Showing 77 results for Nn
Maryam Akbari, Sattar Mirzakuchaki, Mahdi Fazeli, Mohammad Reza Tarihi,
Volume 19, Issue 4 (12-2023)
Abstract
In light of the growing prevalence of Internet of Things (IoT) devices, it has become essential to incorporate cryptographic protection techniques for high-security applications. Since IoT devices are resource-constraints in terms of power and area, finding cost-effective ways to enhance their security is necessary. Physical unclonable function (PUF) is considered a trusted hardware security mechanism that generates true and intrinsic randomness by extracting the inherent process variations of circuits. In this paper, a novel pure magnetic memory-based PUF is presented. The fundamental building blocks of the proposed PUF design are magnetic devices, the so-called mCells. These magnetoresistive devices exclusively utilize Magnetic Tunnel Junction (MTJ) components. Using purely MTJ in the main memory and sense amplifier in the proposed PUF leads to high randomness, high reliability, low power, and ultra-compact occupation area. The Monte Carlo HSPICE simulation results demonstrate that the proposed PUF achieves a uniqueness of 49.89%, uniformity of 50.02 %, power consumption of 1.43 µW, and an area occupation of 0.01 µm2 per bit.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
Shankarshan Prasad Tiwari,
Volume 20, Issue 1 (3-2024)
Abstract
In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid. Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms.
Zahra Ahangari,
Volume 20, Issue 2 (6-2024)
Abstract
In this paper, an innovative vertical bi-channel tunnel field effect transistor is presented that exploits line tunneling mechanism to achieve improved electrical performance. In this device, the source contains germanium, while the channel and drain regions consist of GaAs., which results in a type-II heterostructure with low resistance tunneling barrier. The source region is situated in a vertical position, enclosed by two sidewall channels that encompass a broad area of tunneling. Our proposed design effectively blocks the electric field that is originated from the drain at the tunneling junction, thereby conferring high immunity to drain induced barrier thinning effect. The device that has been suggested offers a significantly greater on-state current, a factor of 144, when compared to the traditional TFET and provides a subthreshold swing of 3mV/dec and an on/off current ratio of 9.76×1010. According to statistical analysis, the design parameters of metal gate workfunction value and source doping concentration are crucial and have the potential to impact device performance. Therefore, selecting the appropriate combination of these parameters is essential. The proposed device serves as a foundation for the development of computing systems that are low in power and high in speed.
Shivanand Konade, Manoj Dongre,
Volume 20, Issue 2 (6-2024)
Abstract
The proposed research presents a two-port compact Multiple Input Multiple Output (MIMO) antenna for Ultra-Wide Band (UWB) applications. The designed antenna has two identical radiators and has an overall dimension of 20 × 44.1 × 1.6mm3 on a FR4 substrate. The designed antenna is fed by a 50-microstrip line. Extended F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. Extended F-shaped stubs are introduced in the ground plane to produce multiple resonance and high isolation between the radiating elements. The antenna offers good impedance matching in the UWB band. The proposed antenna has lower isolation < -25 dB and Envelope Correlation Coefficient (ECC) < 0.015 from 3.1 to 10.6 GHz. Antenna parameters are evaluated in term of return loss, ECC, Diversity Gain (DG), gain, Total active reflection coefficient (TRAC) radiation pattern and isolation. The proposed antenna is tested and fabricated. However, obtained results are good agreement which make suitable for UWB wearable applications.
Shamil H. Hussein , Khalid K. Mohammed,
Volume 20, Issue 3 (9-2024)
Abstract
This work presents an analysis and design of the two barrier-quantum well asymmetric spacer tunnel layer (QW-ASPAT) diodes for implantable rectenna circuits application. The RF and DC characteristic of a 10×10μm2 QW-ASPAT devices based on GaAs and In0.53Ga0.47As platform was simulated and extracted by using SILVACO atlas software. The highest extracted curvature coefficient, kv value of the both QW-ASPAT devices at zero bias was about 33V-1 compared with the standard structure GaAs/InGaAs was about 13V-1. The effects of changing in the thickness of the thin AlAs-barrier, the well width, and the spacer layer are fully investigated on the non-linear relationship between current and voltage of these diodes. A CV simulation was carried out, and it was found that the addition of the quantum-well layer between spacers and barrier reduced the junction capacitance of the QW-ASPAT device when compared with standard devices. The cut-off frequency of the proposed QW-GaAs and QW-InGaAs devices are 26GHz and 46GHz respectively. Finally, we conclude that the QW-ASPAT device is the best structure and can be used for microwave rectifiers in the miniaturized integrated rectenna systems.
Tadele A Abose, Thomas O Olwal, Abel D Daniel, Murad R Hassen,
Volume 20, Issue 3 (9-2024)
Abstract
Due to cost and energy concerns with digital beamformers, much of the beamforming is done by hybrid beamformers in mm-wave (mm) massive multiple input multiple output (MIMO). Various works in hybrid beamforming structures considered either phase shifters, switches, or radio frequency lenses individually as switching mechanisms between antennas and precoding systems. Works that consider the hybrid use of phase shifters, switches, and radio frequency lenses need further investigation since there is a tradeoff between cost and system performance in each switching mechanism. The main aim of this research is to analyze the performance of a hybrid switch, a 1-bit phase shifter, and radio frequency (RF)-Lens in a hybrid beamforming network as a switching network. Simulation results showed that the hybrid of three has a spectral efficiency (SE) performance of 59.04 bps/Hz, which increases by 6.9 bps/Hz from that of the switch and lens antenna array network. The energy efficiency (EE) of the switch, phase shifter, and lens showed a performance of 46.41 bps/Hz/W, while the switch and lens antenna array, phase shifter, and lens antenna array showed a performance of 48.52 bps/Hz/W. The result also shows that the hybrid network achieves optimum performance at the expense of higher computational complexity.
Pedram Yamini, Fatemeh Daneshfar, Abuzar Ghorbani,
Volume 20, Issue 4 (11-2024)
Abstract
With the exponential growth of unstructured data on the Web and social networks, extracting relevant information from multiple sources; has become increasingly challenging, necessitating the need for automated summarization systems. However, developing machine learning-based summarization systems largely depends on datasets, which must be evaluated to determine their usefulness in retrieving data. In most cases, these datasets are summarized with humans’ involvement. Nevertheless, this approach is inadequate for some low-resource languages, making summarization a daunting task. To address this, this paper proposes a method for developing the first abstractive text summarization corpus with human evaluation and automated summarization model for the Sorani Kurdish language. The researchers compiled various documents from information available on the Web (rudaw), and the resulting corpus was released publicly. A customized and simplified version of the mT5-base transformer was then developed to evaluate the corpus. The model's performance was assessed using criteria such as Rouge-1, Rouge-2, Rouge-L, N-gram novelty, manual evaluation and the results are close to reference summaries in terms of all the criteria. This unique Sorani Kurdish corpus and automated summarization model have the potential to pave the way for future studies, facilitating the development of improved summarization systems in low-resource languages.
Julian Herrera-Benavidez , Cesar Pachón-Suescún, Robinson Jimenez-Moreno,
Volume 20, Issue 4 (11-2024)
Abstract
This paper presents the design and results of using a deep learning algorithm for robotic manipulation in object handling tasks in a virtual industrial environment. The simulation tool used is V-REP and the environment corresponds to a production line based on a conveyor belt and a SCARA type robot manipulator. The main contribution of this work focuses on the integration of a depth camera located on the robot and the computation of the gripping coordinates by identifying and locating three different types of objects of interest with random locations on the conveyor belt, through a Faster R-CNN. The results show that the system manages to perform the indicated activities, obtaining a classification accuracy of 97.4% and a mean average precision of 0.93, which allowed a correct detection and manipulation of the objects.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
Aboubakeur Hadjaissa, Mohammed Benmiloud, Khaled Ameur, Halima Bouchenak, Maria Dimeh,
Volume 20, Issue 4 (11-2024)
Abstract
As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time.
Srinivas Babu N, Shashikiran S, M Jayanthi, Rajani N, K M Palaniswamy, M R Kushalatha,
Volume 20, Issue 4 (11-2024)
Abstract
Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. The modified CNN's improved feature extraction and classification accuracy are maintained throughout construction. To obtain good performance a TBX11K publicly accessible dataset is used it consists of 11000 images of which 4600 chest x-ray (CXR) images are considered in this research, and the suggested model is verified. This approach significantly increases the accuracy of categorizing TB symptoms. The PCA in this system locates the elements and extracts a large amount of variance technique applied to the full chest radiograph for pulmonary tuberculosis identification accuracy using SVM is 93.14% and modified CNN 96.72% respectively. When it comes to helping radiologists diagnose patients and public health professionals screen for tuberculosis in places where the disease is endemic, the proposed system SVM and modified CNN perform better than existing methods.
Haniye Merrikhi, Hossein Ebrahimnezhad,
Volume 20, Issue 4 (11-2024)
Abstract
Robots have become integral to modern society, taking over both complex and routine human tasks. Recent advancements in depth camera technology have propelled computer vision-based robotics into a prominent field of research. Many robotic tasks—such as picking up, carrying, and utilizing tools or objects—begin with an initial grasping step. Vision-based grasping requires the precise identification of grasp locations on objects, making the segmentation of objects into meaningful components a crucial stage in robotic grasping. In this paper, we present a system designed to detect the graspable parts of objects for a specific task. Recognizing that everyday household items are typically grasped at certain sections for carrying, we created a database of these objects and their corresponding graspable parts. Building on the success of the Dynamic Graph CNN (DGCNN) network in segmenting object components, we enhanced this network to detect the graspable areas of objects. The enhanced network was trained on the compiled database, and the visual results, along with the obtained Intersection over Union (IoU) metrics, demonstrate its success in detecting graspable regions. It achieved a grand mean IoU (gmIoU) of 92.57% across all classes, outperforming established networks such as PointNet++ in part segmentation for this dataset. Furthermore, statistical analysis using analysis of variance (ANOVA) and T-test validates the superiority of our method.
Dalila Yessad,
Volume 20, Issue 4 (11-2024)
Abstract
This paper introduces the CTDRCepstrum, a novel feature extraction technique designed to differentiate various human activities using Doppler radar classification. Real data were collected from a Doppler radar system, capturing nine return echoes while monitoring three distinct human activities: walking, fast walking, and running. These activities were performed by three subjects, either individually or in pairs. We focus on analyzing the Doppler signatures using time-frequency reassignment, emphasizing its advantages such as improved component separability. The proposed CTDRCepstrum explores different window functions, transforming each echo signal into three forms of Short-Time Fourier Transform reassignments (RSTFT): time RSTFT (TSTFT), time derivative RSTFT (TDSTFT), and reassigned STFT (RSTFT). A convolutional neural network (CNN) model was then trained using the feature vector, which is generated by combining the cepstral analysis results of each RSTFT form. Experimental results demonstrate the effectiveness of the proposed method, achieving a remarkable classification accuracy of 99.83% by using the Bartlett-Hanning window to extract key features from real-time Doppler radar data of moving targets.
Mousa Abdollahvand, Sima Sobhi-Givi,
Volume 21, Issue 1 (3-2025)
Abstract
This paper introduces a new method for improving wireless communication systems by employing beyond diagonal reconfigurable intelligent surfaces (BD-RIS) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-RIS represents a departure from traditional RIS designs, providing advanced capabilities for manipulating electromagnetic waves to optimize the performance of communication. We propose a DRL-based framework for optimizing the UAV and configuration of BD-RIS elements, including hybrid beamforming, phase shift adjustments, and transmit power coefficients for non-orthogonal multiple access (NOMA) transmission by considering max-min fairness. Through extensive simulations and performance evaluations, we demonstrate that BD-RIS outperforms conventional RIS architectures. Additionally, we analyze the convergence speed and performance trade-offs of different DRL algorithms, emphasizing the importance of selecting the appropriate algorithm and hyper-parameters for specific applications. Our findings underscore the transformative potential of BD-RIS and DRL in enhancing wireless communication systems, laying the groundwork for next-generation network optimization and deployment.
Sharulnizam Mohd Mukhtar, Muzamir Isa, Azremi Abdullah Al-Hadi,
Volume 21, Issue 2 (6-2025)
Abstract
The development of advanced diagnostic tools is critical for the effective monitoring and management of electrical insulation systems. This paper presents the development of an Ultra High Frequency (UHF) sensor designed for the detection of partial discharges (PD) within high-voltage substations. The study focuses on the sensor’s technical development, encompassing design considerations, fabrication processes, and initial performance evaluations in laboratory settings. The engineering principles underlying the sensor design are detailed, including the selection of innovative materials that enhance sensitivity and frequency response. The sensor configuration is tailored to optimize the detection of PD signals, with adjustments made based on simulated PD scenarios. Initial testing results demonstrate the sensor’s capability to detect a range of PD activities, showcasing its potential effectiveness in real-world applications. The sensor's performance is analyzed through a series of controlled lab experiments, which confirm its high sensitivity and broad operational frequency range. This paper not only illustrates the technical specifications and capabilities of the newly developed UHF sensor but also discusses its practical implications for improving the reliability and efficiency of PD monitoring systems in electrical substations.
Majid Golkhatab, Aref Shahmansoorian, Mohsen Davoudi,
Volume 21, Issue 4 (11-2025)
Abstract
This paper presents a novel hybrid navigation approach for autonomous mobile robots in obstacle-rich environments. The method integrates artificial potential fields for obstacle avoidance with fuzzy logic for path planning, which is optimized by a genetic algorithm to enhance adaptability and robustness to sensor uncertainties. Experimental results demonstrate significant improvements over traditional artificial potential field methods and are validated through real-time implementation on a ROS-based mobile robot.
