Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study on Different Measures to Identify Driver States

Road accidents are major issue that creates problem in the society. To overcome the issue, driver behavior and the driving environment has to be monitored regularly and alert the driver before any accident may occur. This could be possible only on analyzing four measures: subjective, behavioral, physiological and vehicle-based. These combined hybrid measures can provide better accuracy on determining various states of driver like normal, drowsy, cognitive and visual inattention. In hybrid measures, physiological measure plays a major role by increasing the accuracy, specificity, sensitivity of the driver state detection. Physiological measures are the signals recorded from various parts of the human body using biosensors to check the internal condition of the driver while driving. These signals can be Electroencephalogram (EEG), Electrocardiogram (ECG), Electro-oculogram (EOG) and Electromyogram (EMG). EEG signals are the brain activity data which corresponds to EEG wave band determines the physical state of the driver. Most researchers have worked on EEG for better accuracy in driver state detection. This paper is one such review on various measures in determining driver states while driving.
Analysis on Impact of Baihetan Inverter Connection Modes on Stability of Zhejiang Power Grid

The Baihetan HVDC system is under construction, and the Zhejiang power grid is at the receiving end. There are three different inverter connection modes for consideration, including the LCC inverter mode, the MMC inverter mode with the constant AC bus voltage control and the MMC inverter mode with the constant reactive power control. Firstly, four indexes are proposed to provide reference for the mode selection. Then, the stability calculation is carried out under the three connection modes respectively. Finally, the impact of the Baihetan inverter connection modes on the stability of the Zhejiang power grid is analyzed and summarized. The results show that the MMC inverter mode with the constant reactive power control will deteriorate the stability of the Zhejiang power grid compared with the LCC inverter mode, while MMC inverter mode with the constant AC bus voltage control will improve the stability of the Zhejiang power grid.
Position Paper: Prototyping Autonomous Vehicles Applications with Heterogeneous Multi-FpgaSystems

One important feature required by autonomous vehicles is the ability to perform a localization task in order to navigate in both known (urban, suburban, and highways) and unknown environments. Instead of relying on LIDAR technology, we propose to leverage a bio-inspired algorithm relying on more conventional cameras and a large neural network (NN) [2]. Yet, this approach must be able to scale. We propose to investigate the development of an FPGA-based solution. Due to the size of NN, dynamic partial reconfiguration will be required, and an efficient (software-based) scheduler must place the hardware tasks on multiple FPGA chip. We intend to implement this algorithm using a unique custom board, Wizarde, which embeds a 3 × 3 matrix of Zynq SoCs with high-end FPGAs to prototype a possible solution.
Equivalence between Kim Method and Omi Method in Single-Cut Near-to-Far-Field Transformations for Antenna Near-Field Measurements

Two promising methods (Kim method and Omi method) in the single-cut near-to-far-field transformations are shown to be equivalent. The derivations and forms of the two methods are quite different: the Kim method is derived from a cylindrical near-field transmission equation with some approximations and the Omi method is derived from an approximation of the free-space Green function and using the plane wave expansions in the 2-D fast multipole method. Whereas the appearance of the equations of both methods are quite different, both methods are proved to be equivalent mathematically.
Simulation Calculation of the Magnetic Torque of Dual Mode Permanent Magnet Transmission Based on Magnetic Circuit Analysis

A method of cutting permanent magnets with circular arcs of offset center was proposed on the basis of the principle of similarity under the condition of slip drive to obtain a permanent magnet transmission scheme for low permanent magnet torque fluctuations. The magnetic flux distribution of each region in the magnetic circuit was altered by changing the shape of the permanent magnet. Different magnetic torque curves were obtained by selecting silicon steel sheet with different thicknesses. The relationship between the magnetic flux distribution and the relative rotation angle in the silicon steel sheet was further analyzed, and the direct cause of the influence of the thickness of the silicon steel sheet on the magnetic torque was obtained. The parameters of different eccentric arcs were selected for simulation calculation, and a smooth magnetic torque curve was obtained after fixing the thickness of the silicon steel sheet. On the basis of the results of the former two, the parameters of the eccentric arc were obtained by the magnetic torque value volatility using an approximation method, and the magnetic torque value was measured by making the physical object. According to the results of the simulation and experiment, the permanent magnets with eccentric arcs can be used to obtain the smooth magnetic torque in the range of a 0°−90° relative rotation angle, which has a certain application value.
Modeling, Simulation and Performance Analysis of Solar PV Integrated with Reverse Osmosis Water Treatment Unit for Agriculture Farming

A design model for sustainable and clean power system integrated with reverse osmosis water treatment/desalination unit to meet the challenges of climate change, water shortage and energy while producing organic and healthy food is presented. The main objectives of the optimized design of renewable energy system are: (1) decrease the CO2 emissions, (2) reduce the energy consumption from the grid by adding more renewable resources such as solar, and (3) fresh water production for irrigation using underground or sea water reverse osmosis water treatment/desalination system. The goal is to integrate sustainable power and water systems to meet the challenges of climate change and water shortages especially in desert regions. Modeling and simulation for the optimized design of renewable energy system for the agriculture farm was used in this study. The results show that the grid-tied solar PV energy system has the best performance compared to the grid and standalone renewable power system. The solar energy system connected to the grid is a good option for the agriculture farm with respect to the cost (85$/MWh), renewable fraction (67%), and greenhouse gas emissions (208 kg of carbon dioxide per MWh of energy produced).
Applying m-Mutation Operator in Genetic Algorithm to Solve Permutation Problems

Routing problems, scheduling problems, transportation scheduling problems are interesting problems and variants of Permutation problems. The intention of solving these problems is to find a better path (solution) among enormous, feasible, available solutions. The better (or best) solution should provide a cost effective path which would enable a anyone (or device) to travel to all the given cities (location) one and only once and finally return to the starting city(location). Approaches like Brute Force would not work since it is not feasible to calculate cost for all the possible paths (because rise in the number of cities would exponentially increase the number of all possible permutation of cities). Approaches like heuristics can be trusted reasonably as it uses reduced amount of computing power. Heuristic techniques are used because it gives quick and better solution even if it does not guarantee the best solution. In this research work, the authors propose a mutation operator called (m - mutation) that could be applied in genetic algorithm to solve permutation problems. The efficiency of the proposed mutation operator is compared with the efficiency of existing mutation operators in solving the same permutation problem and the results are encouraging.
Influence of User Mobility on the Accuracy of Indoor Positioning with the use of RSSI and Particle Filter Algorithm

The paper discusses factors affecting the performance of Bluetooth Low Energy (BLE) based indoor positioning system, received signal strength indicator (RSSI) and a particle filter algorithm for the estimation of the terminal position. The original results of our experimental analysis show the impact of the user mobility on the positioning accuracy for different resolutions of the received power reference maps.
Deep Learning for Organic Clutter Detection in Radar Cross Section Data

To date, radar cross section (RCS) measurements at outdoor ranges require manual inspection by test engineers to check for contamination from natural causes. This is an expensive, time-consuming operation that acts as a bottleneck for collecting RCS measurements at these ranges. Advancements in deep learning (DL) have allowed automation of similar types of manual labor. Unfortunately, such techniques normally require enormous training datasets which are impractical to collect at outdoor RCS ranges. We present a novel deep learning method for detecting organic contamination in RCS data that is robust to differing target geometries and performs well even with as little as one contamination measurement for training. Our test case also demonstrates that this method is even capable of outperforming manual human inspection, by uncovering difficult to find mistakes in the original ground truthing.
MultiNet: Efficient Utilization of Multiple Networks

Divide and Conquer and its application-Multiprocessing, is a basic aspect of today’s world to make process easy, not just in programming but in real life as well. When the upper limit of an individual start reaching, sharing resources and dividing problem is the basic approach. Networking is one such field in which dividing of data is done using packets so as to make the delivery easier and losses minimum. But is that the limit, or can we increase our productivity even more? There are many cases when a user has to wait to browse internet or check emails when a large downloading is being done and siphoning network would either not be possible or would do harm to current progress. In this situation many have wished if they could somehow be able to use their backup network, like a mobile hotspot to either increase the download speed so that if finishes faster, or to utilize that another network without disrupting the original one. Our paper researches if it is indeed possible and optimum to utilize multiple networks and to do it efficiently and how much net gain we can get out if this is done. To show this, we have created a basic utility tool called MultiNet that will let us utilize multiple networks and show the achieved efficiency.
Multiple Access Channels With Full-Duplex Amplify-and-Forward Transmitter Cooperations

In this paper, a full-duplex (FD) transmitter cooperative scheme is proposed to improve the achievable rate for multiple access channels (MACs). In the proposed scheme, by leveraging the FD amplify-and-forward (AF) protocol, two FD transmitters send their own signals and forward the received signals from their counterparts to the receiver, which adopts both the forward and backward decoding schemes to decode the source messages. First, the analyses of the equivalent channel model and the statistics of the accumulated additive white Gaussian noise and residual self-interference (ANRI) caused by imperfect self-interference cancellation are performed. Then, the achievable rate regions are obtained by utilizing both the forward and backward decoding schemes, and a two-stage iterative algorithm is proposed to characterize these regions: in each iteration, the covariance matrix of the ANRI is first updated; then, for the fixed covariance matrix of the ANRI, the transmission parameters of the two transmitters are calculated by a two-step iterative algorithm based on the parametric Dinkelbach algorithm, sequential convex programming (SCP), and semidefinite relaxation (SDR). The numerical results show that the proposed scheme outperforms the half-duplex cooperative scheme and conventional MAC scheme without cooperations when the channels between the two transmitters are in good condition.
Detection of Inter-turn Short Circuits in Power Transformers by No-load Current Analysis

Power transformers are one of the most important components in electrical systems. Failures in this type of machines can cause serious consequences. Traditionally, to ensure proper functioning and to detect possible faults at the incipient state, off-line diagnostic tools have been used. In the last years, and in accordance with strategies applied in other electrical machines, alternative techniques for faults detection in power transformers have begun to be studied. The aim of this new trend is to move towards on-line monitoring.In this work, a proposal for inter-turn short circuits detection in transformer windings is presented. The proposal is based on the analysis of the no-load current harmonic content and it is carried out on an experimental prototype. The results allow affirming that the proposed method is effective even for slight faults.
CashNet-15:An Optimized Cashew Nut Grading Using Deep CNN and Data Augmentation

Since there is a great demand for the quality of agricultural products in the global market. It is very important to improve the quality and standards of agricultural products to competent in the business world. Furthermore cashew is a significant produce in India as well as it takes the major part in the global export market for cashew nut. But the most of the methods proposed for grading system is wouldn’t reach the better accuracy. Hence to improve the performance, we proposed the optimized cashew nut grading using Deep CNN and Data augmentation. This CashNet-15 work consists of totally 15 layers of CNN. Here we used 8 convolution layer and 4 Max-poolong layer for feature extraction and remaining are 1 fully connected layer, 1activation function and 1dropout layer. To attain the better performance we used data augmentation methods. To optimize the network, hyperparameter like SGD with Beta momentum and Leaky rectified linear unit was used to reduce the loss function and to obtain the non-linear property.
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.
Acoustic Tracking of Pitch, Modal, and Subharmonic Vibrations of Vocal Folds in Parkinson’s Disease and Parkinsonism

The prominent and early presence of dysphonia is considered a valuable marker for differentiation of idiopathic Parkinson’s disease and parkinsonian syndromes. Objective quantification of vibrational regimes represented by modal and subharmonic vibrations may thus be vital for improving accuracy of diagnostic decision. The rationale for analyzing vibrational regimes is that abnormal subharmonic vibrations might be the key factor causing dysphonia in parkinsonian syndromes. This study introduces a new fully automated methodology based on robust pitch tracker for decoupling vibrations controlled by laryngeal muscles from the effect of subharmonics that provides distinguishing features of Parkinson’s disease and atypical parkinsonian syndromes. We tested the method on resynthesized signals with known parameters and demonstrated that vibrations controlled by laryngeal muscles as well as subharmonics can be detected reliably with a precision that outperforms available technologies. We analysed 337 sustained vowels of 22 patients with PD, 21 patients with multiple system atrophy, 18 patients with progressive supranuclear palsy and 22 healthy controls. Our results showed that subharmonics are more prominent in atypical parkinsonian syndromes compared to Parkinson’s disease. Also, increased modulation by laryngeal muscles appears to be a distinctive symptom of multiple system atrophy. Developed algorithm and proposed resynthesized voice signals provide further critical step to understanding and evaluation of dysphonia in Parkinsonism.
Measurement of Health, Human Capital, and Economic Welfare at The Poverty Level Influences using Heuristic Network Approach

Poverty alleviation is still a major problem for many international and developing countries. In a weak economic situation, low-income households are very vulnerable to poverty. Therefore, the study of poverty is very important for various economic development plans. Analysis of various social aspects can conclude that poverty can be a function of the diminished ability of a person to live the type of life they choose. This study chooses aspects of health, human capital, and economic well-being as latent variables which are assumed to influence poverty. The relationship between variables modeled by the structure equation through the heuristic network. The purpose of this study is to evaluate the effect of all latent variables (also called independent variables) on poverty. This evaluation is carried out on all the final coefficients of the trained network. The results of the study have shown that the heuristic network significant enough to be used as a representation of the structural equation model. It has proven by MAPE of small enough. The results of this study also have shown that all independent variables did not interdependent. Indicator variables of health did not give any influence on poverty. This concluded that only indicator variables of human capital and economic well-being influence poverty. The results of this study have also shown that only food consumption (as one of indicator variable of economic well-being) which have a negative influence on poverty.
Price Setting of a Microgrid Operator in a Radial Distribution Network

The proliferation of microgrids is rapidly growing into an existing grid due to its advantages like reducing the stress on the main grid and increase the competition in the electricity market. In such a competitive scenario, gaining maximum benefit is essential for both the microgrid aggregator and consumer. To address that paper presents a mathematical framework to schedule the local generating resources like a diesel generator, battery energy storage system (BESS) and price setting to a consumer. The objectives of the aggregator and consumers are complementary to each other. To satisfy both the players an equilibrium problem has formulated. In general, it can be solved by using bi-level programming; this work presents a single-stage framework to meet both the aggregator and consumer requirements without violating the network constraints. And the effect of demand response and uncertainty of random variables like day-ahead electricity price, solar PV, wind power, load demand on the offer price is also studied. The developed framework formulated as a mixed integer non-linear program and test over a 33-bus active radial distribution network.
Segmentation of Organs at Risk in Chest Cavity Using 3D Deep Neural Network

Cancer ranks second in the world in terms of morbidity and mortality. Radiotherapy is one of standard treatment procedures in case of cancer. In lung and esophageal cancer, the irradiation planning begins with the delineation of the target tumor and healthy organs. In this paper we propose solving Organs at Risk (OAR) segmentation task with deep learning approach. Proposed network is based on U-Net-like architecture with ResNet-50 backbone. Multi-resolution approach is used for localization of volumes of interest (VOIs) for each individual organ at inference stage. Generalized Dice Loss is used for neural network weights optimization. Experiments demonstrate competitive performance on a dataset of 60 computed tomography (CT) scans (40 CT scans are used for network training, 20 CT scans - for evaluation). The following metric scores were obtained on the test split: dice score - 0.77 (esophagus), 0.933 (heart), 0.913 (trachea), 0.908 (aorta); Hausdorff distance - 0.605 (esophagus), 0.225 (heart), 0.223 (trachea), 0.326 (aorta).
Analysis of Drawbacks in 802.X and Significant Methodologies on Data Recovery

Bluetooth, a short range communication standard, helps in sharing information between paired devices with high security, using Frequency Hopping Spread Spectrum (FHSS) technique. When a file is being transmitted, the link between the devices undergo any distraction, and if the link is broken, even after transmitting 70% more amount of data, there will 0% in the receiver device, or it cannot be opened as its not completely received. The proposed idea is to develop an effective communication that will help an individual to retrieve the data, first, by moving the receiving content to permanent memory soon after every N bits received in buffer. Secondly by fixing a timeslot after which the content will be moved to permanent memory. With the amount of information received, using bilinear interpolation technique, the left out data (if it is below 30%) is predicted to complete the process.
A Lead Selection Strategy based on an Estimated Detection Quality Index

In this work, we present and evaluate an algorithm that builds a series of heartbeat locations from the outputs of publicly available QRS detectors in order to improve accuracy in QRS detection. For each QRS detector and lead, a set of QRS locations is obtained, and all these sets are ranked according to a quality metric based on their predicted accuracy with a model based on RR interval series features and heartbeat detection co-occurence across leads. Then, for each consecutive 20-s lenght window, the highest ranked set of QRS locations is selected. The performance of this algorithm as well as that of several other lead selection strategies (LSS) was evaluated in a validation set including 14 ECG databases (12 of the public) and 1772 recordings. LSS studied in this work have been made available in an open source toolbox to ease the reproducibility and comparison of results. The performance was calculated in terms of the average sensitivity, positive predictive value and the F score. The best performing LSS achieved an F score of 99.6, with sensitivity S = 99.9 and positive predictive value P = 99.5, weighting equally all recordings. The results suggest the convenience of the strategies as the one proposed for selecting the best heartbeat locations, performing better than any of the public algorithms evaluated by itself.