Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Control for Un-Tripped Rollover Prevention of Heavy Commercial Road Vehicles

Heavy Commercial Road Vehicles (HCRVs) are more prone to rollover owing to their higher centre of gravity. A rollover prevention objective is formulated in this study as a Model Predictive Control (MPC) problem. The proposed MPC formulation combines the objectives of restricting load transfer ratio, a key indicator of a rollover, with the prevention of wheel lock. The controller was experimentally evaluated using a Hardware-in-Loop (HiL) setup for test manoeuvres based on National Highway Traffic Safety Administration (NHTSA) standards. The developed MPC controller effectively prevents rollover using differential braking while avoiding wheel lock.
Vehicle Routing Problem Solving Using Reinforcement Learning

In this study, the Vehicle Routing Problem (VRP) is solved using reinforcement learning (RL) approaches. In order to service a group of consumers efficiently, the VRP involves identifying the shortest, most cost-effective, and fastest routes for a fleet of vehicles. With large-scale instances and dynamic situations, traditional methods for solving the VRP confront difficulties. A promising solution to this issue is provided by RL, a type of artificial intelligence. The suggested RL-based VRP solver learns to make wise routing decisions based on observable environmental information by utilizing RL algorithms such as deep Q-networks (DQN) or proximal policy optimization (PPO). Experimental results show that the RL-based technique is effective, producing competitive or better solutions than conventional approaches, especially for complicated VRP situations.
Design and Analysis of a Non-Isolated Multi-Input DC-DC Converter for Electric Vehicle Applications

In this paper, a multi-input non-isolated DC-DC converter design for use in electric vehicle (EV) applications is described. The converter may simultaneously be connected to two separate power sources. The converter descriptions, various operation modes using equivalent circuits as well as in analytical waveforms are provided. Furthermore, the proposed converter will achieve high conversion ratios while yet exerting minimum stress on its capacitors and semiconductors. The artificial neural network (ANN)-based intelligent perturb and observe (P&O) algorithm drives the proposed MPPT technique, which aims to minimize oscillations at the maximum power point (MPP). The MPP's current and voltage values for solar irradiation and temperature are offered in advance by the ANN. The P&O calculates the optimum duty cycle for the proposed technique to accurately track the photovoltaic (PV) MPP based on the supplied current and voltage. The proposed converter experimental has been constructed and verified effectively in a laboratory environment.
Landscape metrics application in ecological and visual landscape assessment

: The development of landscape-ecological approach application in spatial planning provides exact theoretical and empirical evidence for monitoring ecological consequences of natural and/or anthropogenic factors, particularly changes in spatial structures caused by them. Landscape pattern which feature diverse landscape values is the holder of the unique landscape character at different spatial levels and represents a perceptual domain for its us-ers. Using the landscape metrics, the parameters of landscape composition and configuration are mathematical algorithms that quantify the specific spatial characteristics used for interpretation of landscape features and processes (physical and ecological aspect), as well as forms (visual aspect) and the meaning (cognitive aspect) of the landscape. Landscape metrics has been applied mostly in the ecological and biodiversity assessments as well as in the determination of the level of structural change of landscape, but more and more applied in the assessment of the visual character of the landscape. Based on a review of relevant literature, the aim of this work is to show the main trends of landscape metrics within the aspect of ecological and visual assessments. The research methodology is based on the analysis, classification and systematization of the research studies published from 2000 to 2016, where the landscape metrics is applied: (1) the analysis of landscape pattern and its changes, (2) the analysis of biodiversity and habitat function and (3) a visual landscape assessment. By selecting representative metric parameters for the landscape composition and configuration, for each category is formed the basis for further landscape metrics research and application for the integrated ecological and visual assessment of the landscape values. Contemporary conceptualization of the landscape is seen holistically, and the future research should be directed towards the development of integrated landscape assessment as a guideline for spatial development planning.
Enhancing IoT Security: A Full-System Simulation Dynamic Taint Analysis Framework for Firmware

Dynamic taint analysis is a common and efficient technique in program analysis. IoT devices are widespread and generally have weak protection, making them a hotspot for vulnerabilities. Although some dynamic taint analysis tools and frameworks have been proposed for IoT firmware, they all suffer from one or more issues: performance degradation, lack of generality, or being limited to user mode only. We propose a cross-platform, full-system simulation dynamic taint analysis framework for IoT firmware, Firmware Dynamic Taint Analysis Framework (FDTAF). FDTAF provides a novel Virtual Machine Introspection (VMI) combined with bit-level taint propagation at TCG layer of QEMU. Additionally, we provide analysis tools for the generated taint data flow to improve the usability of dynamic taint analysis when analyzing IoT devices. The implementation of FDTAF includes 1680 lines of C++ code, 9490 lines of C code, and 320 lines of Python code. We present a comparison of the applicability of FDTAF and DECAF in firmware analysis and validate the practicality of the analysis framework using real-world vulnerabilities.
Contactless HR Measurement from Facial Videos Using Alternative Color Spaces with CEEMDAN

Monitoring vital signs non-invasively by analyzing the blood volumetric variation visible on the skin surface captured in the recorded video frames has recently gained popularity. However, the performance of contactless vital sign measurement can easily deteriorate due to noise interference, resulting in information loss. This paper proposes a new framework for contactless HR measurement. First, in order to improve and retain the image texture quality, a denoising method based on a fractional filter is used. Then, six different color spaces are explored, and the most suitable color channel is chosen from each respective color space to strengthen the cardiac information. The chosen color channels are decomposed using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method followed by the principal component analysis to obtain the pulsatile signal resembling blood volumetric changes. The experimental analysis is performed on two publicly available datasets to assess the efficacy of the proposed approach. The obtained mean absolute error values are 1.57 beats per minute (bpm) for the PURE dataset and 2.11 bpm for the COHFACE dataset. The experimental results demonstrate that the proposed method outperformed other state-of-art approaches by providing promising results.
Extending Shor's Quantum Error Correction Circuits Using Quantum Adders

One of the critical limitations quantum computers are yet to overcome is quantum errors. Efforts have been made in quantum error correction fields to overcome them. However, since we are bound by the technology of this noisy intermediate-scale quantum era, it is important that we focus on the efficiency and optimization of our error correction circuits. In this paper, we present a way of extending quantum error correction circuits using quantum adders by utilizing the notion of majority voting over all the qubits. We use adder circuits and a comparator to achieve majority voting. We demonstrate how our proposed methodology exhibits a linear trend in the required number of quantum gates. Further, we confirm the accuracy and efficacy of our proposed methodology for optimizing the number of quantum gates through extensive simulation in IBM's Qiskit and compare them with existing literature.
Fruit and Vegetable Classification Using MobileNet V2 Transfer Learning Model

Fruits and vegetables possess considerable economic worth as genetic resources in agriculture. The motivation for this research comes from the necessity to achieve accurate and efficient classification of fruit and vegetable images. This study proposed a MobileNetV2 model architecture for categorizing fruits and vegetables into 36 groups. The aim is to explore the capabilities of this model, which has already demonstrated promising outcomes in earlier studies. The work utilizes a dataset including 3,825 photographs, and the model undergoes extensive training for ten epochs, employing a batch size of 32. The training process is optimized using the default learning rate parameters and the Adam optimizer. The investigation findings demonstrate a notably favorable outcome, indicating that the proposed MobileNetV2 model achieved an impressive accuracy rate of 96%. These discoveries will have a significant influence on the food and agriculture sectors. Developing a highly accurate and efficient automated system for classifying fruits and vegetables holds significant promise in revolutionizing various aspects of agricultural monitoring, quality assurance, inventory management, and retail automation. This study contributes to developing resource-efficient and sustainable practices in the food and agriculture sectors by enhancing categorization accuracy. Ultimately, both consumers and the industry are poised to benefit from the potential of reducing waste, improving product quality, and increasing the availability of high-quality fruits and vegetables.
Precoding Design of Reconfigurable Massive MIMO in the Electromagnetic Domain

Reconfigurable massive multiple-input multiple-output (RmMIMO) is expected to unlock the unexplored degrees of freedom in the electromagnetic (EM) domain, offering more flexibility to the design of communication systems. Specifically, optimizing the delicate EM properties such as radiation pattern depends on detailed domain knowledge. Traditional spatial domain channel state information (sCSI) has obscured the underlying EM domain information of the channel, thereby hindering the base station's efforts in exploring the EM properties of the channel for precoding optimization. This paper exploits the detailed EM domain channel state information (eCSI) for radiation pattern design at the base station. Using the orthogonal decomposition method on the spherical harmonics bases, the radiation pattern is decomposed into the linear combination of these orthogonal bases. We then formulate the pattern design problem as a problem of optimizing projection coefficients on these bases. Specifically, we propose a manifold optimization-based method that can alternatively optimize the radiation pattern design and the digital precoder. Simulation results demonstrate that, in comparison to traditional mMIMO systems with fixed antenna radiation patterns, RmMIMO architecture induces significant throughput gain for downlink transmission.
Channel Estimation for Intelligent Reflecting Surface-Assisted Wireless Energy Transfer Network Using Only One-Bit Feedback

Acquiring the wireless channel state information (CSI) is an essential task to reap the wireless system performance gain brought by intelligent reflecting surface (IRS). In this paper, we study an IRS-assisted wireless energy transfer (WET) network, where an energy receiver (ER) harvests the wireless energy transmitted from an energy transmitter (ET) with the help of an IRS. Different from the commonly adopted wireless CSI acquisition approaches such as pilot or codebook based methods, we propose a novel channel learning method that requires only one-bit feedback information from the ER. Specifically, each feedback bit indicates whether the increase or decrease of the harvested energy amount at the ER within the present interval as compared to the previous one. Based on the feedback information, the ET continually adjusts its transmit beamforming in subsequent channel learning intervals to help infer the cascaded ET-IRS-ER CSI. It is worth noting that an optimization technique named analytic center cutting plane method (ACCPM) is applied in the channel learning phase. Numerical results unveil that our proposed one-bit feedback based channel estimation method is able to effectively estimate the cascaded ET-IRS-ER channel, and greatly reduce the requirement on the hardware complexity of the ER simultaneously.
Analysis of Public Sentiment on Dhaka Metro Rail with Transformer Based Architectures

Dhaka Metro Rail is a mesmerizing initiative of the Bangladesh Government that has brought comfort to people. This project got a huge appreciation from people for its continuous service. Mass people use the train for rambling, going to the workplace, etc. Again, for some unwanted circumstances, Dhaka Metro Rail gleaned negative criticism from various public.In this research, we analyzed public sentiment on Dhaka Metro Rail from popular social media using recent NLP architectures like BERT, XLNet and Electra, where BERT outperformed than other with an accuracy of 91.11% in correctly identifying the sentiments of the public comments.The sentiment of a comment is identified into predefined categories, such as positive, negative, or neutral.
A depth completion method based on cross-feature extraction window

A Dense depth maps with clear object boundaries are very important for robot mapping and navigation. However, today’s depth sensors can only produce sparse depth data, so it is necessary to complete them. Traditional methods mostly rely on RGB images as an auxiliary modality to provide color information in the depth-filling process. However, RGB by itself is not enough to provide all the required color information, so we add semantic maps as a guide on this basis. In addition, we add a cross-feature extraction window (CFEW) based on the attention mechanism to the feature extraction stage of this model. The essence of the window is to use the channel attention mechanism, the spatial attention mechanism, and the mutual features attention mechanism to enhance the important features and suppress the irrelevant features, which can enhance the border of the object in the environment.
Stock Price Prediction During Pandemic Situation

Accurately predicting the stock value enables investors to earn more money, reducing their uncertainty on whether to buy or sell. Again during the COVID-19 period, many companies have shown a different picture of the stock market situation. That is why investors cannot consider the company’s exact status in the stock market. The primary objective of our paper is to predict the future behavior of the stock market in the event of a pandemic using machine learning classification. To consider the future stock market condition, first, we looked at the past stock market condition and tried to make predictions by collecting data from two companies. Second, we tried to understand what happened in the stock market during the pandemic and used machine learning algorithms. Finally, make predictions through machine learning classifications by merging the data during the pandemic with past data. In conclusion, we have attempted to identify what was lacking in our instance and provide a concise description of the next steps.
Transmission Design for RadCom System under The Spectrally Crowded Environment

This paper investigates the RadCom system with multiple input multiple output (MIMO) structure under mutual spectrally crowded environment, where the radar system acts as the primary function, while the communication system is the secondary function. An alternative formulation aims at maximizing signal-to-interference-plus noise ratio (SINR) with multiple constraints to control the peak-to-average power ratio (PAR), spectrally compatibility and transmit power. The design degrees of freedom are the space-time waveform for radar and communication system. Then we develop an optimal scheme that tackles the original non-convex formulation into a series of sub-problems with alternating iteration, Taylor expansion, and the double alternating direction method of multipliers (DADMM) design. Numerical examples are performed to assess the merits of the derived solutions.
Optimizing Client and Data Selection in Federated Learning: A Centralized Optimization and Decentralized Game-Theoretic Approach

Federated Learning (FL) is a distributed machine learning approach that enables multiple individual devices (clients) to collaboratively train a global machine learning model without directly sharing their raw data with each other. By keeping the raw data on local devices, FL can effectively preserve data privacy and security. However, the performance of FL system is highly dependent on the amount and quality of client data, as well as the relevance of data from different clients. In this article, we investigate the client and data selection problem in FL system from both system and individual perspectives, while considering the impacts of data quality and data relevance. Specifically, from the system perspective, we establish a centralized optimization problem that aims to optimize social welfare in a centralized manner. That is, a central controller decides on the amount of each client's data to be utilized for the FL system, aiming at maximizing the overall social welfare. From the individual perspective, we formulate a two-stage Stackelberg game for incentivizing clients to contribute their data and resources to the FL system in a decentralized manner. In the first stage, the FL server acts as the game leader and specifies a reward mechanism. In the second stage, each client acts as a game follower and competes for the reward by deciding on the amount of data to contribute to the FL system, aiming at maximizing its individual payoff. We analyze the centralized optimization problem and the Stackelberg game systematically for both low and high data relevance scenarios. In particular, we derive the closed-form optimal solution and game equilibrium for the low data relevance scenario, and propose iterative algorithms that effectively converge to a suboptimal solution and subgame equilibrium for the high data relevance scenario. Simulation results verify that data relevance has a significant negative impact on the system performance. That is, the social welfare achieved through centralized optimization and distributed Stackelberg game approaches in the high data relevance scenario is only 19.7% and 24.0%, respectively, of those achieved in the low data relevance scenario.
Skin Cancer Detection Using Deep Block Convolutional Neural Networks

One of the most common and potentially fatal forms of cancer, skin cancer is on the increase across the world. The likelihood of a positive outcome from therapy increases dramatically with early and precise diagnosis. This is especially true with deep learning approaches. This research introduces the use of Deep Block Convolutional Neural Networks (DB-CNNs) as a unique method for detecting skin cancer. The suggested DBCNN model was trained and tested using a large dataset consisting of photos of various skin lesions. DB-CNNs are built to take advantage of the hierarchical characteristics of skin lesions, capturing the complex patterns and textures that are essential to a correct diagnosis. The model makes use of a chain of convolutional layers, supplemented with blocks trained to extract features and establish spatial relationships. By combining these modules, the network can better identify nuances in skin lesion features, leading to more precise diagnoses. This study explores several data preparation techniques, such as image enhancement and normalization, to determine whether any of them may strengthen the model's robustness and generalizability.
PlantHealthAI: An Integrated System for Plant Disease Detection, Severity Prediction with Knowledgebase Chatbot Support

Plant Disease detection and severity estimation is crucial for mitigating the impact on crop yield. This research focuses on the development of a deep learning-based model to detect the incidence level of early leaf spot disease in groundnut crops. A real-field dataset is created for evaluating our methodology by inducing the early leaf spot disease to susceptible plants in a controlled and observatory environment. The developed model achieved a precision and recall rates of 98 % and 95 %, respectively. Additionally, the developed severity estimation model with the custom metric demonstrated promising results, with a maximum absolute difference of 0.5 between the ground truth severity measure and the predicted severity measure. Furthermore, a user-based query feedback system backed by a comprehensive knowledge base was integrated, enabling users to acquire information on various aspects of the detected disease. This feature enhances the usability and accessibility of our solution, empowering farmers to make informed decisions for effective disease management and unlocks new potential in promoting sustainable crop protection practices and thereby positively impacting the agricultural sector.
Face Reconstruction from Sketch Using Deep Learning

Face reconstruction is a critical task in computer systems, with applications in fields such as forensics, entertainment, and healthcare. In this article, we propose a fresh method for face reconstruction using sketches and deep learning techniques. In our approach, a human artist's drawing is used to train a neural network to create a realistic 3D representation of a face. Our method's main benefit is that it makes it possible to perform accurate and efficient face reconstruction without the need for complex 3D scanning equipment or extensive manual labor. Sketch-based face reconstruction using deep learning techniques, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), Data augmentation techniques to improve the robustness and accuracy of the model, including rotation, scaling, and translation of the input sketches. Incorporation of prior knowledge about facial anatomy and features to guide the reconstruction process.
A Machine Learning Based Image to Object Detection and Audio Description for Blind People

Autonomous driving may enhance road safety, traffic congestion, and the environment. However, several obstacles prevent autonomous vehicle adoption. The work converts visuals into audio for blind people. The visually challenged need this project because it uses machine learning to recognize objects in photographs and provide audio descriptions, bridging the accessibility gap and giving them meaningful access to visual information. This program uses machine learning to translate visual material into aural information to help blind people feel included and independent in a more equal society. The Image Collection, carefully selected, has over 8,000 different photos with item names and audio explanations to help the visually handicapped. The hybrid strategy combines VGG-16, CNN, and NLP. CNN identifies things quickly, VGG-16 classifies well, and NLP synthesizes speech. Multimodal transformation effortlessly synthesizes selected text into audio descriptions, harmonizing modalities for accessibility after training and testing. Visualizing the design using a VGG16 model graph yields 95.80% recognition accuracy. This breakthrough and smart glasses may revolutionize object detection and give the blind control over their lives. It stands out amid a sea of efforts and shows a genuine commitment to help the blind.