Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Sign Recognition with Binarized Multi-Scale Neural Networks

Computer vision has grown tremendously thanks to advances in convolutional neural networks (CNNs), the field of traffic sign recognition and intelligent transportation performs better because of that. However, as traffic sign recognition systems are often deployed in computing and storage constrained environments, it is critical for the network design to be accurate and real-time in these instances. Whether it is training or running an ordinary neural networks framework, it requires one or many high-power Graphic Processing Units (GPUs) and considerable memory resources. To solve this problem, this project creatively propose a 2-stage binarized multi-scale neural network framework, called B-MNN, extends Pierre Sermanet and Yann LeCun's Convolutional Networks. When training a B-MNN, we intentionally restrict the weights and activations of the model to +1 or −1. The mathematical calculation (addition, subtraction, multiplication and division) of the binarization network can be implemented using bit-wise bit operations, which is convenient for hardware to implement the algorithm, faster and more energy efficient. B-MNN approaches state-of-the-art results on the GTSRB traffic sign dataset implemented with the Keras artificial neural network library. We achieved 91.34% accuracy. Although it is slightly less accurate than models based on CNNs, the same ordinary neural network structure is an order of magnitude larger than ours. It is an important direction to realize the neural network into the realistic scene in the future.
Low Complexity Modeling of Cross-Spectral Matrix and Its Application in the Non-Synchronous Measurements of Microphones Array

The resolution related with the image quality of acoustic imaging using a microphone array is limited by the size and density of the array. However, non-synchronous measurements can exceed the constraints defined by measurements with a single fixed array. The non-synchronous measurements can achieve a larger and denser aperture array by moving the single array. The key problem of archiving the non-synchronous measurements of a microphone array is the matrix completion of a block diagonal spectral matrix. A comprehensive investigation of the block diagonal spectral matrix completion is still missing in the microphone array signal processing literature since it is usually considered as a difficult problem if without the integrated physical information of the sound field. In this paper, the mathematical relation between the max-norm and the nuclear-norm is pointed out, and the max-norm minimization of the spectral matrix is discussed, which is an effective model with integrated spatial representation of the sound field for the spectral matrix completion. First, the max-norm is utilized for the low complexity modeling and the corresponding Proximal-Point method for Max-norm based Spectral matrix completion (PPMSMC) is proposed; Second, the rank, nuclear-norm and max-norm have been investigated as complexity measures in the context of spectral matrix completion, and their performances are compared. The proposed low-rank methods have constructed a concrete theorem foundation for the non-synchronous measurements of the microphone array.
A Review on SMART GRID Power System Network

This paper briefly discusses about the new revolution in grid system i.e; ‘Smart Grid’. The evolution of smart grid development leads to energy autonomy, and sustainable economic growth. This technology will surely fulfil the dreams of Dr. A.P.J Abdul Kalam i.e; “Energy forever and energy for all”. Smart grid is a ‘system of system’ where it consists of three layers, (a) the power and energy level (b) the communication layer (c) the IT/computer layer. It provides the bidirectional flow of energy as well communication with consumers also. It also known as ‘Auto Healing System’. It brings the better energy utilisation with the great efficiency of grid system. It brings the overall integrity and security in the system with the concept of interconnected power system. It helps to estimate the real time electricity consumption with the help of smart meters, SCADA, intelligent controllers, photovoltaic materials, Nano materials, high current quantum wires etc. Here 9 research papers in the area of Smart Grid are reviewed and the validation table has been compiled.
Low-Complexity Robust Beamforming with Blockage Prediction for Millimeter-Wave Communications

We propose a low-complexity robust beamforming algorithm for millimeter-wave (mm Wave) Coordinated MultiPoint (CoMP) architectures subject to random path blockages due to moving objects such as human bodies and the low scattered nature of mm Wave channels. In order to tackle this difficulty without dealing with computationally-demanding possible combinations of channel statistics, which are considered to be a bottleneck of the existing state-of-the-art method for this issue, we introduce a weighted-sum formulation of the latter problem as well as the associated effective weight design, which is shown to be efficient from a complexity point-of-view in comparison with the other state-of-the-art alternatives. Aiming to clarify the above, complexity-order analyses in terms of floating point operations per second (flops) are also provided for both the proposed and state-of-the-art robust beamforming designs for mm Wave path blockages. Simulation results illustrate the superior performance of the proposed method to the state-of the-art in terms of the performance-complexity trade-off, also demonstrate the legitimacy of the complexity analyses derived in this paper.
Towards the Use of a Framework to Make Technical Debt Visible

Technical debt concept has been in use since the 90s’s decade. Several processes, techniques and tools, such as those related with software maintenance and risk control, are used to manage, prevent, measure and reduce technical debt. Technical debt management includes activities to identify, measure, prioritize, repay, and monitor it, but one of the main issues related with management resides in the complexity to make technical debt visible to organizations. In this paper is presented the application of the Normative Process Framework to make technical debt visible with a large system developed by students of software engineering. The Normative Process Framework is used in conjunction with a process to find technical debt and document it in a simple format. Results show how technical debt was made visible for that system in a simplified way, by using documentation generated during development, and considering not only code, but also other software assets. Once technical debt is made visible is easier to evaluate and prioritize it, to establish a convenient set of actions to control it.
Security Analysis in Correlated Nakagami-m Fading Channels with Diversity Combining

This paper investigates the effect of exponential antenna correlation on the secrecy performance of Nakagami-m fading correlated channels in the presence of an eavesdropper. Diversity combining techniques are imposed to reduce the effect of that exponential correlation. The closed-form analytical expressions (CFAEs) for the ergodic secrecy capacity (EGC), probability of non-zero secrecy capacity (PNZSC) and the secure outage probability(SOP) are derived to facilitate the investigation clearly. Our results show that the effect of exponential antenna correlation in Nakagami-m fading channel significantly affects the security. But the antenna diversity is the best solution to reduce that effect maintaining a tradeoff between the number of antennas and the intensity of correlation. Finally, the validity of analytical expressions are checked via Monte-Carlo simulations.
Sentiment and Emotion in Social Media COVID-19 Conversations: SAB-LSTM Approach

Sentiment and Emotion detection in social media conversations remains a challenge and analyzing the people emotion emerged as an important task in this unprecedented time of COVID-19. People sentiment and emotions are affected by lockdowns, social distancing, travel, work-from-home, wearing mask, reading social media posting. Most of them are feeling sad, anger, depressed, and some of them are neutral and happy. The most recent Sentiment Analysis (SA) researches are done using Twitter dataset (short-text) and rule-based (sentiment lexicon) approach, the outcome of these SA models' results is not showing the consistent prediction of people sentiment about COVID-19. To mitigate and overcome limitations of lexicon approach, processing unstructured social media long text posting, getting context based sentiment score, model overfitting, performance problems of sentiment models, authors' proposed and built a novel multi-class SA model using extension of Bidirectional LSTM (SAB-LSTM) with additional layers. In this experiment SAB-LSTM model has been used to process long text of social media posting, news articles text dataset. Experiment result showed, SAB-LSTM model performance is better than traditional LSTM and BLSTM. Compared SAB-LSTM performance metric of Precision, Recall, F1 Score and sentiment score with traditional LSTM and BLSTM. For this experiment collected COVID-19 related dataset from various social media sources such as Twitter, Facebook, YouTube, News articles blogs and collected data from friends and family.
Predictive Failure Analysis of Spindle Motor & Cutting Oil Condition Monitoring of Grinding Machine using Artificial Intelligence Models

With the advent of Industry 4.0, manufacturing industries are competing to adopt intelligent machining systems to deliver high performance in minimum possible time. Machine downtimes results in hefty production loss(s). To avoid them, we must identify their causes (spindle failure in our case) and predict their occurrences. We used vibration analysis methods to analyze wear-&-tear of spindle during running condition. Our objective is to design a low-cost smart maintenance model to predict the failure before its occurrence using Support Vector Machine model. Additionally, condition-monitoring of cutting fluid by analyzing variation in temperature of rotating spindle, pH due to Sulphur concentration has been included. Learning Vector Quantization algorithm is used for data analysis of sensors monitoring the health of cutting fluids and filter oil. Time Series Analysis has been performed on pH data to predict the pH of cutting fluid for prevention of cutting fluid deterioration. The model is tested for a good range of working conditions and results are found more promising than existing system.
Comparative analysis of the impacts of CNTFET and GNRFET drivers on the crosstalk effects in MLGNR interconnects at 7nm technology node

This paper presents a comparative evaluation of the performance of the multilayer graphene nanoribbon (MLGNR) lines with carbon-based drivers and receivers at 7nm technology node in presence of the crosstalk effects. Graphene nanoribbon field-effect transistors (GNRFET) and Carbon nanotube field-effect transistors (CNTFET) are used as driver and receiver inverters at the input and output of the MLGNR interconnects. For a more accurate assessment, the five-line transmission lines including two pairs of aggressor lines is considered. The results show that for the same Ioff and the same channel dimensions, CNTFET drivers show significant improvement regarding the crosstalk-induced effects over GNRFET. According to the results, using CNTFET drivers leads to 67%, 64%, and 34% lower crosstalk delay than the GNRFET-based for 100μm, 200μm and 500μm MLGNR interconnect length, respectively. Moreover, the CNTFET- based lines have 36%, 27%, and 16% smaller crosstalk noise area than their GNRFET counterparts for different lengths of MLGNR interconnects.
A New Type Method of Adhesive Handwritten Digit Recognition Based on Improved Faster RCNN

Aiming at the low recognition accuracy of the traditional machine learning algorithm which is susceptible to digital writing quality, inter-digital adhesion, random noise background and other factors in the process of adhesion handwritten digit recognition, an new method based on improved fast regional convolutional neural network(Faster RCNN) of adhesion handwritten digit recognition is proposed. Firstly, the NIST19 dataset is used as the basic dataset, and a mixed dataset is created by setting different hand-to-hand ratios with different degrees of overlap, and then randomly add salt and pepper noise and Gaussian noise in the experimental images. Secondly, aiming at the problem of a large number of overlapping objects in the handwritten digital images, a model based on improved Faster RCNN network is built and trained with the above data sets. Finally, the average accuracy of the model is evaluated. The experimental results show that the average detection accuracy of the proposed model is good. Compared with the original Faster RCNN and YOLO models, the improved model not only reduces the scale of parameters, but also ensures high recognition accuracy, and realizes the accurate and efficient recognition of handwritten adhesive digits.
Speed Control of Brushless DC motor using Fractional Order Fuzzy PI Controller Optimized via WOA

DC motor is one of the most straightforward and valuable equipment in industrial applications; hence its speed control is crucial and vital. PI controller is usually performed on speed control of BLDC motor due to its simplicity and popularity. PI controller's main drawback is its constant gain and cannot perform well in nonlinear and uncertain systems. Nonlinearity and uncertainty lie in the nature of BLDC. The fuzzy controller, as an intelligent controller, can handle this problem. In this study, a fractional-order fuzzy PI (FOFPI) controller is proposed to speed control of the BLDC motor. The controller parameters are optimized via Whale Optimization Algorithm (WOA). To check the control system's robustness, its performance has been investigated at different speeds. As the results show, the excellent account (less overshoot, less settling time, etc.) and the proposed controller's robustness are verified.
Helpfulness Prediction of Online Drug Reviews

Online drug reviews are increasingly available for drug users and medical professionals to collect first-hand patient experiences on drugs. The challenge is, however, to provide them with quick and accurate access to the helpful reviews mingled with an extensive amount of less helpful ones. One way to facilitate information access to helpful reviews is helpfulness prediction. By analyzing the features extracted from reviews, the helpfulness of a review can be objectively evaluated. However, the user sentiments and opinions contained in drug reviews, compared with the ones in other product reviews, are exceedingly complicated to be analyzed due to the differences in users’ physical and mental conditions. Hence, in this paper, we proposed a mechanism that combines medical-domain features (medical word sentiments, proportion of medical words in reviews) into a helpfulness prediction model. Besides, we noticed that the number of useful counts received by a review cannot sufficiently validate its helpfulness since useful counts are significantly influenced by the length of duration it was exposed to readers and total read times. Hence, to address this selection bias issue, we proposed four methods and examined their performances. Our findings show 1) the added medical features significantly elevated the accuracy of helpfulness prediction results. 2) readability, proportion of medical words and proportion of nouns are the three features showing the highest importance in helpfulness prediction. 3) the method to select sample based on useful counts achieves the best performance possibly because the sample data generated by this method contains more unbiased feature characteristics of review helpfulness.
Early Detection of Sepsis in ICU Patients Using Logistic Regression

Sepsis is a dangerous condition that happens when the body’s reaction to contamination results in tissue harm, failure of organs, or sometimes even death. Early recognition and anti-infection sepsis-treatment are basic to enhancing sepsis results, where every hour of deferred treatment has been related with an escalation in mortality. Along these lines, this image gives a reasonable view as to the significance of early discovery of sepsis. This study focuses on detecting the onset of sepsis based on 40 features from ICU patients using the logistic regression classifier. The heavy class imbalance between septic and nonseptic patients is addressed by giving more weight to the minority class. Pearson correlation coefficient was used to select the features useful to predict the target variable and the data was standardized afterwards. Using the scoring system provided by PhysioNet/Computing in Cardiology (CinC) Challenge 2019 ,the model achieved a score of 32.4% on the training dataset (20336 subjects) and 14% on the unseen test dataset (20000 subjects).
Green Computing in Heterogeneous Internet of Things: Optimizing Energy Allocation Using SARSA-based Reinforcement Learning

Green computing has been emerged as a promising paradigm to harvest energy from renewable resources to reduce use of conventional energy source in Internet of Things. Energy harvested battery-operated computation chargers are deployed in network to increase energy efficiency and network lifetime by recharging the nodes. The goal of paper is to allocate optimal power and optimal channel distribution to each node in network, subject to optimize the energy efficiency of network. As, wireless channel and energy harvested from environment are stochastic in nature. So, we propose a model-free 'on policy' based reinforcement learning (RL) approach to learn transition probability of continuous state space to get optimal reward which is unknown to network. To solve the formulated problem, an RL agent uses state-action-reward-state-action (SARSA) algorithm with linear function approximation to optimize the reward. Further, the proposed SARSA algorithm is compared with Q-Learning and Myopic algorithm to analyze the network energy efficiency.
Simultaneous Intrinsic and Extrinsic Calibration of a Visual-Odometric Sensor System

The navigation system of a mobile robot is usually a combination of different sensors. In order to align these measurements, the intrinsic parameters of each sensor and the spatio-temporal extrinsic parameters among them are required to be precisely calibrated. In this paper, a simultaneous intrinsic and extrinsic calibration algorithm of a visual-odometric system is proposed. An initialization process is designed to generate rough estimation of the camera intrinsics, the odometric parameters, and the spatio extrinsic parameters. Then, a joint optimization problem is iteratively solved to obtain a refined calibration result. By taking temporal delay into consideration during the calibration process, the estimation accuracy is highly improved. Experiments are conducted with simulation datasets and also an industrial autonomous guided vehicle (AGV) system. The results are presented to validate the efficiency of our algorithm.
Deep Learning Enabled Parameters Estimation Using Time-Frequency Analysis of Chirp Signals

This study is concerned with the problem of estimating the parameters of constant amplitude chirp signals whose duty ratio is less than 1 during an observation period, with mono or multiple components, embedded in noise. A novel method for estimating the parameters, central frequency and chirp rate of an chirp signal is proposed. The method is referred to as Wigner--Ville distribution--complex valued convolutional neural network (WVD--CV CNN). The proposed method can be regarded as a processor, whose input is a complex-valued WVD matrix and the output is one or more sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of conventional methods. Further, we demonstrate that the estimation accuracy of the proposed method is robust to cross interference on WVD.
5G New Radio Compatible Multicarrier Signals Delivery over an Optical/Millimeter-Wave Analog Radio-over-Fiber Fronthaul Link

Millimeter-wave carriers with high spectral purity are paramount for carrying kHz level subcarrier spacing multi-carrier signals. This work demonstrates the successful generation of 60.6 GHz OFDM signal, with 61 kHz subcarrier spacing, in an optical heterodyne analog radio-over-fiber system using gain switched laser source.
Extending Attribute-Based Access Control Model with Authentication Information for Internet of Things

Internet of Things (IoT) brings not only wide range of opportunities but also security and privacy concerns. Consisting of many connected devices used in a highly interactive way, one of the main security concerns in IoT is unauthorized access. Traditional access control models do not support dynamic and fine-grained access control policies. Attribute-Based Access Control (ABAC) model is usually considered the most satisfactory access control model for running IoT applications. In this paper, we propose to take into the user authentication matching score obtained from a biometric authentication system consideration during making access control decisions. We emphasize the need of fine-grained access control and suggest to create access control policies per functionality of the device instead of per device regarding to the least privilege principle of information security. We give full or partial permission to certain functionalities of the IoT devices based on the user’s authentication matching score thus provide more fine-grained and powerful access control mechanism. We present an extended Attribute-Based Access Control model that includes the assurance level of user authentication in access control policies and partially or fully permit the request accordingly.
Order Dependency in Sequential Correlation

Data analytics is a core part of the fast-moving world in the modern era, and its origin is data mining. SSequential pattern mining is a cutting-edge studies location of facts mining. An easy sample mining technique is inadequate to apprehend the connection among the objects in a dataset. A correlation degree may be extra beneficial to decide the dependency among the entities. We suggest a method in this paper that can evaluate the relationship’s strengths and the degree of order. While assessing the ranking of dependency, we use one way and reverse connections. Moreover, To determine the order of dependency, we use one method and contrary relationships. Sequential correlations are widely applicable in the sector of diagnosing disease, optimize the internet, and develop active retail strategies.
Independent Vector Analysis for Blind Speech Separation Using Complex Generalized Gaussian Mixture Model with Weighted Variance

In this paper, we propose using complex generalized Gaussian mixture distribution with weighted variance for speech modelling and devise an improved independent vector analysis (IVA) algorithm for blind speech separation (BSS). Capable of capturing both non-Gaussianity and non-stationarity, the proposed complex generalized Gaussian mixture model (CGGMM) allows for a much flexible characterization of practical speech signals. The majorization minimization (MM) framework is adopted for the IVA algorithm design. Each iteration of the algorithm is comprised of the updates of demixing matrices and mixture model parameters. For demixing matrices, the update operates in a manner similar to that of the auxiliary function based IVA (AuxIVA) method, and for mixture parameters, the expectation maximization (EM) update is performed. As both updates are in closed form and pre-whitening is not a prerequisite, the IVA algorithm under CGGMM is of low complexity and can be carried out efficiently. Experimental results show that the proposed algorithm outperforms existing ones in terms of separation accuracy and also enjoys a fast convergence rate in both simulated and real environments.