Bulletin of Electrical Engineering and Informatics
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Ultra-low-power super class-AB adaptive biasing operational transconductance amplifier with enhanced gain for biomedical applications
Get PDFThe operational transconductance amplifier (OTA) proposed in this article is a bulk-driven (BD), single-stage, super-class-AB, adaptive biasing, functioning in the subthreshold region (ST) with an enormously low power supply of ± 0.25 V, providing high-gain. The input core of the OTA circuit is composed of adaptively biased BD differential input pairs based on flipped voltage follower (FVF), which drive in class-AB mode with a partial positive feedback (PPF) approach. The circuit additionally employs FVF and self-cascode (SC)-based low-power current mirror loads at its output to obtain significantly high gain and unity gain frequency. In addition, using adaptive loads based on source-degenerated metal oxide semiconductor (MOS) resistors raises dynamic current more efficiently, consequently improving the slew rate and unity gain frequency (UGF) without drawing additional power. Employing the cadence spectre tool and the UMC 0.18 μm complementary metal oxide semiconductor (CMOS) process technology, the designed OTA has been simulated. The simulation outcomes substantiate that the amplifier provides high open loop DC gain of 75 dB, 18.75 kHz UGF with a phase margin of 63.93º, and input-referred noise (IRN) of 0.734 µV/Hz0.5 at 1 kHz frequency. The proposed OTA consumes just 60.15 nW of power. The performance results confirmed that the proposed OTA circuit is appropriate in biomedical applications
Enhancing quality measurement for visible and invisible watermarking based on M-SVD and DCT
Get PDFThis study introduces an advanced method for evaluating non-blind watermarking quality, leveraging both visible and invisible watermarking techniques grounded in principles of discrete cosine transform (DCT) and modified singular value decomposition (M-SVD). The primary focus is to refine the assessment process of watermarked images by integrating M-SVD, known for its efficacy in measuring image quality and watermarking performance. Results from the M-SVD implementation exhibit a striking resemblance to the original images. The mean squared error (MSE) values for watermarked images range from 0.0003 to 0.0168, while peak signal-to-noise ratio (PSNR) values vary between 42.52 dB and 82.72 dB. These outcomes underscore the potential of DCT and M-SVD techniques in bolstering watermarking processes, especially in invisible watermarking contexts
Empowering hate speech detection: leveraging linguistic richness and deep learning
Get PDFSocial media has become a vital part of most modern human personal life. Twitter is one of the social media that was formed from the development of communication technology. A lot of social media gives users the freedom to express themselves. This facility is misused by users, so hate speech is spread. Designing a system to detect hate speech intelligently is needed. This study uses the hybrid deep learning (HDL) and solo deep learning (SDL) approach with the convolutional neural networks (CNN) and bidirectional gated recurrent unit (Bi-GRU) algorithm. There are 4 models built, namely CNN, Bi-GRU, CNN+Bi-GRU, and Bi-GRU+CNN. Term frequency-inverse document frequency (TF-IDF) is used for feature extraction, which is to get linguistic features to be analyzed and studied. FastText is used to perform feature expansion to minimize mismatched vocabulary. Four scenarios are run. CNN with an accuracy of 87.63%, Bi-GRU produces an accuracy of 87.46%, CNN+Bi-GRU provides an accuracy of 87.47% and Bi-GRU+CNN provides an accuracy of 87.34%. The ability of this approach to understand the context is qualified. HDL outperforms SDL in terms of n-gram type, where HDL can understand sentences broken down by hybrid n-gram types, namely Unigram-Bigram-Trigram which is a complex n-gram hybrid
Observer-based single phase robustness load frequency sliding mode controller for multi-area interconnected power systems
Get PDFIn multi-area interconnected power systems (MAIPS), all the plant state’s measurement is stiff due to the lack of a device or the cost of the sensor is expensive. To solve this restriction, a novel sliding mode control technique- based load frequency controller (LFC) is investigated for MAIPS where the estimation states of the system is utilized fully in the switching surface and controller. Initially, a single-phase switching function is suggested to dismiss the reaching phase in traditional sliding mode control (TSMC) approach. Secondly, the MAIPS’s unmeasurable variables is estimated by using the suggested observer tool. Next, a new single phase robustness load frequency sliding mode controller (SPRLFSMC) for the MAIPS is established based on the support of the observer instrument and output data only. The entire plant’s stability is ensured through the Lyapunov theory. Even though the plant’s variables are not measured, the obtained results in the simulation display that the frequency remains in the nominal domain under load instabilities on the MAIPS. The simulation results for a three-area interconnected electricity plant verify the preeminence of the anticipated SPRLFSMC over other current controllers with respect to settling time and overshoot
Optimization of a CH3NH3SnI3 based lead-free organic perovskite solar cell using SCAPS-1D simulator
Get PDFIn this study, a CH3NH3SnI3-based perovskite PV cell with the structure (FTO/TiO2/CH3NH3SnI3/Cu2O) was made and optimized by changing the layer thickness, defect density, and doping profile using the solar cell capacitance simulator (SCAPS) 1D simulator. To better understand how the device interface affects carrier dynamics, a synergic optimization of the device is done by altering the electron-transport layer (ETL) and hole-transport layer (HTL) materials. The light glows through the window layer of Sn2O: F, which serves as the transparent conducting oxide layer in our suggested cell construction and then travels over TiO2 as an n-type ETL. Due to its unique features, the p-type perovskite (CH3NH3SnI3) is chosen as the primary absorber layer. Lastly, Cu2O is added as an HTL before the back contact because it has a higher hole conductivity and the proper offsets for spreading the valance and conduction bands. Additionally, Cu2O-based devices outperform frequently utilized spiro-OMeTAD-based devices in terms of efficiency. According to the findings of these simulations, the optimized structure has a power conversion efficiency (PCE) of 41%, an open-circuit voltage of 1.32 V, a short-circuit current density of 34.31 mA/cm2 and a fill factor (FF) of 90.5%. Additionally, the optimized structure has a short-circuit current density of 34.31 mA/cm2
Performance evaluation of generative adversarial networks for generating mugshot images from text description
Get PDFThe process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification
Chaotic ant colony algorithm to control congestion and enhance opportunistic routing in multimedia network
Get PDFThe creation of wireless multimedia networks imposed wireless devices that can retrieve multimedia material such as video and audio streams, still photos, and scalar sensor data from the environment is made possible by the availability of low-cost devices. This approach considers the issues of routing packets across a multi-hop network consisting of several traffic sources and links when ensuring bounded delay. The exits of an obstacle create several geographic routing issues, for example, congestion and delay. This article, chaotic ant colony algorithm (CACA) to control congestion and enhance opportunistic routing (CAOR) in multimedia network, is proposed to solve these issues. This mechanism uses the CACA algorithm to detect the obstacle and transmit the data packets on the obstacle edges optimal nodes. Moreover, an opportunistic routing (OR) selects the best forwarder by the forward aware factor (FAF) from the forwarder list (FL). The FAF measures node energy, node received signal strength indication (RSSI), available bandwidth (AB), and packet transmission rate for choosing the best forwarder. Experimental outcomes demonstrate that established delay, energy utilization, and throughput performances are greater than the conventional mechanism
Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller
Get PDFIn this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithm-based approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system
An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units
Get PDFThe game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively
Real-time object detection and distance measurement for humanoid robot using you only look once
Get PDFHumanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4’s efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human–robot interaction capabilities via data transmission