Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data imputation. Despite their superior imputation accuracies, the adoption of these imputed data is not uniform in modern ITSs applications. The primary goal of this paper is to explore how to use tensor completion methods to support ITSs. In particular, we study how to improve traffic flow prediction accuracy under different missing scenarios. Specifically, three common missing scenarios including element-wise random missing, time-structured missing, and space-structured missing are considered. Four classical tensor completion models including Smooth PARAFAC Decomposition based Completion (SPC), CP Decomposition-based (CP-WOPT) Completion, Tucker Decomposition-based Completion (TDI), and High-accuracy Low-rank Tensor Completion (HaLRTC) are used to impute the missing data. Four well-known prediction methods including Support Vector Regression (SVR), K-nearest Neighbor (KNN), Gradient Boost Regression Tree (GBRT), and Long Short-term Memory (LSTM) are tested. The simple mean value interpolation completed traffic data is regarded as the baseline data. The extensive experiments show that improvements of traffic flow prediction can be achieved by increasing missing traffic data imputation accuracy at most cases. Interestingly we find that prediction accuracy cannot be improved by an imputation model when the sparsely observed training datasets already provide sufficient training samples.
A Detailed Study on Bangladeshi Road Sign Detection and Recognition

Automatic road sign detection and recognition is crucial for autonomous Driver Assistance Systems (DAS). Numerous road accidents and casualties are reported every year due to the negligence of drivers. The system is mainly developed for autonomous vehicles and drives to warn them about the existing road sign on the road and also to reduce the frequency of accidents throughout the country. Road sign detection and recognition is still challenging concerning Bangladesh because of the shortage of big dataset. The dataset for this work has been collected from different regions throughout the country. In this system, the Single Shot Multibox Detector (SSD) is used for automatic detection and recognition of road sign. The Convolutional Neural Network (CNN) is also applied for the recognition purpose. The system is able to achieve the average detection rate of 76.52% and the recognition accuracy of 86.23% using SSD and also accomplish the recognition accuracy of 80.26% while using CNN. Therefore, the proposed system is robust enough to detect and recognize road sign.
Underwater Fish Classification of Trout and Grayling

Classification of fish is important to assist biologists in environmental monitoring, understanding fish behavior and more. Live fish classification is a challenging problems due to free movements, the light condition and image quality. Recently, deep neural network has shown great performance in image classification and object recognition problems, therefore, transfer learning based on Alexnet is applied on brown trout (Salmo trutta) and European grayling (Thymallus thymallus) images extracted from videos for classification without prior pre-processing. Very high accuracy above 99% and almost perfect F1-score are obtained and this network also can classify the incomplete fish images well with 98% accuracy.
Advancing Uncooled Infrared Imagers Using An Open-Circuit Voltage Pixel

A topology leveraging a photodetector in the forward bias region generating an open-circuit voltage is proposed. Connecting the anode of the photodetector to the gate of a MOSFET device operating in the subthreshold region provides the basis for a new open-circuit voltage pixel (VocP). Theoretical analysis outlining the effective photodetector response and performance benefits is described. An integrated circuit (IC) with direct-injection pixels modified to support the VocP front-end and analog output readout fabricated in a CMOS 0.18 μm technology is also presented. The IC allows testing of mid-wave infrared (IR) photodiodes operating in both the photocurrent and VocP modes. The VocP pixel is compared to a traditional reverse bias current mode photodetector configuration. Simulation, modeling, and measurement show improved sensitivity and faster response time for the VocP over direct photocurrent detection.
Efficient two stage approach to detect face liveness : Motion based and Deep learning based

Face liveness detection is a big challenge for the researcher. Face recognition based security system suffer from spoofing attack, because of lacking of proper face liveness detection system. In this paper, we proposed a new approach to prevent spoofing attack with a two stage approach, one is motion based and another is deep learning based. The network is train on ROSE-Youtu Face Liveness Detection Database. The whole model is test on real time videos from webcam. This combine approach gives a better performance than other approaches in ROSE-Youtu Face Liveness Detection Database. Our proposed model gives an accuracy of 95.44% and error rate of 4.56% which is better than existing models on ROSE-Youtu Face Liveness Detection Database.
ResiRCA: A Resilient Energy Harvesting ReRAM Crossbar-Based Accelerator for Intelligent Embedded Processors

Many recent works have shown substantial efficiency boosts from performing inference tasks on Internet of Things (IoT) nodes rather than merely transmitting raw sensor data. However, such tasks, e.g., convolutional neural networks (CNNs), are very compute intensive. They are therefore challenging to complete at sensing-matched latencies in ultra-low-power and energy-harvesting IoT nodes. ReRAM crossbar-based accelerators (RCAs) are an ideal candidate to perform the dominant multiplication-and-accumulation (MAC) operations in CNNs efficiently, but conventional, performance-oriented RCAs, while energy-efficient, are power hungry and ill-optimized for the intermittent and unstable power supply of energy-harvesting IoT nodes. This paper presents the ResiRCA architecture that integrates a new, lightweight, and configurable RCA suitable for energy harvesting environments as an opportunistically executing augmentation to a baseline sense-and-transmit battery-powered IoT node. To maximize ResiRCA throughput under different power levels, we develop the ResiSchedule approach for dynamic RCA reconfiguration. The proposed approach uses loop tiling-based computation decomposition, model duplication within the RCA, and inter-layer pipelining to reduce RCA activation thresholds and more closely track execution costs with dynamic power income. Experimental results show that ResiRCA together with ResiSchedule achieve average speedups and energy efficiency improvements of 8x and 14x respectively compared to a baseline RCA with intermittency-unaware scheduling.
Ensemble Residual Networks for Short-Term Load Forecasting

In this paper, we propose a new ensemble residual network model for short-term load forecasting (STLF). This model improves the accuracy of short-term load forecasting (24 hours in advance). The model has a two-stage network structure. First, the different fully-connected layers are combined, and the combined structure is similar to a recurrent neural network (RNN). Features obtained from historical load data are input to the first stage of the model to get preliminary prediction results. The second stage of the model is a modified residual network, and the final predictions are output from here. We use the ensemble snapshot model with learning rate decay to improve the generalization capability of the model. The model proposed in this paper was trained and tested on two public datasets. Numerical testing shows that the proposed model can get better forecasting results in comparison with other methods, and the ensemble method adopted effectively improves the generalization ability of the model.
Stock Market Prediction Using Artificial Neural Networks

Anticipating securities exchange precisely has dependably charmed the market analysts. During the previous couple of decades different AI strategies have been connected to ponder the very stochastic nature of financial exchange by catching and utilizing dull examples. Stock exercises evaluating are an intense interest for stock clients. This stock evaluating is a testing issue. Henceforth, we should a need to create application that is able to precisely foresee bearings of stock value movement. Forecasting and anticipating the patterns of market is the most critical utilizations of securities exchange. It likewise reveals the future market conduct which dependably encourages the speculators to comprehend when and what stocks can be obtained for the development of their venture. For this reasons, a considerable lot of the explores have been done as such far in the territory of investigating the financial exchange utilizing information mining.
Passive Capacitor Voltage Balancing in Modular Multilevel Converter During its Precharge: Analysis and Design

The modular multilevel converter (MMC) is one of the main converter topologies used in High Voltage Direct Current and is also used in other applications including medium voltage drives and renewable energy sources integration. Some advantages of the MMC are that it can be used without bulky transformers and output filters and its modular design based on the connection of submodules (SMs), which makes it easy to manufacture. It is advantageous that each SM local signal electronics circuits are self powered by means of a local auxiliary power supply fed from the SM dc-link. However, during the precharge stage this configuration might not lead to balanced nor stable SM voltages. The balancing of the MMC’s capacitor voltages during its precharge stage is crucial to its correct operation. A passive balancing strategy that consists in adding a balancing resistance in parallel to each one of the SMs is analyzed in this work in light of its non linear dynamics. The stability of the desired operation equilibrium point is analytically proven for an arbitrary number of submodules. Guidelines for the design of the resistance and characterization of the system nonlinear dynamics are presented from simulation results. Experiments confirm the analytical and simulation results.
Convolutional Neural Networks for Fingerprint Liveness Detection System

Fingerprint liveness detection is a challenge that possess tremendous security risks. The detection is a technique for determining the liveness of a given biometric system and to ensure it is not fake. Fingerprint attack deals with images coming from artificial replication of symptomatic fingerprints, which are made of wood glue, gelatin or latex. To obtain a better liveness detection system, a convolution neural network is used to develop a system in our work. Our system is evaluated with LivDet 2019 Database which consist of 2000 live and fake biometric fingerprint images. Within the small training set containing 1300 samples, the trained model achieved 100% training accuracy and testing accuracy of 95%. The features taken from the CNN were given to machine learning classifiers for a comparison study where K-Nearest Neighbors, Logistic Regression and Naïve- Bayes classifiers gave results as good as the CNN architecture.
Estimation and Compensation for Discrete-Time IT2 Fuzzy Systems Against Time Delay Switch Attacks

This paper examines estimation and compensation for discrete-time interval type-2 (IT2) against time delay switch (TDS) attacks. Our goal is to propose a novel method to attenuate the negative effect derived from the TDS attacks. To synchronously estimate the system states and attack perturbation, an augmented observer is first proposed by using the descriptor system approach. The attenuation of negative effects, from the TDS attacks to the system performances, is then realized by using the compensation controller. Finally, we use a numerical simulation to demonstrate the effectiveness of the proposed methods.
Dynamic Modeling and Control of a Three-Port ZVS-PWM Three-Phase Push Pull DC-DC Converter

This paper proposes the mathematic model and the control of the current and voltage loops of the ZVS-PWM three-phase current-fed push-pull DC-DC converter. The converter has three ports, two bidirectional ports and one unidirectional port and can be used in various applications such as electric vehicles, battery charging system, fuel cell, isolated mini grids and micro grids. The proposed control is able to invert power flow by imposing main inductor current value and direction as well as maintaining voltage at a given port under load variations. The control structure for each case is presented, as well as the deduction of the main mathematical expressions. In order to validate the performance of the proposed control strategy, simulation results for a 500 W converter with 50V in port 1 and 200 V in port 3 and a switching frequency of 40 kHz are presented here.
Integration of 5G Rectangular MIMO Antenna Array and GSM Antenna for Dual-Band Base Station Applications

In this paper, a dual-band dual-polarized antenna array is presented for 5G base station application. The developed antenna array consists of a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> planar MIMO array operating at 3.3-5.0 GHz band (upper band UB) and a single antenna element working at 0.69-0.96 GHz band (lower band LB). The dual-band operation is based on our previously proposed antenna topology. The novelty of this paper is that the UB antenna array is a large-scale rectangular lattice array with 16 antenna elements for practical MIMO applications. Meanwhile, both the cross-band and in-band mutual coupling among the LB and UB antennas are furtherly suppressed by employing three decoupling techniques including rectangular ring resonator, ferrite chock ring, and novel baffle structure. With these decoupling technologies, the UB antenna array and the LB antenna are successfully integrated within a compact volume of <inline-formula> <tex-math notation="LaTeX">$0.93\lambda _{L} \times 0.93\lambda _{L} \times 0.17\lambda _{L}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{L}$ </tex-math></inline-formula> is the wavelength at 0.82 GHz). Experimental results show that the proposed dual-band antenna array offers high cross-band port isolation (>30 dB). Stable radiation patterns are also achieved for both the LB and UB antennas with averaged gain of 8.6 dBi and 7.3 dBi, respectively. The radiation efficiency is higher than 90% across the entire operation bands. It is the first time to realize a large scale sub-6 GHz MIMO antenna array shared-aperture working with a GSM band antenna element in a such compact volume.
Towards Automatic Prostate Gleason Grading Via Deep Convolutional Neural Networks

Prostate Cancer has become one of the deadliest cancers among males in many nations. Pathologists use various approaches for the detection and the staging of prostate cancer. Microscopic inspection of biopsy tissues is the most accurate approach among them. The Gleason grading system is used to evaluate the stage of Prostate Cancer using prostate biopsy samples. The task of assigning a grade to each region in a tissue is a time-consuming task. Furthermore, this task often has several challenges since it has considerable inter-observer variability even among expert pathologists. In this paper, we propose an automatic method for this task using a deep learningbased approach. For this purpose, we use the DeepLabV3+ model with MobileNetV2 backbone and train it with the newly released dataset from Gleason 2019 challenge. Our model achieves the mean Cohen's quadratic kappa score of 0.56 with the pathologists' annotations on the test subset which is higher than the inter-pathologists' score (0.55).
Experimental Workbench: a tools to help to teaching the techniques of drives of electric machines

The rapidly growing technological advancements for the electric machines driven systems have made teaching techniques are obsolete. This advancements have introduced difficulties in the teaching process. Aiming to improve the quality of the education, this paper aims to present an experimental workbench for assist in laboratory classes, facilitating the absorption of the content. Will be shown the development and validation of an experimental workbench set up to help teaching. The development of experimental workbench will be divided into three parts: the Power Circuit, the Control Circuit and Precharge Circuit. To validation the experimental workbench as a theaching tool, will be presented the theory of scalar control (V/f), comparing the results from simulation with the experimental. Showing that the experimental workbench as a good tool to improve the quality of the education.
Technical Loss Evaluation and Reduction for Agricultural Distribution Feeder

In India, the growth in underground irrigation increasing which makes sharp growth rate of electricity in agricultural sector. The irrigation pump sets will connect to the local distribution network in order to get supply. It is observed that technical losses in distribution networks is high. The technical losses mainly due to in-proper Low Tension networks, Load growth, unaccountable consumers (power theft) and inefficient irrigation pump sets. To evaluate the technical losses in the network, a detailed network information is required. In this paper, a detailed methodology has been proposed to collect the proper technical data of the network, loss evaluation and finally loss reduction. Loss evaluation and reduction studies have been carried out for 11kV Hosakote agriculture distribution feeder in Chamundeswari Electricity Supply Corporation (CESC) Mysuru area Karnataka, India. Finally, two approaches are used to reduce the technical losses in the network. Replacing the inefficient irrigation pump sets and 11kV network reconfiguration by using particle swarm optimization (PSO) technique. The results will show that loss reduction and energy savings in 11kV Hoskote feeder CESC Mysuru.
Feature Selection Techniques for Cancer Classification applied to Microarray Data: A survey

In multidimensional microarrays, data that collect gene expression profiles that fulfill the state of the cell at the molecular level. Feature selection and extraction have become an obvious need for the analysis of this microarray. There are many different methods for selecting and extracting attributes, and they are widely used. One of the serious tasks is to learn how to extract useful information from huge microarrays datasets with complex relationships between different genes. These methods are aimed at removing excess and irrelevant traits and extruding marker genes that effectively maintain classification accuracy. This report gives an overview of the various ways of performing dimensional reduction methods that were used in these microarrays to select important features and presents a comparison between them. The advantages and disadvantages of several methods are described in order to show an obvious idea of when to use each of them to save computational time and resources.
Visual Navigation Using a Webcam Based on Semantic Segmentation for Indoor Robots

The realization of a moving robot that can autonomously work in an actual environment has become important. A three-dimensional dense map that was created using three-dimensional (3D) depth sensors, such as light detection and ranging (LiDAR), is popular in the research field of autonomous moving robots. However, this approach has a few disadvantages: the price of 3D sensing devices and the robustness of localization in practical scenarios with many movable obstacles. To solve this problem, this paper proposes a vision-based navigation scheme that enables autonomous movement in indoor scenes; only a webcam is used as an external sensor. The experimental results from an experiment conducted in a university building demonstrated that a robot can move around on a floor.
Transmission Timing Adjustment Algorithm in IoT System Equipped with Low-cost Oscillator with Low Frequency Stability

This paper focuses on devices used for Internet of Things (IoT). The IoT devices are assumed that they transmit signals periodically at a regular interval and a Low-cost oscillator is equipped. The authors showed an impact of packet collisions between IoT devices due to a low frequency stability caused by the cheap oscillator by using computer simulation. Moreover, the authors have proposed an algorithm that a base station modifies the communication cycle by delaying a transmission timing considering the reception interval between IoT devices, and confirmed that it can suppress the impact of packet collisions under devices' frequency instability. However, there are problems; the device which modified the transmission timing collides with the following IoT device, and the total number of modifications greatly increases from this modification on. Therefore, this paper confirms to suppress packet collisions more than the previous method by simulation performances with a new method.
Cyber-Physical Interdependencies in Power Plant Systems: A Review of Cyber Security Risks

Realizing the importance of the concept of “smart city” and its impact on the quality of life, many infrastructures, such as power plants, began their digital transformation process by leveraging modern computing and advanced communication technologies. Unfortunately, by increasing the number of connections, power plants become more and more vulnerable and also an attractive target for cyber-physical attacks. The analysis of interdependencies among system components reveals interdependent connections, and facilitates the identification of those among them that are in need of special protection. In this paper, we review the recent literature which utilizes graph-based models and network-based models to study these interdependencies. A comprehensive overview, based on the main features of the systems including communication direction, control parameters, research target, scalability, security and safety, is presented. We also assess the computational complexity associated with the approaches presented in the reviewed papers, and we use this metric to assess the scalability of the approaches.
A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks

This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models a distortion stomp box as a neural network consisting of LSTM layers. In this approach, the neural network is employed for learning the nonlinear behavior of the distortion stomp boxes. All the parameters for replicating the distortion sound are estimated through its training process using the input and output signals obtained from some commercial stomp boxes. The experimental result indicates that the proposed technique may have a certain appropriateness to replicate the distortion sound by using the well-trained neural networks.