently parked is less than the slots provided, the parking area will inform you that it is still empty along with the number of available slots. Detection of available parking spaces is based on coordinates that have been determined manually on the camera. A total of five slot areas are determined by this camera. The result of this research shows the system accuracy is 90% for the available car parking.
The complete mitochondrial genome of spearnose grenadier, Coelorinchus multispinulosus, Katayama, 1942 (Gadiformes: Macrouridae)

Abstract The complete mitochondrial genome of Coelorinchus multispinulosus was determined using the MiSeq platform. The mitogenome of C. multispinulosus (15,937 bp) contained 13 protein-coding genes, 22 tRNAs, two ribosomal RNAs (12S and 16S), and a control region (D-Loop). The G + C content of its mitogenome was 44.69%, which was lower than A + T contents (55.31%). Unusual start codon (GTG) was identified in three protein-coding genes including COX1, ND2, and ND3. Incomplete stop codons (TA–/T–) were shown in six genes including COX1, COX2, COX3, ND3, ND4, and ATP6. Based on the phylogenetic tree with the currently reported mitogenomes in the family Macrouridae, C. multispinulosus was most closely related to Coelorinchus kishinouyei with 85% nucleotide sequence identity.
Ant Colony Algorithm in Selection Suitable Plant for Urban Farming

Urban Farming is the right solution where this agricultural method is an agricultural method that can take advantage of the narrow open land for farming purposes. Choosing a suitable crop type for a city can give better results. This research by observing the temperature of 5 (five) big cities. The purpose of this study is to select suitable plants for a city according to the temperature of each city. The Ant Colony Optimization (ACO) algorithm is inspired by the observation of an ant colony. Ants are animals that live as a unit in their colony as opposed to being seen as individuals who live independently of the colony. The results of this study provide selection of plants suitable for cultivation in each city with an the level compatibility of 68 percent.
Modal Vertical Vehicle Dynamics Control for Semi-Active and Active Suspension Systems

The shift towards automated driving will cause ride comfort to become even more important for premium class vehicles in the future. This research involves the design, implementation, analysis, and testing of a new vertical dynamics controller for vehicles, combining sky-hook control and lotus modal control. The objectives of the controller are to achieve simplicity for easy tuning during the development, while pro-viding the best possible ride comfort and road-holding. Modal analysis of the suspension system, as well as virtual and real vehicle testing of the new controller are conducted in the scope of this research.
Investigation on short channel GGNMOS ESD protection device for low power IC

In this paper, the variation of trigger and hold voltages of electrostatic discharge (ESD) snapback of a 20 nm grounded-gate NMOS (GGNMOS) are analyzed by changing different parameters such as the p-well doping, gate workfunction, drain to body contact distance and external resistance added to the body contact using TCAD simulation. It was found that decreasing the body doping concentration of a 20 nm GGNMOS decreases the trigger and hold voltages. Increasing the gate work-function increases the trigger and hold voltages. The body contact with respect to drain was moved closer, as a result, the trigger and hold voltages were increased. External resistance was added to the body contact terminal and with increased resistances, the trigger and hold voltages decreased. The results inevitably show routes toward optimization and designing of a low voltage ESD protection circuit for aggressively scaled technology node.
Reliability Evaluation of Renewable-Rich Microgrids Using Monte Carlo Simulation Considering Resource and Equipment Availability

This paper proposes a new reliability evaluation method for renewable energy (RE) rich microgrid (MG) systems using a holistic approach by modelling the availability of renewable resources such as solar irradiation and wind along with the equipment availability during each hour. Where an electric network relies heavily on renewable resources the availability of such resources will significantly affect the reliability of the whole system. In addition, energy storage systems (ESSs) impact the reliability of a microgrid. In this paper, the mean time to failure (MTTF) of a RE rich MG system is evaluated based on the MTTF of equipment for different levels of renewable energy generation and energy storage. The loss of load frequency (LOLF) in each hour due to equipment failure, due to resource unavailbalitly, and the system failure due to combined effects were counted in a simulation study of 25 years.
Automated Detection of Cognitive Load from Peripheral Physiological Signals based on Hjorth’s Parameters

The prolonged exposure to high levels of cognitive effort causes fatigue and stress-related decrease of attention and concentration, which are known to compromise work efficiency, safety, and health. In the present study, we investigate the applicability of the Hjorth parameters, namely Activity, Mobility, and Complexity, computed from peripheral physiological signals, as features on the automated cognitive load detection task. Specifically, here we consider the detection of high cognitive load in a person-independent scenario based on galvanic skin response (GSR) and photoplethysmographic (PPG) signals. To assess the practical worth of Hjorth’s parameters, we carried out a comparative evaluation in a common experimental protocol based on a subset of the CLAS dataset, which contains GSR and PPG recordings of 60 people while they were engaged in problem-solving tasks, such as Math-task and IQ-task. The discriminative capability of the Hjorth parameters was evaluated with four classification methods when Activity, Mobility, and Complexity are used individually and in combination. We report detection accuracy of up to 84.7% and 80.5% on the Math-task and IQ-task, respectively.
Natural Genetics Adapted Control for an Autonomous Wind-Battery Based Microgrid

Autonomous microgrids supply power to large remote areas where access to the grid is infeasible. The generation of these microgrids is highly dominated by renewable energy resources (RESs) equipped with a storage battery. Due to the uncertainty associated with RESs, the sustainability and reliability of supply become the prime areas of focus. The battery reduces the uncertainty during demand rise, power deficiency, and overloading with their faster response than diesel generators and microturbines. In this article, the power generation of the isolated microgrid is considered from the wind energy source along with a battery. For the favorable speed regulation of the wind turbine driven generator, the traditional proportional integral (PI) controller is replaced by natural genetics [genetic algorithm (GA)] and evolution inspired PI controller. The issues identified by the use of a fixed gain PI controller such as unsuitable regulation due to incorrect tuning and discontinuous tuning are addressed by the use of a GA tuned speed controller. The performance of the PI controller degrades while dealing with the ac quantities, so in terms of dealing with sinusoidal signals, here an adaptive proportional resonant controller with a combined generalized integrator based phase locked loop is utilized. It tracks the reference ac voltage and provides frequency adaptation in the presence of harmonics pollution with zero steady state error. The batteries deployed through a bidirectional converter, act as the residential storage for backing up the power firming renewable production. The maximum power point (MPP) extraction of the wind generator is carried by a perturb and observe based MPP technique. The performance improvement obtained from the laboratory developed test setup verifies the microgrid effectiveness under renewable generation variability and load alterations.