This paper presents a proactive blockage detection algorithm and performance for sub-terahertz (sub-THz) communication systems. It is a critical technology for next-generation wireless networks. Human body blockage is a significant challenge for millimeter wave and sub-THz systems.
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Recent advancements in artificial intelligence and computer vision have led to the automation of many conventional surveillance techniques, especially in smart city applications. However, object detection systems often struggle in poorly lit environments and may suffer from slow processing speeds. To address these restrictions, this paper proposes an adaptive illumination correction technique using an enhanced Logarithmic Image Processing model. The method improves object visibility in low-light video frames and enhances detection performance. Additionally, a customized deep convolutional neural network is developed to accurately detect objects after applying the illumination correction. The combined framework is evaluated on standard datasets and demonstrates superior robustness to varying illumination conditions compared to existing state-of-the-art methods. The results show significant improvements in metrics such as accuracy, recall, precision, and F-measure, proving the effectiveness of the proposed approach.
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The essential parts of the circuit breaker working system is the relay which is susceptible to performance deterioration when operating in extreme conditions like salt spray. This can eventually impair the circuit breakers functionality and compromise the power system's ability to function effectively and consistently. This study used an existing test structure to conduct rapidly deteriorating tests on a specific design of additional switch under salt spray in order to assess the relay's lifetime condition effectively. As distinctive parameters impacted by salt spray, contact resistance, pickup voltage, release voltage, pickup time, release time and coil resistance of the relay were examined. The results demonstrated that the presence of salt spray reduced the coil effectiveness of the relay, necessitating a higher operating voltage in order to supply the electromagnetic force required for functioning. Moreover, actual and predicted lifetime from individual parameter is calculated. However, coil deterioration also led to a reduction in the electromagnetic force that the step voltage produced, which lengthened the operating period. A Random Forest algorithm model was then created that was appropriate for determining the relay status in this investigation. The existing Gradient Boosting, XGBoost algorithm is compared with proposed method to analyze the performance metrics such as accuracy, precision, recall, F1-score, R2 score, specificity, sensitivity, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) is determined.
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This paper presents an experimental exploration of position control techniques applied to a Rotary Motion Servo Plant (SRV02), employing output feedback controllers. The transfer function model of the SRV02 is derived through first principles. Output feedback controllers, specifically Position Velocity (PV) and Multirate Output Feedback (MOF), are formulated and designed. The study evaluates the efficacy of these controllers in real-world scenarios for positioning control of the SRV02. A comparative analysis between PV and MOF controllers was conducted, assessing their static and dynamic characteristics. The results, obtained from both simulation and experimental setups, illustrate the superiority of MOF over PV controller in achieving precise position control of the SRV02. The findings of this study not only validate the effectiveness of MOF in position control applications but also provide insights into the practical implementation of output feedback controllers for enhancing the performance of rotary motion servo systems like SRV02.
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Accurate and efficient road obstacle detection is crucial for ensuring the safety and reliability of autonomous and assisted driving systems. However, current object detection models, including YOLOv8, often encounter difficulties in accurately detecting small obstacles and balancing detection precision with inference speed. To address these challenges, we propose an improved YOLOv8-based detection framework featuring three key enhancements: (1) the backbone is redesigned using RepVGG modules to accelerate inference through structural re-parameterization without compromising feature representation; (2) an additional small-object detection head at the P2 feature level is introduced to significantly enhance sensitivity towards small-scale obstacles; (3) a Swin Transformer module is integrated into the final stage of the backbone to improve global contextual understanding and semantic feature extraction. Extensive experiments conducted on a comprehensive, multi-source road obstacle dataset demonstrate that our proposed model achieves superior performance with a precision of 0.812, recall of 0.782, mAP@0.5 of 0.822, and mAP@0.5:0.95 of 0.588. Additionally, it maintains a real-time inference speed of 180 FPS on an NVIDIA H800 GPU. The results confirm the efficacy of our approach, particularly in enhancing small object detection in complex real-world traffic scenarios.
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It is a real challenge to bring reliable and stable power to remote locations, critical facilities such as telecommunication towers, because it is costly and challenging to expand the conventional power distribution networks. In this paper, a new power supply system is proposed that can overcome this problem by exploiting common overhead ground wire (OHGW) energy transmission lines that run high-voltage (HV). The principle of action is that the system isolates a portion of the OHGW and applies an alternating current (AC) voltage to it by capacitively coupling it to the phase conductors of a transmission line. To take an example, a 4-km span of OHGW on a 735 kV line can produce a maximum power of 25 kW of power which is modelled as a 52.4 kV Thevenin source with the capacitive impedance as 26nF. The main problem is to achieve a stable output voltage (within 5%) across a large load range (0 to 25 kW) without the need for complex control systems, power electronics and so on. To obtain good regulation, this problem is solved using a mix of passive components, nonlinear inductances, and a unique magnetic component, IVACE. Simulation studies have proved that the system could sustain a load voltage of about 62 kV, and the system would be fault resistant and could recover fast with a load disturbance. Harmonic analysis: The value of the total harmonic distortion (THD) is 6.5 per cent at no load and 4.7 per cent at nominal load, with the third harmonics as the most important factor. This method has the advantage as compared to other means of power supply to remote regions, where expansion of the HV transmission network is not needed, and the method is a low-cost and low-maintenance method that can be implemented quickly.
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The increasing demand for energy and environmental concerns necessitate the integration of Renewable Energy Sources (RES), particularly Photovoltaic (PV) systems, into modern power grids. This paper presents an efficient grid connected PV system utilizing a dual-phase Interleaved Boost Converter (IBC) and an intelligent Maximum Power Point Tracking (MPPT) approach based on Adaptive Neuro Fuzzy Inference System (ANFIS). To enhance the controller's performance, optimization algorithms Class Topper Optimization (CTO), Falcon Optimization Algorithm (FOA), and Pufferfish Optimization Algorithm (POA) are employed. The dual phase IBC ensures reduced ripple, improved voltage gain, and stable power conversion. Simulation results in MATLAB/Simulink demonstrate that the proposed system achieves 94% efficiency, 0.95% Total Harmonic Distortion (THD), and a maximum MPPT tracking efficiency of 97.05% using the POA-ANFIS controller.
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In Wireless Sensor Networks (WSNs), sensor nodes with limited resources must manage energy consumption and network efficiency. This work proposes an AM-DRL framework that adjusts transmission power, duty cycling, and clustering methods in response to real-time network conditions. Our methodology implements Multi-Agent Reinforcement Learning (MARL) to allow distributed decision-making by individual sensor nodes, which improves network lifetime while ensuring QoS parameters such as latency, packet delivery ratio, and throughput are met. The framework steers learning on the path of the energy-efficient decisions by introducing a novel hybrid reward function that accounts for energy expenditure, network topology, and data garnered. Transfer learning allows the model to be applied to different WSN configurations with minimal retraining efforts. AM-DRL outperformed other tested methods at saving energy and enhancing network lifetime and data transmission, during extensive simulations and real-world trials, proving more efficient than energy-saving approaches using reinforcement learning, and clustering. This paper provides scalable and intelligent WSN energy optimization solutions for industrial IoT, environmental monitoring, and smart city infrastructure.
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In Wireless Sensor Networks (WSNs), sensor nodes with limited resources must manage energy consumption and network efficiency. This work proposes an AM-DRL framework that adjusts transmission power, duty cycling, and clustering methods in response to real-time network conditions. Our methodology implements Multi-Agent Reinforcement Learning (MARL) to allow distributed decision-making by individual sensor nodes, which improves network lifetime while ensuring QoS parameters such as latency, packet delivery ratio, and throughput are met. The framework steers learning on the path of the energy-efficient decisions by introducing a novel hybrid reward function that accounts for energy expenditure, network topology, and data garnered. Transfer learning allows the model to be applied to different WSN configurations with minimal retraining efforts. AM-DRL outperformed other tested methods at saving energy and enhancing network lifetime and data transmission, during extensive simulations and real-world trials, proving more efficient than energy-saving approaches using reinforcement learning, and clustering. This paper provides scalable and intelligent WSN energy optimization solutions for industrial IoT, environmental monitoring, and smart city infrastructure.
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Early detection of oil spills is crucial and essential for marine environments to minimize environmental harm and enable quick responsive measures. Oil spills can cause significant ecological and financial losses, which emphasizes the need for an efficient monitoring system. This paper presents the use of YOLO deep learning algorithms to enhance the oil spill detection speed and accuracy. A robust and high-quality dataset is taken, consisting of images extracted from Roboflow. To maximize the data quality, preprocessing techniques such as label normalization, contrast enhancement and noise reduction were used. The proposed YOLO algorithms were trained using Adam and SGDM optimizers with an initial learning rate of 0.01, 0.001 and 0.0001. Among the adopted YOLO models, YOLOv9 yielded impressive results with an mAP@0.5 of 94.45%, precision of 95.6%, recall of 93.3% and F1 Score of 94.44%. The recommended system, which incorporates deep learning technologies into marine environment monitoring, greatly improves the marine surveillance systems for oil spill detection and emergency response capabilities by enabling real-time monitoring.
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The variability in output power from renewable energy sources (RES), load and system disturbances in autonomous microgrid (MG) can lead to significant frequency deviations. Traditional PID controllers often struggle to perform adequately under these challenging conditions. To address these issues, this paper recommends a multistage PID (MSPID) controller designed explicitly for microgrid frequency management. The parameters of this controller are optimized using a Bald Eagle Search (BES) algorithm. The controller is rigorously tested on an autonomous hybrid microgrid, considering various parametric and systemic disturbances. The results demonstrate notable improvements in error reduction, settling time, and overshoot, highlighting the effectiveness of the proposed controller compared to multilevel PID(MLPID) and traditional PID controllers found in existing literature.
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The evolution of wireless communication systems has intensified the demand for compact, efficient antennas tailored to specific applications. This research introduces an S-shaped microstrip patch antenna for wireless communication, emphasizing its compactness and operational efficiency. The antenna designed and simulated using Computer Simulation Technology (CST) software, achieves a gain of 1.75 dBi, 95% radiation efficiency, an operational bandwidth of 200 MHz (2.37 GHz to 2.57 GHz), and an excellent impedance matching with compact dimensions of 29 mm × 20 mm optimized to achieve resonance within the desired frequency of 2.45 GHz and make the antenna suitable for space-constrained environments. Detailed analysis of return loss, voltage standing wave ratio (VSWR), and radiation characteristics are presented. The antenna is fabricated, and the simulated results were verified by practical measurements of a Vector Network Analyzer (VNA). The measured resonance frequency and return loss proved the appropriateness of the antenna to the desired ISM band of 2.45 GHz. Though some minor deviations were noted between the simulated and practical performance. Comparative results with recent antenna designs highlight the proposed S-shaped antenna's performance and potential for specific applications in constrained environments requiring highly efficient narrowband antennas.
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This paper presents a dual-fed approach to the design of a wide bandwidth microstrip patch antenna (MPA) of patch dimensions (l × w = 42 mm × 37 mm) and substrate dimensions (l × w × h = 100 mm × 100 mm × 3.14 mm); featuring two symmetrically inverted U-slots of dimensions (l × w × h = 25.62 mm × 15.5 mm × 2 mm) and two coaxial probes (7 mm each) adjacent to each slot. The substrate material utilized is Rogers-RT/duroid-5880, exhibits a dielectric constant of 2.2, makes it extremely efficient in reducing lossy nature of generated output parameters. The antenna demonstrates broad -10 dB impedance bandwidth ≈ 44% from 2.55 GHz - 3.85 GHz centered at a frequency of 3 GHz within the S band (2-4 GHz) region. A radiation intensity of 12 dB is observed alongside impeccable efficiency of 99.98%.
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In the context of heterogeneous cloud radio access networks (H-CRANs), there's a growing challenge in achieving efficient resource allocation due to the surge in data and complexities related to asynchrony in federated learning frameworks. This research presents an innovative methodology that blends federated averaging with asynchronous federated deep reinforcement learning (AF-DRL). By enabling individual agents to interact with distinct environments and subsequently adjust policy parameters, local updates are consolidated at a central point to generate a global update.
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Predictive maintenance is essential to industrial operations, especially in lithium-ion battery systems. Remaining Useful Lifetime (RUL) prediction is the most accurate means of maintaining optimal performance and avoiding unexpected failure in these systems. Noisy sensor data and complex degradation patterns, however, make the task complex and require sophisticated techniques for proper analysis and forecasting. Hence, this manuscript proposes an optimized Electric Vehicle (EV) lithium-ion battery RUL prediction using a multi-time scale bidirectional Long Short-Term Memory (MTS-BiLSTM) Network. Initially, input data is taken from the Remaining Useful Lifetime Prediction Dataset available on Kaggle, containing sensor readings and operational parameters for systems under degradation. It uses the trimmed global adaptive mean filter approach (TG-AMF), which enhances input data quality by removing general noise and preserving the essential feature points related to degradation patterns. A novel MTS-BiLSTM network approach is proposed for predicting RUL by capturing both Proximal correlations and Delayed dependencies in data to model trends accurately. Hyperparameters of the proposed model are optimized via the Walrus Optimizer (WO), which improves prediction and computation overhead. The robustness of the proposed framework is analyzed through benchmarking with performance metrics coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), prediction horizon accuracy (PHA), and computation time (CT). The overall PHA of 98%, MAE of 17.04, MAPE of 8.63, CT of 7.57s, and RMSE of 5.83 are obtained by the proposed method of forecasting the battery RUL for EVs
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The development of the Massive Multiple-Input Multiple Output (MIMO) system in recent years has revolutionized wireless communication, delivering significant benefits to energy and spectrum efficiency. While these strategies have been instrumental in the continuous evolution of system performance, traditional static beamforming methods (e.g. Zero-Forcing (ZF), Maximum Ratio Transmission (MRT) and Minimum Mean Square Error (MMSE)) are limited concerning their scalability and reliability on channel state information to a large extent. In this paper, we investigate the techniques of beamforming with machine learning (ML) integration to bypass these limitations and better optimize system performance. This review covers the usage of different approaches to Machine Learning: supervised learning methods (including Neural Networks and Support Vector Machines (SVM). We also study reinforcement learning techniques due to their dynamic optimization features, as well as deep-learning models like Recurrent and Convolutional Neural Networks (RNN/CNN), which are popular for treating big data or temporal dynamics. Our analysis shows several key findings: ML-based methods are effective in improving the performance of beamforming, including enhancing spectral efficiency and reducing energy consumption as well as their robustness with respect to channel state information (CSI) errors. Finally, we conclude by identifying how potential emerging trends such as federated learning and quantum computing can be positioned to overcome these challenges in the future direction of ML-optimized beamforming for massive MIMO systems.
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Along with the development of increasingly advanced technology, innovations continue to develop, one of which is in the field of transportation. In line with the rapid advancement of technology, electric vehicles (EVs) have gained significant attention due to their environmental and performance advantages. Among the EV components, the Brushless Direct Current (BLDC) motor stands out due to its high efficiency and minimal maintenance. This paper proposes a speed control system for BLDC motors based on the Fuzzy Inference System (FIS). The system was evaluated through acceleration, deceleration, and energy efficiency tests over a 2400-meter track. Results indicate that the FIS-based controller achieved smoother speed transitions and higher energy efficiency (380.95 km/kWh) compared to open-loop control (358.2 km/kWh). These findings suggest that FIS can enhance the performance and reliability of electric vehicle drivetrains.
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Digital high-pass filters play an important role in transcending the low-frequency noise to maintain the signal integrity in several modern applications of communication systems. The most important distinction between them is the attempt to obtain a flat step response and a minimum of distortion in the amplitude-frequency response (AFR). The trade-off between flat step response and the minimum amplitude-frequency response distortion in IIR high-pass filters generated by bilinear transformations of Bessel and Gaussian low-pass prototypes is explored in this paper. The proposed design uses a parallel structure, subtracting the amplitude-frequency response of the low-pass filter from the direct pass AFR to eliminate step response overshoot without sacrificing time-domain flatness. Filter performance is validated and overshoot behavior quantified using numerical evaluations performed in the Mathcad environment. Comparison of various filter orders in parallel and stage-by-stage connections shows that Gaussian-based HPFs have minimal negative step overshoot (as low as –0.002%) compared to Bessel-based filters (e.g., –0.37% for 8th order). However, AFR overshoot increases with order (72% for Gaussian, 78% for Bessel). The parallel scheme reduces the AFR overshoot by up to 30% over the conventional schemes without degradation in transient response and results in robust low-distortion filters in real-time detection applications.
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Accurate trajectory tracking is a fundamental yet challenging requirement for mobile robots, especially when operating on surfaces with varying frictional characteristics (mixed terrain) and subjected to external disturbances as well as model uncertainties. This study presents the design and evaluation of an integrated control strategy aimed at enhancing trajectory tracking performance under these demanding conditions. A Sliding Mode Controller (SMC), known for robustness but prone to chattering, was integrated with a Nonlinear Disturbance Observer (NDO). The NDO was designed to estimate lumped disturbances encompassing varying friction, model errors, and other external disturbances; this estimate was then used for compensation within the SMC control law to reduce the switching component's amplitude. The effectiveness of the proposed SMC-NDO method was verified through simulations on a mobile robot model following a reference trajectory in a simulated mixed terrain environment under various disturbance conditions. Simulation results showed that the proposed SMC-NDO controller significantly improves trajectory tracking accuracy and reduces chattering compared to the traditional SMC controller and a PID controller. The integration of the NDO with SMC proves to be an effective approach for improving mobile robot trajectory tracking, enhancing accuracy and robustness while mitigating chattering in challenging environments with varying terrain and disturbances.
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This paper presents the intend of a Tilt Integral Derivative (TID) controller for addressing the Load Frequency Control (LFC) issue in a multi-area restructured electric power system. The recommended TID controller configurations are refined by means of a narrative methodology known as the Hunger Games Search (HGS) algorithm. A multi-area electric power system including different producing units is worn to assess the efficacy of the anticipated TID controller based on HGS. The effectiveness of HGS's optimization has been confirmed to surpass that of other prominent optimization techniques, such as the Slime Mould and Artificial Gorilla Troop algorithms. The simulation outcome demonstrates that the anticipated TID controller using HGS markedly improves system frequency constancy underneath diverse load interruption scenarios.
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The statistical properties of aberrant samples are unstable, and traditional chest X-ray image data is difficult to gather and unevenly distributed. A CAE-D-GAN anomaly detection model based on dual GANs and convolutional autoencoders is proposed in this paper. We employ the DGAN module to obtain clear degraded images, the convolutional autoencoder to extract low-dimensional features from high-dimensional data, and the DDGAN module to learn how to degrade images and recover clearer images from degraded photos during the adversarial process. While the reconstruction error score identifies sample flaws, the network is optimized by the error loss between the original and reconstructed samples. The network just needs normal samples to be trained, and it can reach a maximum AUROC value of 0.86. The findings demonstrate that the CAE-D-GAN model outperforms a number of different anomaly detection models in terms of detection effects and feature reconstruction capabilities. There are special opportunities for using this approach to detect other anomalies in medical images.
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STATCOM is a reactive shunt compensator belonging to the FACTS family. STATCOM is often used for voltage enhancement, power factor correction, and harmonics mitigation. The controller adjusts the injecting voltage to achieve the required compensation. This paper offers a Neuro-Fuzzy controller based on STATCOM to improve the unbalanced voltage in the distribution network. The paper shows the ability of the STATCOM to reduce the multiple effects of unbalanced voltage and dynamic loads together. The results show the ability of the new proposal to manage the load voltage up to 95% of the minimum value.
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This work investigates the use of electrical springs (ES) to reduce energy storage requirements in modern power networks. The increasing integration of renewable energy sources has introduced greater unpredictability in power supply, necessitating advanced energy storage solutions. Electrical springs, with their real-time voltage and power control capabilities, present a promising alternative. This study explores the concepts and control design of electrical springs to demonstrate their potential for significantly reducing the required capacity of energy storage devices. Through theoretical analysis, modeling studies, and practical validation, this work shows that electrical springs can effectively balance power supply and demand, stabilize grid voltage, and reduce reliance on energy storage. The proposed control mechanisms for electrical springs are evaluated under various operating scenarios for their reliability and adaptability. Our results indicate that deploying electrical springs can lead to substantial cost savings and enhanced power system stability, thereby supporting more efficient integration of renewable energy sources.
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Energy loss in distribution networks significantly impacts system efficiency and operational costs. This work focuses on minimizing power losses and improving voltage profiles in an IEEE 33-bus system using a Genetic Algorithm (GA)-based reconfiguration and DG placement. The GA optimally places Distributed Generators (DGs) and reconfigures switches to achieve loss minimization while ensuring voltage stability. Power flow analysis is conducted using the Forward-Backward Sweep method which is introduced for better system analysis. The proposed method establishes an intelligent framework for modern power distribution optimization. A mathematical model is formulated to define power loss minimization and voltage constraints, ensuring network reliability. The Forward-Backward Sweep power flow method is employed to analyse voltage variations and system losses, while a state-space representation is introduced to model the system dynamics. The proposed GA-based approach iteratively enhances network performance by optimizing DG placement and switch configurations simultaneously, leading to better efficiency in power distribution. Results demonstrate that GA significantly reduces losses and enhances voltage profiles compared to conventional techniques.
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The inclusion of renewable energy sources (RES) into a stabilized network has opened gates to numerous optimization problems. Optimization of RES generation might not imply merely the myth of high availability but it is furthermore fenced by parameters such as transmission line power flow patterns, having the optimized location, bus nodal pricing (LMP), congestion scenario, congestion cost and reliability margins of the system (ATC,TRM). Integration of RES also drags towards the congestion episode within the transmission system. The uncertain behavior of renewable energy initiates uncertainty into the system from generation perspective. These uncertainties lead to congestion which alters the linear sensitivity factors (LSF), LMP and reliability margins of the network. The congestion scenario may jeopardize the security of transmission network hampering the limits of transmission lines. The change in marginal values reflects the occurrence of congestion with additional congestion cost. Different optimization tools have been introduced in order to optimize various objectives like optimization of generation, rescheduling generators, load curtailment etc. In this work we have presented optimization of congestion cost (i.e. Congestion Management in terms of economics) post inclusion of uncertain RES (here wind and solar source are considered) into the system. The optimization problem is resolved using Probability Optimal Power Flow (P-OPF) based Particle Swarm Optimization (PSO) technique in MATLAB-MATPOWER software with Area Based Congestion Management (ABCM).
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Blackhole attacks stance a substantial menace to Routing Protocol for Low-Power and Lossy Networks (RPL)- based Internet of Things (IoT) networks. This study investigates the impact of such attacks on key performance metrics, including power consumption and Directed Acyclic Graph Identification Object (DIO) packet delivery. Using the Contiki and Cooja simulators, we analyze the effects of scenario-based blackhole attacks under various conditions. Our findings show that blackhole attacks lead to significant increases in CPU and radio energy consumption, with CPU utilization rising by 15-20% and radio listen energy increasing by 20-25%, while idle power consumption remains unchanged. These results highlight network vulnerabilities and inform the design of more resilient, trust-centric IoT networks, particularly for critical applications like remote healthcare monitoring.
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Tele-healthcare systems must store and transmit the digital data created by the various imaging modalities. For efficient handling of these data, compression techniques that provide a greater compression ratio with notable image quality are needed. This research proposes a selective image compression technique for brain MR images leveraging the energy compaction property of the wavelet coefficients and the feature extraction and learning capabilities of CNN. The procured images undergo a hybrid filter to eradicate the possible noise in the image. The Fuzzy C-Means technique optimized using the Greywolf optimization algorithm is used to segment the clinically relevant region from the rest of the MR image. Clinically significant areas are compressed using optimized zerotree wavelet transform to ensure the reconstructed image quality. The background information is compressed using enhanced CNN models to achieve a greater compression ratio. The algorithm put forward is implemented using MATLAB 2021a, and the algorithm completion is evaluated using the BRATS 2018 brain MR image dataset. The evaluation metrics show a commendable performance over the analyzed compression techniques with a PSNR of 42.11dB, CR-29.3, and MSE of 14.37.
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Prioritizing and taking the necessary procedures to ensure human safety is critical. One of the many challenges we face in modern life is the increased risk of traffic congestion. Congestion on the streets may be problematic in large cities, especially for emergency vehicles such as fire engines and ambulances. Traffic delays are caused by a range of factors, including the weather, the time of day, the season, and unexpected occurrences such as accidents or construction projects. This creates delays in responding to emergencies, which leads to several deaths. A traffic control system limits the number of cars that can pass on a route without colliding with or impeding traffic flow. To minimize traffic congestion, a busier lane requires a longer green light than others. Systems with large traffic volumes require an adaptive system with traffic density control capabilities. A generic traffic control system may struggle to distinguish between emergencies and high-priority scenarios. This project proposes an intelligent traffic control system that prioritizes emergency vehicles utilizing artificial intelligence and image processing to address this issue. This allows these automobiles to drive through busy regions of traffic without stopping. The ESP32-CAM used in this project captures images of vehicles and then uses AI and an image processing-based model to detect emergency vehicles (Ambulances). If it identifies the presence of any emergency vehicle, the corresponding lane will be cleared till the vehicle passes the signal point. After the emergency vehicles pass the road, the system will return to normal mode.
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Low energy efficiency, misalignment-induced energy loss, limited range, and sensitivity to external variables describe Wireless Power Transfer (WPT) solutions for Internet of Things (IoT) devices. Traditional methods include inductive and RF-based WPT with uneven power distribution in dynamic environments and proximity restrictions. The proposed system dynamically changes transmission parameters and combines adaptive resonance tuning and beamforming to improve energy economy, range, and stability using machine learning (ML) for real-time adaptation. Simulation and experimental results reveal considerable increases in power transmission efficiency with the proposed system obtaining up to 95% efficiency at 1 meter compared to 82% and 88% in the existing systems. Energy loss at 1 meter is 0.15 W; at 7 meters, stability gains from just an 8% fluctuation in power output. The results provide a feasible substitute for sustainable wireless power transfer and prove the brilliance of the proposed system in long-range and dynamic IoT applications.
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