Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Object Detection and Grasp Form Classification

Intricacy is one of the challenges associated with robotic hand systems. By offering simple and efficient systems, the chance of utilizing them being rejected is reduced. The study aims to develop a deep learning-based model for automated detection of the suitable grasp form of objects. The methodology was developed using the U-Net model and image processing method, in addition to five distinct classification models. The data source is a data collection referred to as ALOI for coloured small objects. By using ground-truth labelling in four grasp forms and substituting different backgrounds, the issue of there being only black backgrounds was overcome. Two experiments have been conducted on the dataset. The accuracy and intersection over union of the segmentation algorithm, as well as the accuracy, sensitivity, specificity, and precision of the classification models, were measured to evaluate their performance. The proposed system paradigm has yielded reliable results. In particular, employing the proposed segmentation algorithm produced a significant improvement in the performance and efficacy of all five models.
Verifiable and Privacy-Preserving Ad Exchange for Smart Targeted Advertising

In this paper, we propose a novel verifiable and privacy-preserving ad exchange scheme (VPAE) for smart targeted advertising that enables an ad exchange to deliver promotional advertisements to Internet users according to their specific interests, traits, and preferences. VPAE achieves the distinguished feature to preserve private information of both user profiles and advertisers, while allowing users to verify why they receive specific advertisements for adverting transparency. By utilizing homomorphic proxy re-encryption, VPAE addresses the challenge that the ad exchange has the ability to select advertisements for users, but they cannot know more than what they should. Meanwhile, VPAE integrates polynomial evaluations to achieve that the user is capable of verifying why she receives the advertisement, without learning any information of other advertisements that she does not receive. The privacy of both user profiles and advertisers are protected without sacrificing the efficiency of profile matching. We show the computational and communication overhead of VPAE through extensive analysis to demonstrate practicality and resource needs in implementation. 1
Design of Voice Style Detection of Lecture Archives

Due to the COVID-19 pandemic, most universities endeavored to adopt online education as an alternative to conventional face-to-face classroom instruction. However, capturing students' Temporal Region of Interest (T-ROI) in long-duration video lectures poses a significant challenge. Therefore, lecture archive summarization becomes essential from an online perspective. The results of lecture archive summarization still require further improvement. This research aims to distinguish T-ROI using a speech processing approach h. Our plan is divided into collecting instructors'/presenters' voice datasets, clarifying the T-ROIs through sound processing technology, and building a suitable deep neural network architecture to detect the T-ROIs in the actual lecture archives automatically. We will inevitably encounter various challenges to achieve the objective, such as individual differences. This article describes the experimental dataset collection design considering individual differences and lecture room environments. It summarizes how such efforts will be effective in realizing personalized voice style detection and improving the accuracy of speech processing in real environments.
A Self-Calibrated Activation Neuron Topology for Efficient Resistive-Based In-Memory Computing

In-Memory Computing (IMC) accelerators based on resistive crossbars are emerging as a promising pathway toward improved energy efficiency in artificial neural networks. While significant research efforts are directed toward designing advanced resistive memory devices, the nonidealities associated with practical device implementation are often overlooked. Existing solutions typically compensate for these nonidealities during off-chip training, introducing additional complexities and failing to account for random errors such as noise, device failures, and cycle-to-cycle variability. To tackle this challenge, this work proposes a self-calibrated activation neuron topology that offers a fully online non-linearity compensation for IMC accelerators. The neuron merges multiply-accumulate operations with Rectified Linear Unit (ReLU) activation function in the analog domain for increased efficiency. The self-calibration is integrated into the data conversion process to minimize overheads and be fully online. The proposed activation neuron is designed and simulated using 22 nm FDSOI CMOS technology. The design demonstrates robustness across a wide temperature range (-40°C to 80°C) and under various process corners, with a maximum accuracy loss of 1 LSB for an 8-bit activation accuracy.
Influence of multitasking-induced mental workload on electrodermal activity and human cortisol

Current work environments have undergone a noticeable shift towards higher cognitive demands and increased involvement in multitask performance. It has therefore become crucial to investigate the levels of mental workload experienced by operators. Simulated cognitive multitasking, with varying workload intensity, duration and work order, was tested in 18 volunteers to determine its impact on task performance, perceived workload, galvanic skin response; GSR, and cortisol levels in sweat and saliva. The results showed that as workload intensity increased, there was a concurrent increase in perceived workload and a significant negative impact on task performance. As time progressed, there was a notable increase in performance. Differences in GSR can be observed based on time on task and order of stressor levels delivered to the participants. Preliminary results of cortisol levels in sweat and saliva showed distinct trends in relation to workload intensity.
Optical Reconfigurable Intelligent Surfaces Aided Optical Wireless Communications: Opportunities, Challenges, And Trends

As communication technology continues to develop, the demand for capacity and spectrum resources is also increasing. With the abundant spectrum resources and high energy efficiency, optical wireless communication (OWC) has become one of the key technologies in future wireless communication systems. However, due to the high frequency characteristics of optical signals, traditional OWC systems, such as free space optical communication (FSO) and visible light communication (VLC), suffer from low degrees of freedom, low robustness, and low energy efficiency, which limits its application. Optical reconfigurable intelligent surface (ORIS), as a new type of optical communication device, can change the direction, shape, amplitude, phase, and other characteristics of the optical beam. ORIS is expected to improve the defects and expand the application scenarios of OWC systems. In this article, we provide an overview of ORIS technologies and architectures, study typical cases of ORIS-aided OWC systems, and highlight key challenges in this area.
Uptake of primary and secondary nutrients in sorghum under irrigation with different qualities of water

The gradual decrease of fresh water resources is le ading towards the inevitable use of saline water for irrigation. Using saline waters for irrigation, there is needed to take management practices to prevent the development of excessive soil salinisat ion for crop production. A field experiment was carried out to investigate the effect of saline water irrigation and management practices on uptake of primary and secondary nutrients. At harvest, the si gnificantly highest total N, K, S and Mg uptake was recorded by C 2-FYM, followed by C 3-FYM and C 3/C 4-FYM. Regarding P and Ca total uptake, among water quality levels, the significantly highe st uptake was recorded by C 2 water followed by C 3 water and alternate irrigation with C 3/C 4 water. Among management practices, significantly t he
A recursive deconvolutional kernel for ECG

Convolution is a fundamental technique in biomedical signal processing, but deconvolution requires a more dedicated processing technique than the convolution. Previous iterative Krylov subspace methods or division in the Fourier domain might be inappropriate for high-precision applications. To the end of stable and efficient deconvolution, pseudoinverse reconstruction could be an ideal deconvolution technique. However, pseudoinverse reconstruction through singular-value decomposition (SVD) requires excessive memory to save the singular vectors. In this study, we proposed a recursive deconvolution kernel using the pseudoinverse without SVD. We applied the deconvolution kernel to the PhysioNet electrocardiography (ECG) data with a Gaussian convolution kernel.
Diagnosis and Warning Method for Internal Overheating Defects of GIS Isolating Switches on the Basis of Multi-point Temperature Monitoring

Due to its fully enclosed form and complex internal heat transfer processes, it is difficult to monitor the temperature field in the event of internal abnormalities in GIS equipment, which makes it challenging to quickly diagnose and warn internal defects. This issue seriously affects the safe operation of equipment. This paper focuses on the problem of overheating defects in GIS equipment and designs a multi-point temperature monitoring scheme for the shell, conducting related simulation experiments. By monitoring external temperature key points, the experiment acquires time series data for multiple temperature points. Combined with the Long Short-Term Memory (LSTM) model, it fully explores the time correlation between heat points and multiple temperature points. By training with multiple time steps, it obtains a prediction method for the temperature of the internal heat point in GIS equipment and judges the severity level of the temperature data obtained. Through experimental data analysis, this method can more accurately, conveniently and stably diagnose overheating defects in GIS equipment compared to traditional heat monitoring techniques. The method is applicable to different overheating defect application scenarios and can effectively improve the overheating detection capability of power equipment, ensuring the safe operation of GIS equipment.
Downlink Transmit-End Beam Refinement for mmWave Communications Under Mobility

This article discusses a method for adjusting the radiation pattern of a base station antenna array when conducting radio communications in the millimeter wavelength range for mobile user devices. The article describes a technique for selecting a training sequence of beams that minimizes the Cramer-Rao lower bound when estimating the transmission direction. The proposed technique makes it possible to reduce the beam adjustment time for transmission by 16 times compared to the conventional protocol, which improves the quality of service for highly mobile users.
Assessment of the Performance of Inverse Class-F Power Amplifiers in a Discrete Doherty Architecture

This work presents an assessment, at simulation and experimental levels, of the performance of inverse class-F power amplifiers in a Doherty architecture. Two connectorized amplifier modules, designed for standalone operation, are adopted to construct a quasi-balanced Doherty architecture exploiting 3-dB 90° hybrid couplers at the input and output to demonstrate the concept. The Doherty architecture shows competitive performance at 1.8 GHz, with 43 dBm output power and around 60% efficiency from saturation to 6 dB output power back-off. The performance is in line with the state of the art of integrated load-modulated amplifiers, demonstrating the validity of the approach.
Bidirectional Edge-Based 3D Scene Graph Generation from Point Clouds

The study of 3D scene understanding is a crucial area in computer vision and has received much attention. While ignoring the relationships between objects, current research on 3D scene understanding mostly focuses on object-level knowledge. In recent years, 3D scene graph has become an effective tool for attaining greater comprehension and perception of the surroundings. However, the long-tailed distribution of the training data causes existing 3D scene graph prediction models to yield sub-optimal scene graphs. In this paper, we provide a straightforward but effective 3D scene graph prediction model. Experiments on the 3DSSG dataset demonstrate that the model described in this research when compared to the baseline model, can increase relationship prediction's effectiveness and accuracy while successfully reducing the long-tailed distribution impact brought on by dataset labeling.
Monitoring Agricultural Land Degradation in Egypt Using MODIS NDVI Satellite Images

Land degradation is one of the root causes of declining agricultural productivity globally. The aim of this study is mainly directed to monitoring and assessment the agricultural land degradation in Egypt using the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) with spatial resolution 250 m during the period from December 2010 to May 2012. The results indicated that the agricultural land degradation during 2011 was about 95269 feddan, changed to about 27000 feddan in May 2012. The highest land degradation was found in New Valley Governorate and the highest impact was found in South Sinai Governorate where it lost more than 10% from their total agricultural area. (Khalil A.A.; Y.H. Essa; M. K. Hassanein. Monitoring Agricultural Land Degradation in Egypt Using MODIS NDVI Satellite Images. Nat Sci 2014;12(8):15-21). (ISSN: 1545-0740). http://www.sciencepub. net/nature . 2
Optimization of Home Health Vehicle Routes

Home health care (HHC) defines the set of services that are provided to a patient at home. They have the advantage of being less expensive and less stressful for patients. However, this practice requires in most cases the displacement of a medical staff for a patient's home intervention, which generates the problem of effective management at lower cost of these visits. Internet of Things (IoT) technologies is enabling the medical sector as well as the transportation sector to witness a huge change in the way they operate. The implementation of intelligent solutions, building a bridge linking the Health 4.0 sector to that of Intelligent Transport Systems (ITS), allows the resolution of new problems and guarantees an improvement in the quality of services. This article provides a vehicle routing solution for home healthcare based on CPLEX solver. The proposed solution provides efficient route optimization for vehicles to assist patients in their treatment.
Asymmetric Electrical Structure in the Mantle Beneath the East Pacific Rise at 17°S

The magnetotelluric component of the Mantle Electromagnetic and Tomography (MELT) Experiment measured the electrical resistivity structure of the mantle beneath the fast-spreading southern East Pacific Rise (EPR). The data reveal an asymmetric resistivity structure, with lower resistivity to the west of the ridge. The uppermost 100 kilometers of mantle immediately to the east of the ridge is consistent with a dry olivine resistivity structure indicating a mantle depleted of melt and volatiles. Mantle resistivities to the west of the ridge are consistent with a low-melt fraction (about 1 to 2 percent interconnected melt) distributed over a broad region and extending to depths of about 150 kilometers. The asymmetry in resistivity structure may be the result of asymmetric spreading rates and a westward migration of the ridge axis and suggests distinct styles of melt formation and delivery in the mantle beneath the two plates.
Smart Agents for Academic Studies on Scale Model Grid

A 30kW laboratory scale model electric power grid was re-engineered to use more modern distributed data acquisition in the form of a Multi Agent System (MAS) with a laboratory-wide timing pulse to serve for measurement synchronization similar to GPS signals used in industrial synchrophasor equipment. The scale model power grid is useful in an electric power academic environment for teaching concepts, providing a hands-on tool for students, and to serve as a testbed for researching new ideas in the ever evolving “smart grid”. Various elements of the design of the MAS Smart grid are presented highlighting unique circuit designs, a state machine control algorithm, distributed timing pulse synchronization, and Smart Meter protection. Three test cases of the grid are presented highlighting autonomous generator connections, synchronized measurements, and power flows.
A New Vision of the Role Played by Buffer Dopants on the Operation of AlGaN/GaN HEMTs

This paper presents a detailed vision of the role played by buffer doping in the operation of AlGaN/GaN HEMT devices. The study employs transient two-dimensional TCAD simulations of a drain-to-source voltage step-up response to corroborate the physical description of buffer currents and their associated buffer trapping effects in three distinct cases of trap densities in the buffer. The results show that the trap centers in the buffer are essential in achieving proper HEMT operation. The concentration of traps not only effectively influences the drain-to-source current value but also plays a specific role in enhancing the confinement of the 2DEG channel, thereby enabling effective control of the device current through gate-to-source voltages. Finally, it is shown how important it is to introduce and differentiate the channel current from the buffer current in order to provide hints on how to create a better compact model.
Trajectory Planning for Autonomous Overtaking on Curved Roads with Avoidance of Human Driving Vehicles and Static Obstacles

A proposed obstacle avoidance trajectory planning method for autonomous vehicles considers social behavior characteristics to address the interaction problem with human drivers in mixed traffic environments. The method integrates graph theory and centrality functions to classify and predict driver behavior, employing model predictive control (MPC) to plan the autonomous vehicle's trajectory. By combining these social behaviors, the method ensures safe completion of the driving task based on probability. Experimental results from an interactive obstacle avoidance scenario in a curved environment confirm the successful completion of the task, validating the proposed method's feasibility and effectiveness, supporting its potential for real-world application.
Enhanced VINS-Fusion-Based SLAM Algorithm for Reliable Camera Pose Estimation in GNSS-Challenging Environments

Simultaneous Localization and Mapping (SLAM) is a prominent research area in robotics. This paper presents a SLAM algorithm based on VINS-Fusion to address the challenge of estimating camera pose in GNSS-challenging environments. The algorithm incorporates a method for evaluating the reliability of GNSS measurements to optimize their usage, and performs online optimization of the GNSS lever arm. These enhancements improve the robustness and positioning accuracy of the system in GNSS-challenging scenarios. The GNSS measurement quality evaluation method consists of two layers: the first layer is situated at the input end of GNSS measurements and utilizes a dynamic threshold method to monitor the quality of the input measurements; the second layer is employed after the system completes graph optimization and employs a chi-square test to identify all GNSS outliers present in the optimization graph. Experimental results demonstrate that the proposed algorithm outperforms the VINS-Fusion algorithm significantly in GNSS-challenging environments. In dataset testing, the root mean square error (RMSE) is reduced by an average of 76.3%.
Trajectory Planning for Safety-Critical Driving Scenarios based on Dynamic Programming

Autonomous Vehicles (AVs) are critical, and nowadays are essential technology that transfers traditional transportation systems into smart, and driverless ones. Considering the available processed data and information that came from localization, perception, and HD map, AVs could exceed the limit of the efficiency and safety of those conventional systems. AVs could use enhanced techniques and algorithms to execute safety-critical scenarios in dynamic environments that have static and dynamic obstacles, traffic signs, …, etc. This paper aims to propose a novel motion planning algorithm that guarantees the safety of the AV in such complex scenarios. This algorithm is based on the extracted data from the HD map and the constrained nonlinear dynamic programming technique. The constraints will be extracted and fed to the optimizer which oversees generating the optimal safe control points within the safe search space. A simple car model is being used for the equations of motion for the AV. Moreover, this formulation guarantees that the entire trajectory is constrained and safe. Finally, the planning safe trajectory problem is simulated. Moreover, this formulation guarantees that the entire trajectory is constrained and safe. Finally, the planning safe trajectory problem is being simulated.
An energy router with current-limiting function structure

The core component of the energy router is dual active bridge (DAB) DC transformer, for its short circuit fault current rise rate is fast, the peak value is large, resulting in the DAB internal power electronic device blocking problem, this paper proposes a current-limiting type structure of the energy router with current-limiting type structure, the structure of current-limiting device from the high-voltage side of the H-bridge of the DAB connected to a single-phase rectifier bridge, and then connected with the current-limiting inductor at the output port. When a short-circuit fault occurs on the output DC side, the current-limiting energy router uses a model predictive control strategy to rectify the current, provide clamping voltage for the current-limiting inductor to limit the peak value of the fault current, and cooperate with the circuit breaker to realize the arc-free opening, which avoids blocking of the power electronic devices in the DAB, and quickly realizes the current limiting and isolation of the fault. The model of current-limiting energy router based on 10kV/750V DC transformer is constructed in the simulation system, and the simulation results show the effectiveness and feasibility of current-limiting energy router fault current limiting and isolation.
Dual-Mode Stepped-Impedance Resonator with High Signal Suppression

A dual-mode stepped-impedance resonator with noise elimination is presented in this paper. The dual-mode resonator is designed as a meander input/output coupling port and still has a compact circuit structure. This circuit structure can also have a good passband in the operating frequency and eliminate interference from the required band. The operating response of the resonator circuit at a central frequency is 2.1 GHz. The passband of the proposed resonator has a loss of insertion at 0.52 dB and a loss of reflection at 30 dB. The frequency range of 2.5 to 5.5 GHz is suppressed by increasing the folded input/output feed, resulting in a 20 dB rejection.