Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Deep Learning-based Approach for Covid-19 Infection Identification in Chest X-ray Image using Improved Image Segmentation Technique

Covid-19 diagnosis systems are being improved with the emerging development of deep learning techniques. Covid-19 is widely known for the deadly effects and its high transmission rate. To overcome the challenges, different deep learning-based detection methods have been introduced through which the disease can easily be identified in patient's body. But only identification of the disease is not sufficient to assist physicians for further diagnosis. Infection identification process with severity measurement from medical image can put an advancement in current Covid-19 diagnosis systems. This work presents a novel infection detection approach based on image segmentation technique that can be used to localize the infection. The proposed system is able to predict segmented lung and mask images with visual representation so that it makes the diagnosis task easier for the physicians. ResNet-U-N et, VGG16-U-Net and a modified U-Net model have been implemented in the proposed work where the modified U-Net performed better with 0.968 IoU, 98.60% accuracy and 0.9567 of dice coefficient. An advanced module using OpenCV has been designed that can calculate the area of the predicted lung and infection mask images separately and then the infection percentage can be calculated accurately.
A Comparative Analysis of Retinal Disease Image Classification for OCT Using Deep Learning Techniques

Classifying retinal disorders with optical coherence tomography (OCT) scans is crucial. For patients to survive, these diseases must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors regularly by looking at optical coherence tomography (OCT) pictures. The manual diagnosing process is laborious, though. In this work, an automated detection and classification system for retinal diseases has been developed to assist doctors in their diagnosis. The noninvasive OCT test, which generates cross-section images of the retinal layer of the eye, allows ophthalmologists to diagnose conditions based on the layers of the retina. As a result, it is a crucial modality for the identification and measurement of retinal disorders and diseases. Ophthalmologists must spend a lotof time analyzing the images because OCT produces many images for each patient. The classification of patients’ OCT pictures into eight categories–Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, Normal, Diabetic Retinopathy, Age-Related Macular, Central Serous, Macular hole–is studied. To prevent patients from blindness, these diseases should be detected, diagnosed, and treated early. The purpose of this research is to present a thorough analysis of how various retinal disorders are classified using a variety of machine learning and deep learning techniques.
Independent PV-Battery Systems by Combining Autonomous Incremental Conductance Particle Swarm Technique with a Power Management Circuitry

The two primary problems with distributed generating systems were production power and sustain abilities. Lighting loads have lately had a significant impact on these concerns. Today's DC-provided lightings load deals a greater solutions. In the conventional network, obtaining DC outputs requires the use of a rectifier, which is expensive and has a low efficiency. The research offers an effective independent PV-battery (IPVB) system fed light emitting diode as a load that addresses these problems by utilising a power managing circuitry (PMC) with peak power point tracing. By using the Incremental conductance Particle Swarm (ICPS) approach with a different step size, the undesirable variations at MPP brought on via the conventional Incremental conductance's technology are greatly reduced. The non-isolated DC bidirectional converters in the PMC operates as a voltage regulator by a regulated DC voltages bus of 36 Volts. It selects the battery's charge and discharge phases depend upon the battery's capacity, the solar irradiation power, and the converter's output watts. The effectiveness of three peak power and PMC has in fact been investigated through simulations.
Power Quality Improvement of the Distribution System using a Solid-State Transformer

This paper demonstrates the capability of a solid-state transformer (SST) to improve the power quality of the distribution system. The three-stage 11 kV/400 V, 300 kVA, 3-phase SST discussed in this article comprises a cascaded multilevel converter-based ac/dc converter, multiple-active-bridge (MAB) converter with a high-frequency transformer acting as an isolated dc/dc converter, and a dc/ac inverter. The paper shows by simulation that the SST can address many of the power quality problems caused by grid irregularities such as voltage sag, swell, and harmonic distortion, which can cause voltage instability to the load and over-voltage across power devices, without the need for other traditional compensating devices as harmonic filters or dynamic voltage regulators (DVRs). Furthermore, the SST can offer reactive power support to the power grid similar to that from static compensators (STATCOMs) or static VAr compensators (SVC). To demonstrate the advantages of SST when compared with the traditional transformer, various simulations using the MATLAB/Simulink platform have been carried out. Simulation results reveal that while the transient sag/swell and the harmonic distortion on either side of the traditional transformer windings will have a direct influence on the load voltage/currents on the other side of the transformer windings, these effects can be mitigated with SST and the controller of SST can actively regulate the reactive power to provide the desired reactive power support.
Bayesian Optimized Autoencoder for Predictive Maintenance of Smart Packaging Machines

Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can help industries to maintain a sustainable production environment. On the contrary, a faulty system may degrade production efficiency and increase the cost. Smart packaging machines comprising several sensors can generate time series data and leverage data driven condition monitoring models to overcome faulty conditions. In this work, we have studied the application of Autoencoder as a data driven condition monitoring tool for the predictive maintenance of packaging machines. The trained Autoencoder on the new system's data can detect worn or degraded components over time. We have also used the Bayesian optimization algorithm to tune the hyper-parameters of the Autoencoder for better predictive performance. Moreover, the reconstruction error is analyzed to identify the worn components in the packaging machine.
An Enhanced Stress based Hairfall Detection and Prevention using KNN and Machine Learning Techniques

Numerous factors might affect a person's stress level, which results in hair loss. Due to variables such as increased employee dominance, job pressure, and work overload, among others employees in IT sectors are more prone to experience stress. Depression, anxiety, somatization, and attention deficit disorder are just a few of the mental health issues that stress can lead to, and even mortality. As a result, it's critical to recognize human stress early so that the proper treatments may be given and tension can be reduced. Numerous studies have been conducted on stress prediction. An extension of the skin, hair is an essentialcomponent of a person's facial beauty. The outcomes of some learning algorithms, like KNN, are superior. Other intelligent methods such as ML algorithms can be used to diagnose the diseases.
A Comparative Analysis of Different Control Strategies for Three-Level Neutral Point Clamped based Photovoltaic Central Inverter under Unbalanced Grid Operation

This paper investigates the different control techniques need to be applied to a three-phase three-level neutral point clamped based photovoltaic central inverter connected to an unbalanced utility grid. The power conversion stage for integrating the PV source to three-phase utility grid consists of a three-level DC-DC boost converter + three-phase three-level NPC based central inverter. The three-level boost converter helps in balancing the individual DC link capacitor voltages during sub-MPPT mode of operation. With unbalanced grid operation, the central inverter has been controlled to maintain either the balanced positive sequence components of three-phase grid current or a reduction in grid side active and reactive power fluctuations by allowing a certain amount of negative sequence current in the system. The entire system has been simulated in PLECS simulation tool to obtain the power loss distribution incurred in different parts of the system. This paper analyses the different power losses incurred in system for different control strategies when the PV central inverter injects power to an unbalanced utility grid and compares them to obtain the most efficient one.
Autonomous Vehicle System for Pothole Detection and Avoidance

In this rapid age of industrialization, artificial intelligence, and machine learning have played a pivotal role in achieving technological development. Autonomous Vehicles are no exception to the same. Leading companies such as Bosch, Tesla, Audi, and Toyota are working actively to improve the customer experience of autonomous vehicles on the road. Therefore, the issue of reliability over the technology and passenger safety becomes an essential issue. At the same time, the presence of potholes over roads in urban cities remains a persistent concern for the safety of road vehicles and their passengers. It thus gives rise to the need to develop technologies to ensure swift mobility of autonomous vehicles around the potholes to ensure improved customer feedback. Hence, this paper proposes a viable system based on modulating the speed and positioning of the autonomous vehicle, staying within the lane upon detecting a potential pothole in its course.
Short-term Load Demand Forecasting using Supervised Deep Learning Techniques: A Case Study of Suranaree University of Technology

The implementation of effective energy management necessitates the accurate forecasting of load demand. This study presents the efficacy and accuracy of supervised deep learning in forecasting short-term load demand. The deep learning technique is a neural network-based machine learning widely applied for load demand forecasting tasks. This study utilized five deep learning techniques: Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Bidirectional Gated Recurrent Units (BiGRU), CNN-GRU, and CNN-BiGRU. The daily electricity load demand data from the Suranaree University of Technology, Nakhon Ratchasima Province, Thailand, from January 1, 2022, to August 28, 2022, and the average daily temperature from NASA's data source were used to train the proposed deep learning techniques and forecast three-day load demand. By comparing the CNN, GRU, CNN-GRU, and CNN-BiGRU techniques, the BiGRU technique provides the most accurate three-day load demand forecast. The respective MAE, MAPE, and RMSE error indices are 20.38kW, 1.99%, and 30.3kW, respectively. The study's results demonstrate the effectiveness of deep learning techniques for forecasting short-term load demand.
Unified Model of Entity Tracking Using Deep Learning

The act of identifying a moving object inside a video is referred to as “entity tracking. ” It has applications in monitoring and robotics, as well as imaging medicine and tracking autonomous vehicles, among other areas. Single Object Tracking, Multiple Object Tracking, Video Object Segmentation, and Multi-Object Tracking and Segmentation are the four subtasks that make up the majority of the field of entity tracking. The vast majority of previously developed techniques are only produced for a single one or a portion of the subtasks. With the same set of model parameters, the unified model Unicorn is able to simultaneously solve all four different kinds of object tracking issues (SOT, MOT, VOS, and MOTS). The “Unicorn” model that we are currently working to implement uses a single network to solve all four tracking problems to the same degree or a higher degree than its explicit undertaking partners in eight different tracking datasets. These datasets include LaSOT, Tracking Net, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS.
Robot Locomotion Control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons

Central pattern generators (CPG) generate rhythmic gait patterns that can be tuned to exhibit various locomotion behaviors like walking, trotting, etc. CPGs inspired by biology have been implemented previously in robotics to generate periodic motion patterns. This paper aims to take the inspiration even further to present a novel methodology to control movement of a four-legged robot using a non-linear bio-mimetic neuron model. In contrast to using regular leaky integrate and fire (LIF) neurons to create coupled neural networks, our design uses non-linear neurons constituting a mixed-feedback (positive and negative) control system operating at multiple timescales (fast, slow and ultraslow ranging from sub-ms to seconds), to generate a variety of spike patterns that control the robotic limbs and hence its gait. The use of spikes as motor control signals allows for low memory usage and low latency operation of the robot. Unlike LIF neurons, the bio-mimetic neurons are also jitter tolerant making the CPG network more resilient and robust to perturbations in the input stimulus. As a proof of concept, we implemented our model on the Petoi Bittle bot, a quadruped pet dog robot and were able to reliably observe different modes of locomotion-walk, trot and jump. Four bio-mimetic neurons forming a CPG network to control the four limbs were implemented on Arduino microcontroller and compared to a similar CPG built using four LIF neurons. The differential equations for both neurons were solved real-time on Arduino and profiled for memory usage, latency and jitter tolerance. The CPG using bio-mimetic non-linear neurons used marginally higher memory (378 bytes, 18% higher than LIF neurons), incurred insignificant latency of 3.54ms compared to motor activation delay of 200ms, while providing upto 5-10x higher jitter tolerance.
Neural Network and Bias Correction Controls for Fast Transient Response of DC-DC Converter

We present a neural network based control method of DC-DC converter to realize fast transient response in coordination with the PID control. The neural network control effectively improves the transient response by modifying the reference value in the PID control dynamically using its predictions in the transient state. In addition, neural network timing control and bias correction are also adopted to obtain faster convergence. In the presented method, data acquisition and repetitive training of neural networks are proceeded off-line and the on-line prediction by repetitive trained neural networks is implemented in a commercially available computation unit. Experimental results confirm that our presented method obtains the significantly faster transient response compared to the conventional PID control.
Machine Learning based Speech Emotion Recognition in Hindi Audio

Emotions are states of the nervous system that are connected to thoughts, sensations, behavioural reactions, and varying degrees of pleasure or annoyance. Failure to recognise emotions may result in communication failure. As a result, identifying emotion becomes crucial. Also, worldwide a total of about 577.8 million people speaks Hindi. Thus, this Speech Emotion Recognition system aims to detect emotion from Hindi audio. Here, the approach used is to extract audio-based as well as text-based features from the input audio speech to detect emotion. Experimentation is done with several machine learning algorithms like Random Forest, Support Vector Classifier and Logistic Regression applied to both audio and text datasets separately. The final model selected in each is the one that gave the highest accuracy respectively. 72% accuracy using Support Vector Classifier with GridSearchCV in the text dataset and 95% accuracy using Random Forest in the audio dataset is achieved. The combined results are used to identify four emotions which are neutral, angry, sad and happy, across a database of many different speakers.
Authentic Tweets Recommendation System

Twitter is an online social networking website where people communicate with each other with the help of short messages, typically up to 150 characters. It is undoubtedly one of the most popular social media platforms that are present today. It has over 100 million daily active users with 500 million tweets being exchanged on the platform on a daily basis. With the advent of social media and networking, tweets of various kinds are being posted by people without a check for what is really true or not. What one reads on Twitter may not be fully authentic as there is no authentication of tweets that happens. People are free to post any tweet of their choice. There are 6 different categories for the user to choose from-Sports, Politics, Entertainment, Business, Technology, and Health. The user can choose at most 3 categories to get tweets. The approach builds a system that would automate the recommendation of authentic tweets using Twitter as the source. This is done by extracting trending tweets from Twitter and then filtering out the fake tweets if any. The users are presented with tweets of their choice as the approach builds a user recommendation system. Not only does this approach provide the users with the recent most trending tweets categorically but also ensures to present them with authentic tweets only.
Back Radiation Reduction in a UWB Antenna using Metamaterial Surface for Wireless Communication Devices

In this a 3*3 array of dual band AMC cells having dimensions of 15*15 is designed to reduce the backward radiations of a UWB antenna of size 35.5*33 . Each slot on the unit cell plays a key role in modelling the two desired frequencies of 4.7 and 9.9 GHz. The proposed AMC structure consists of a square patch loaded with a concentric ring and plus shaped slot in the center. The AMC behavior i.e. zero reflection phase point is observed at 4.7 and 9.9 GHz. The designed AMC structure is used as a reflecting layer to enhance forward radiations and thus reducing the backward radiations. Both structures (UWB antenna and dual band AMC structure) used FR-4 material with dielectric constant 4.4 and thickness 1.6 mm. As the designed UWB antenna already has satisfactory bandwidth (14.5 GHz) and gain (8.2 dB) values, therefore focus is given to reduce the back radiations of proposed antenna by using grounded AMC structure. The array size is fixed to be 3*3 whereas the air gap (d) between the two structures is varied and its corresponding effect on antenna results is studied. The proposed antenna is designed and simulated using computer simulation technology (CST) microwave simulation software.
A Novel Quantum Ant Colony Optimizing Algorithm

Aiming at the shortcoming of optimization problems in continuous space based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a novel quantum ant colony optimization algorithm is presented.In this algorithm,each ant position is represented by a group of quantum bits;a new quantum rotation gates are designed to update the position of the ant so as to enable the ant to move.Some quantum bits are mutated by quantum non-gate so as to increase the variety of ant positions.It not only proves the convergence of the proposed algorithms through theoretical analysis,but also demonstrates that the algorithm can double searching space,maintain better population diversity,rapider convergence speed and global optimal ability than the classical ant colony algorithm by simulation experiments.
Analysis of Machine Learning Techniques for Time Domain Waveform Prediction in Analog and Mixed Signal Integrated Circuit Verification

Pre-silicon analog and mixed signal (AMS) design verification involves exorbitant computing and manual effort and time to verify the design against the specification of an IC. This paper proposes a Machine Learning (ML) based behavioural model to predict the output response of AMS circuits that can be used in the automated verification process including automation of waveform review sign-off, and fast simulation models. The ML based behaviour model is constructed using the time domain features. To address both linear and non-linear behaviours of the circuit, this paper proposes a framework with statistical processing, waveform segmentation and circuit partitioning approaches as a divide and conquer strategy to identify the appropriate suite of ML algorithms. The best performing ML models in each segment are concatenated to stitch the complete response. We also propose SNR as a metric to evaluate the prediction accuracy. An Operational Amplifier (OpAmp) benchmark circuit has been used as a proof of concept to demonstrate this approach. An average SNR of 32 dB has been obtained in the prediction of the output waveform.
Attributed Graph Transformation for Generating Synthetic Benchmarks for Hardware Security

EDA methods in general and hardware security applications in particular require careful benchmarking covering nominal and corner cases of various design parameters. Attributed graph grammars have been used for generating interesting and constraint-satisfying structures in various domains of design. This paper shows how attributed graph transformation systems can be effectively adapted to automatically generate synthetic circuit structures that meet arbitrary constraints on various design parameters and how the method is flexible and scalable. We discuss the method in detail and demonstrate its utility for an example hardware security application, the sequential satisfiability attack.
Assessing the Neuroplasticity of the Brain while Performing Reach and Grasp Tasks

Neuroplasticity is an important property of the brain that facilitates functional motor recovery in impaired individuals. In order to be able to achieve improved neural communication in such individuals, there is a need to study neuroplasticity in able-bodied participants. In this paper, the absolute alpha power and power ratio index obtained from the electroencephalogram signals is calculated to observe if there is any significant improvement in neuroplasticity over trials for participants performing two reach and grasp tasks- palmar and lateral grasp. Alpha power serves as a factor to realize neuroplasticity and the power ratio index is used to compare the two types of grasps.
A Machine Learning Approach For Predicting Crop Seasonal Yield and Cost For Smart Agriculture

In our economy, agriculture plays a key component. Current scenario has plunged the agricultural sector into extensive losses leading to a lot of food shortages and cases of farmer suicides. This can be solved through the implementation of advanced scientific methods like Machine learning, Deep Learning, and Internet of Things. The Proposed framework Smart Agriculture using Seasonal Yield and Cost Prediction (SYCP) uses Machine Learning to predict the ideal crop or set of crops to be grown based on a particular season. The Model also analyzes the current market trends and predicts the approximate price of the crop for that season and geographic region based on which the farmer can decide accordingly. Random forest has been found to be suitable for both crop and price prediction. The framework has been experimented with an agricultural dataset and the results were found to be more efficient than the existing methods. This would be an efficient solution, where it offers a seasonal yield along with price prediction to improve the economy of farmers and to push them to grow.