Miracle-dz/UniFAR-FTUs huggingface dataset:
Double layer wiring on fabric using soft polyurethane film substrate and its application to motion capture devices

E-textiles technology, which incorporates sensors on fabric, is expected to have a variety of applications. We propose a two-layer structure using a soft polyurethane film substrate to realize a soft substrate that does not restrict movement and a two-layer wiring for wiring integration. As evaluation tests of the two-layer wiring, the interlayer insulation resistance, the resistance of the interlayer connections is measured, and conducted static and dynamic bending tests to investigate whether the proposed structure meets the required specifications of the inertial sensor to be used. We also reproduced the posture of the device on Unity using the created sensor device.
Recognition of Human Activity using Signal Processing & Deep Neural Networks

The task of tracking the activities being performed by humans is of immense importance as it finds application in healthcare, marketing, surveillance, etc. To keep track of this, they need to be recognized and classified into various activities. The data can be collected through smart devices such as smartwatches or smartphones which internally use various sensors to measure the physical movements of the person using it. We have used Human Activity Recognition Dataset from the UCI ML Repository, which consists of the data being collected from the smartphones of 30 volunteers. After choosing the appropriate features, we trained an Artificial Neural Network and a Long Short Term Memory Neural Network to classify the activities into various categories and performed a comparative analysis of these neural networks among themselves & also with respect to the previously leveraged techniques & algorithms on the dataset.
Solar PV Fed Modular Multilevel Inverter with Fuzzy Logic Control for Marine Water Pumping Applications

This paper presents the implementation of the intelligent techniques to drive the induction motor used in the application for pumping water in ships. The eleven-level inverter that is being suggested can regulate the speed of an IM drive that is powered by solar photovoltaics. With an induction motor (IM) drive and MMI, the proposed control architecture for a marine water pumping system is investigated and validated in this study. The inverter is evaluated using Proportional-Integral (PI), Fuzzy Logic (FL) and ANFIS logic driven controllers to improve its performance. A comparative analysis will be conducted towards increasing of robustness of the inverter in terms of Total Harmonic Distortion(THD), peak over shoot and controller settling time. The design and combination of IM Drive, MMI, and controllers exclusively for ocean pumping stations applications make the proposed control method innovative.
A Single-Phase Integrated Battery Charger Simulation Compare On-board Battery Charger with PFC Boost Converter and PSFB DC-DC Converter

Currently, electrical vehicles (EVs) are popularly used, and various technologies have been developed to improve systems related to EVs. Electric vehicle charging systems will play an important role. The development of electric vehicle charging systems, one of the systems used to charge the battery of an electric vehicle for a single-phase system that can be used at home is a charging system consisting of a power factor collector (PFC) boost converter in combination with a Phase-Shift Full-Bridge (PSFB) dc-dc converter. There is one charging system has been developed that can be charged directly through the coil wiring of an electric vehicle's electric machine for reducing device usage semiconductors used in the charging circuit and including being able to increase the space for the EVs. In this paper present a simulation of two types of battery charging systems, one is an electric vehicle charging system that uses a PFC circuit combined with a PSFB that uses an LLC resonant converter and IBC system to compare the operation to charge the batteries of both systems using MATLAB Simulink software for a 48 V 150 Ah lithium-ion battery. As a result, the state of charge (SOC) of the battery increases during both charging systems, thereby confirming the suitability of the charging system. The battery in electric vehicles uses the IBC system to develop converters for use in energy storage systems.
Deep Learning-based Approach on sgRNA off-target Prediction in CRISPR/Cas9

CRISPR/Cas9 as a gene editing tool has been widely applied in many organisms. However unintended mutation caused by sgRNA off-target effect is still possible to occur while implementing CRISPR/Cas9. To reduce the off-target possibility some researchers already develop deep learning models to predict the sgRNA off-target to the corresponding DNA target. Those models implement deep learning approaches such as CNN and embedding to compute and obtain the off-target scores. Therefore, the aim of this study was to compare five different combination models of Word2Vec embedding and CNN to find which one is the best for predicting off-target sgRNA in classification schema. The final result shows that a combination of Word2Vec embedding and biLSTM in CNN model can achieve auROC and auPRC score of 99.61% and 86.67% respectively, which is better than CnnCRISPR model that was used as the reference for model architecture in this study. By comparing five different models, the highest accuracy achieved in this experiment reached 99.67%.
An Elastic Shoulder Joint for Humanoid Robotics Application

Designing multiple DOFs joints having a parallel kinematic structure brings additional challenges, if compared with classical single DOF joints. Generally, it is more difficult to formalize the forward kinematic model for a parallel kinematic chain, in addition, while performing a target motion, the actuators have to be carefully coordinated to avoid wasting energy or cause damages to the system. The problem is becoming even more complex if instead of using a rigid structure, elastic elements are incorporated in the design. With this work, we present how to make usage of the method of Finite Element Analysis (FEA) in order to compare alternative designs for the central leg of a shoulder joint having a parallel kinematic architecture. The optimization process of the joint's design was defined by providing 2 different scenarios: 1) bending and 2) twisting. Moreover, we introduce how to model the kinematics of an elastic legs, by replacing the bending object with a combination of 3 universal joints and 2 links. In order to increase the payload-to-weight ratio of the shoulder joint, a high power-to-weight ratio servomotor was integrated by using a tendon-driven mechanism.
A Modified Dual Loop Droop Control for Parallel Operation of Grid-forming Converters

Distributed Generators (DGs) are becoming more integrated into the existing power system due to the growing popularity of renewable energy sources. However, in contrast to synchronous generators in a conventional power system, most renewable-based DGs are connected to the grid via a low inertial power electronic interface, which can impact the system stability significantly due to their uncertain generation. These power electronic converters can operate in grid following or grid forming mode. However, the latter taxonomy is the most widely used topology since it can operate in grid-connected or standalone modes and can self-restore the nominal frequency and voltage conditions. This article presents a modified dual loop droop control technique to operate two grid-forming converters in parallel, acquire precise power sharing, and maintain voltage regulation according to the DGs’ rated capacities. The mathematical formulation and the simulation of the proposed control strategy are performed in MATLAB/Simulink, while OPAL-RT is utilized for the real-time digital simulation of the framework in Software in loop (SIL) mode.
Detecting Fire in Color Images using Convolutional Neural Network Architectures

Fire accidents are the most prevalent disasters both indoors and outdoors. Early detection of such accidents helps prevent loss of property and lives. Approaches to detect fire are mostly based on environmental sensors or visual signals. Camera captured images and video sequences are utilized to detect smoke and fires by employing statistical & structural techniques as well as deep learning methods.In this work, we have employed various standard and custom CNN architectures for detecting fires in both indoor and outdoor environments. We have used 2 different publicly available datasets to train & test our models. These results are compared against already reported results. We find that AlexNet, though a simple architecture, performs the best at 86% accuracy in detecting fire in both indoor and outdoor environments.
Portable Camera based Identification System for Visually Impaired People

The global expansion of visual impairment is a very sensitive subject. Living in constant uncertainty has always had a negative impact on many aspects of their lives. The digital revolution is accelerating in the fast-paced culture, and it has had a good impact on blind people's life as well. This study intends to develop a support system for those who are blind or visually impaired. People with visual impairments from the partially unsighted to the completely blind can use the image recognition and text navigation system to support and direct them via auditory commands. The tool includes the sequential operations of indoor obstacle detection and recognition. The presence of an object is identified using an ultrasonic sensor. Here, a single chip known as a Raspberry Pi is being integrated with both the text navigation device and the identification system. The Raspberry Pi operates with great accuracy and speed. An object is photographed by the navigation system's camera module, processed using the single shot detection (SSD) algorithm, and then converted to text by an optical character recognition (OCR) engine before being communicated over the speaker using the text-to-speech (TTS) converter tool. The object recognition and the text navigation system are specially designed for blind individuals to operate the tool conveniently in a solitary manner.
Diagnosis of Lung and Pleural Diseases by Machine Learning Algorithms

This present work employs the classification equipment for detection of pleural carcinoma with an accuracy of 98.30%. The data collected from the lung cancer patients is divided into health data and clinical data for analyzing the efficacy of the Algorithms of Machine learning. To direct the physicians in early detection and providing suitable treatment for the lung cancer patients, the machine learning algorithms are utilized and analyzed based on the data collected from the health and medical data of the patients. Separation process applied with various algorithms of machine learning to increase the success levels. To attain about 98.30% of success rate many algorithms are evaluated. Among the numerous algorithms tried the most productive isolation process is the Linear Discriminant Analysis.
Approximations for some statistical moments of the solution process of stochastic Navier-Stokes equation using WHEP technique

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P4 based Switch Centric Flow table Overflow Detection and Mitigation in Data Plane Devices

Flow table overflow attack on data plane devices is one of the prominent vulnerabilities in the Software Defined Networking (SDN) architecture. Flow table uses limited-sized TCAM to store the flow rules in the data plane. Unfortunately, TCAM based Flow tables are prone to various attacks such as memory saturation attacks, DDoS attacks, cross-plane attacks, Flow table overflow attacks, etc. These attacks lead to the starvation of benign requests, and saturation of network resources. However, the existing solutions are focused on the controller-based attack mitigation mechanism using OpenFlow switches which increases communication overhead between the control plane and data plane. This paper proposes a switch centric based in-network Flow table overflow attack detection and mitigation framework. We introduce IP_SourceGuard which keeps an audit of the flow rules by counting the threat value of a particular port. Mitigating the attack traffic when the threat value exceeds the limit of the warning threshold. Further, IP_SourceGuard blocks the attacker port from further not communicating it to the network. The solution has been implemented using the BMv2 software switch and determined that the solution reduces the Flow table utilization to 88%. From the result, it is observed that our solution mitigates the Flow table overflow attack in a real-time environment.
Modeling of Body Pressure Distribution on Alternative Pressure Mattress

Recently, an alternative pressure mattress has been proposed to prevent pressure ulcers. In this study, we aim to design a control system for body pressure distribution using the mattress. This study focuses on modeling the dynamics between the body pressure distribution and the mattress. As modeling based on first principles is challenging, we identify the model from experimental data. However, when high-dimensional data such as body pressure distribution are directly used for identification, the model accuracy can deteriorate. Therefore, we need to determine the suitable model structure or dataset. Herein, we examined how the prediction accuracy of body pressure distribution varies with the dataset and model structure. We also proposed a method for representing the model as the dynamics of the parameters of the Gaussian Mixture Model to suppress the estimated parameters. To the best of our knowledge, this study is the first to propose the body pressure model as dynamics of the parameters of the Gaussian Mixture Model.
Object Size Recognition as Intra-class Variations using Transfer Learning

The ability to differentiate various products in the retail store plays an essential role to provide effectiveness to customers and reduce or even eliminate long queues. However, traditional machine learning algorithms are incapable of recognizing many subordinate categories in various retail product. This paper aims to recognize the retail product recognition algorithm based on the YOLOv7 model in terms of intra-class variations, with the sub-categories of brand and size. We used two schemes of the dataset to compare recognition performance between them. Firstly, the YOLOv7 is applied in the two schemes of the dataset that is annotated with the subordinate category to detect the brand as meta category. Secondly, the proposed method is applied by adding the object size classification into the YOLOv7 model where the square area of the bounding box was calculated to classify the product according to size. Confidence score and square area are used to verify the object and to obtain the product size, which represents sub-category of the product. The experimental results show that our proposed method achieves higher recall compared to baseline object detection.
Weekly Load Demand Forecasting using Supervised Deep Learning Techniques: A Case Study of Suranaree University of Technology

This paper presents supervised deep learning techniques for weekly electrical load demand forecasting at the Suranaree University of Technology, Nakhon Ratchasima, Thailand. Deep learning is a modern artificial intelligent technique developed from neural networks-based machine learning that mimics the nervous system of living organisms. Six supervised deep learning techniques, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (BiGRU) techniques, were used in this study. The supervised deep learning model was trained by daily load demand data from January 1, 2018, to May 18, 2022, before forecasting weekly load demand. The forecasting results show that LSTM, BiLSTM, and GRU are all techniques with minor errors. The BiLSTM technique, on the other hand, produced the most accurate forecasts, with an MAE of 13.07 kW, MAPE of 1.66%, and RMSE of 19.826 kW, respectively.
A Particle Swarm Optimization based Hybrid Pulse Width Modulation Strategy for Single DC Source per Phase Fed Binary Asymmetric Cascaded H-bridge Photovoltaic Inverter

This paper proposes an optimized hybrid nearest level-carrier based pulse width modulation (PWM) strategy for binary asymmetric cascaded H-bridge (CHB) multilevel inverter applicable for high power photovoltaic (PV) systems. The binary asymmetric CHB topology requires only one isolated PV source in each phase irrespective of the number of submodules present. This paper investigates the effect of hybridization of low frequency nearest level PWM and high frequency carrier based PWM methods on the multilevel inverter topology specifically in terms of power loss distribution and voltage harmonic distortion. A particle swarm optimization based hybrid PWM technique has been proposed to optimally hybridize the PWM techniques depending on the operating modulation index in order to minimize the power loss incurred in the multilevel inverter and to maintain the voltage harmonic distortions within grid code standard at the same time. The optimized hybrid PWM controlled photovoltaic multilevel inverter has been simulated for different modulation indices and the simulation results justify the effectiveness of the proposed PWM strategy.
Crisis Assessment Oriented Influence Maximization in Social Networks

Influence maximization (IM) aims to find a subset of $k$ nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node’s importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.
Scalable Low-Cost Sorting Network with Weighted Bit-Streams

Sorting is a fundamental function in many applications from data processing to database systems. For high performance, sorting-hardware based sorting designs are implemented by conventional binary or emerging stochastic computing (SC) approaches. Binary designs are fast and energy-efficient but costly to implement. SC-based designs, on the other hand, are area and power-efficient but slow and energy-hungry. So, the previous studies of the hardware-based sorting further faced scalability issues. In this work, we propose a novel scalable low-cost design for implementing sorting networks. We borrow the concept of SC for the area- and power efficiency but use weighted stochastic bit-streams to address the high latency and energy consumption issue of SC designs. A new lock and swap (LAS) unit is proposed to sort weighted bit-streams. The LAS-based sorting network can determine the result of comparing different input values early and then map the inputs to the corresponding outputs based on shorter weighted bit-streams. Experimental results show that the proposed design approach achieves much better hardware scalability than prior work. Especially, as increasing the number of inputs, the proposed scheme can reduce the energy consumption by about 3.8% - 93% compared to prior binary and SC-based designs.
Haar-CNN Cascade for Facial Expression Recognition

Recognizing emotions can aid in the interpretation and communication of non-verbal communication. Although deep neural networks perform well in automatically extracting and learning high-level representations of facial features from large amounts of multidimensional data, there is still a need for a face detection model to work fast for real-time applications. This working experiment investigates the use of Haar features as another feature extraction stage for a deep neural network to decrease the dimensionality of inputs in facial detection prior to expression recognition which is applied to multi-scale facial expression recognition (FER). Hybrid approaches are used for locating and categorizing facial features, with the initial approach consisting of Haar feature extraction combined with CNN-based models. The proposed inclusion of Haar Cascade with CNN demonstrated a recognition rate exceeding 90% for all facial expressions, with the greatest accuracy achieved in identifying “Happy” and “Surprise” expressions.
Droop Control Approach to Reduce Frequency Deviation and Enhance Active and Reactive Power sharing

This paper proposes an improved performance of droop control of Distributed Generator (DG) in frequency deviation, power sharing and voltage regulation at Point Common Coupling (PCC) during ON/OFF operations mode or during substantial change in load. A cascade control of speed droop and voltage droop is proposed which allows the voltage control to be increased/decreased during load variation known as a Load Acceptance Module (LAM). Therefore, the first speed droop control estimates the transient amount of reactive power induced during load variation or ON/OFF mode of operation. Whereas, the second voltage droop control injects the voltage at PCC to reduce the frequency deviation. A power droop control applied to the diesel generator is used to ensure active and reactive power sharing within the micro-grid whether in stand-alone mode or in grid-connected mode. The combination of the two droop controls applied to the diesel power plant optimizes the operating performance of the micro-grid and contributes to its stability.
Smartphone based Human Activity Recognition using CNNs and Autoencoder Features

Recognition of human activities is essential for many applications, and the widespread availability of low-cost sensors on smartphones and wearables has enabled the development of mobile apps capable of tracking user activities “in the wild. ” However, dealing with heterogeneous data from different devices and real-time scenarios presents significant challenges. In this study, a novel learning framework is proposed for Human Activity Recognition (HAR) that combines a Convolutional Neural Network (CNN) with an autoencoder for feature extraction. The study also investigates the importance of preprocessing techniques, including orientation-independent transformation, to mitigate heterogeneity when dealing with multiple types of smartphones. The results show that the proposed approach outperforms state-of-the-art methods in HAR, with an accuracy of 95.74% on the heterogeneous dataset used in this study. Furthermore, the study demonstrates that proposed framework can be effectively deployed on smartphones with limited computational resources, making it suitable for real-world applications.