Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Residential Demand Response: Achieving Resilient Voltage Management with Consumer Preference

This paper aims to develop and optimize an efficient residential demand response (DR) strategy, which ensures a reliable voltage supply to customers, reduces network losses, and minimizes load switching. To maintain comfort levels, this strategy considers compensation costs for voltage management and prioritizes consumer preferences. The network voltage management involves the utilization of the modified binary teaching learning-based optimization algorithm (BTLBO) to determine the optimal combination of household appliances for switching. Additionally, the solar and wind power model is trained using the Levenberg Marquardt Artificial Neural Network (LM-ANN) technique. The proposed method is evaluated on a modified IEEE 33-bus distribution system with significant overload and high integration of renewable energy sources. Two worst-case scenarios are examined to improve network voltage magnitude during DR operations. Simulation results demonstrate that the proposed approach, incorporating direct load control, effectively improves network voltage and addresses the challenges associated with high renewable energy integration.
Collaborative Exploration and Mapping with Multimodal LiDAR Sensors

Exploration and mapping unknown environments have placed elevated demands on the spatial intelligence perception capabilities of unmanned systems. Traditional single unmanned systems suffer from issues such as low exploration efficiency. This paper introduces the LuoJia-Explorer system, an unmanned multi-robot system that can achieve autonomous collaborative exploration and mapping with multimodal LiDAR sensors. For individual agent in the system, the robot achieves tightly-coupled multi-LiDAR-inertial odometry and mapping, thereby leveraging this capability to autonomously explore unknown spaces by computing visibility maps and determining the shortest collision-free paths. On the multi-unmanned system level, the system combines the local poses and maps of individual robots to accomplish collaborative multi-robot localization and mapping. Moreover, the robots within the system exchange information regarding point cloud submaps and relative spatial exploration status, allowing for the sharing of field of view. This collaborative framework enables autonomous path planning among multiple robots. We have conducted practical validation to assess the effectiveness of the proposed system on the campus of Wuhan university. The system achieves an average point-to-point distance of 0.12m between the acquired and ground truth maps that are produced by high-precision TLS instruments. Additionally, the system exhibits a spatial exploration efficiency of 478.104 m3/s in the test scene. Our system has achieved an 86.38% increase in exploration efficiency compared to a single robot, and our system's spatial coverage rate can reach 86.70%.
A Novel Wearable Circular Polarized Antenna Design for Wireless Power Transfer Systems in E-Health Applications

In this article, a new design based on a circular polarized antenna array 2X2 for use in wearable devices is presented. The objective of this work is to implement an efficient wireless power transfer (WPT). Our approach consist to address remote medical sensors using the 5.8 GHz ISM frequency band. We have carried out a detailed simulation of a rectenna system (a combination of antenna and rectifier circuit) for wireless energy transfer. The simulation model was developed to evaluate the system’s performance in terms of transmitted power and energy efficiency. The proposed receiver antenna not only exhibits excellent performance but also achieves an impressive gain of 9.47dB. The results obtained demonstrated the feasibility of remote wireless energy transfer in the medical field, using the 5.8 GHz ISM frequency band. The results of this research contribute to the development of more advanced and autonomous wireless medical monitoring systems, enabling better quality of care and greater flexibility in medical applications.
Model Checking of Intersection Traffic Control Protocols

This study reports the results of model checking of two protocols that control vehicle traffic at intersections. One of the protocols uses a centralized controller that monitors and controls the vehicles at an intersection. The other one is a decentralized protocol in which vehicles communicates with each other with no centralized mechanism. We used the SPIN model checker to verify these protocols. Specifically, we check the safety property that no two vehicles enter the intersection at the same time if their routes are conflicting. As a result, we found a scenario in which the safety property can be violated for the centralized protocol. In addition, for the decentralized protocol, we detected a scenario where vehicles must wait arbitrarily long, which is a liveness error.
Wireless induction charging station for industrial applications

Wiring for charging LiPo batteries poses problems due to electromagnetic interference and limitations of wire length. In this work, a method for charging wireless sensor network wirelessly using NRF52840 is developed for both transmitting and receiving side of equipment. A Qi-compliant WE760308103202 wireless charging coil receiver is used and its performance evaluated to estimate optimal parameters of wireless charging, such as load and resonance frequency detuning for receiver. By tuning the frequency of operation, it is possible to utilize generic electronic components with reduced costs.
Motion Control of Underwater Robot with Four Bionic Fin

The shallow sea biological fishing robot is mainly used for the detection and fishing of marine products, with a wide and urgent application prospect. Due to the defects of spiral propulsion, a propulsion scheme of bionic fin propelling underwater robot for biological fishing in the shallow sea is introduced in this paper. Bionic fin propelling can also be used as a supplementary propulsion mode in the case of propeller damage. In this paper, a control algorithm based on central pattern generator (CPG) is presented on the basis of dynamic analysis. The simulation analysis is carried out for the vibration parameters of the fin, the force generated by the fin and the speed of the robot. Further, the swimming gait of the robot is planned. Finally, through experimental verification, the underwater robot can realize the movement function of each degree of freedom, and the propulsive force and movement speed are proven to be consistent with the theoretical and simulation analysis results.
Application of a U-Net Segmentation Model in Land Cover Classification for Use in Automated Data Prefiltering Onboard Nanosatellites

The limited physical constraints of nanosatellites due to their size, hinders their ability to transmit large amounts of image data. Because of this, the use of machine learning methods to filter data onboard has become more prominent to increase the bandwidth efficiency of these devices. By having an AI-based classification system for the images, the bandwidth necessary to transmit all these images and the tradeoff when it comes to storage, can potentially be offloaded through having a system which generates metadata that can indicate the data samples which offer the most usability, thus freeing up more space and bandwidth for these more important samples. This study explores the task of land cover classification, by utilizing one of the more prominent image segmentation models, U-Net. The model is implemented and evaluated using Pytorch using the DeepGlobe 2018 land cover classification dataset, achieving an average class IoU score of 0.68. This study seeks to support the viability of such a solution and is intended to support any future work which seeks to implement a fully automated data prefiltering system for satellite imagery.
Four-Wire Active Power Filter Based on Asymmetric Cascaded H-Bridges

This article studies a four-wire active power filter composed of three H-bridges, a four-wire three-phase converter, and four dc-links. The first three dc-links with the same voltage values, and the other assumes twice or half voltage. These asymmetrical conditions (dc-link voltage ratio 1:2 and 2:1) and adding a fourth interface inductor are investigated. These aspects affect the minimum dc-link voltage required, quality of waveforms, and compensation capacity. A new control strategy of the dc-links voltages that reduces its voltage ripple is presented in this article. The circuit model, level-shifted PWM strategy, simulation and experimental results, and comparisons in terms of efficiency, ratings of power switches and quality of waveforms are also addressed.
Traffic Monitoring Using IoT And Lambda Architecture

The aim of this paper is to conduct a study on traffic congestion and road activity monitoring, as one solution among others in the field of Intelligent Transport Systems (ITS). It presents the idea of the system as a prototype using Java simulation programs and simple IoT sensors, leading to a complete national safety system using high-tech physical devices, sensors placed at the roadside, traffic lights as well as on compatible vehicles. In addition, Big Data technologies play a major role in the system, through the use of RabbitMQ, Spark and MongoDB. The approach highlights the real-time processing aspect with the aim of having user-friendly processing given that transport is a major problem in everyday life that requires on-the-fly processing, hence the use of Spark live streaming rather than batch processing. Data integrity and availability are emphasized, as is the use of machine learning for knowledge extraction. All these factors were taken into account in the processing and storage sections. The results obtained show that the choices made are largely judicious and have achieved acceptable accuracy in terms of data volume, variety and speed.
Deep Learning Models for Low Dose CT Simulation

Finding the optimal balance between reducing radiation risk for patients and preserving image quality in CT imaging remains a challenging task. To explore the impact of lower radiation doses on CT image quality, the simulation of low dose CT images is highly demanded. Traditional methods often rely on generating Gaussian/Poisson noise on raw data using mathematical models for specific scanners, which requires access to many medical resources. Inspired from image denoising models, we propose two deep learning models (ResNet and U-Net) for low dose CT images simulation, among which the U-Net model demonstrated better performance. Moreover, the trained U-Net model proves its applicability to untrained CT images from various scanners, showcasing its potential as a versatile and generic low dose CT simulation tool. By bypassing the need for complex phantom experiments and mathematical modeling, deep learning for low dose CT simulation emerges as a promising and powerful approach, akin to solving the inverse problem of well-established image denoising techniques.
Data Synthesis and Validation Methods for Parking-Slots

Parking-slot detection plays a crucial role in autonomous driving self-parking systems. To simulate more complex parking scenarios in datasets and alleviate the challenges of manual annotation, we have developed several methods for synthesizing parking-slots under various conditions. This paper introduces three such methods: synthesizing parking-slots in AVM (around view monitor) images, generating parking-slots in fisheye images, and incorporating 2D symbols within parking-slots to create special types. To address the interference caused by lane markers and original parking-slots and to validate the effectiveness of synthetic data on public datasets, we propose a method to remove them through semantic segmentation and mask inpainting. In parking-slot detection experiments, the accuracy rate and recall rate increase by 0.44% and 0.91% respectively when we combine synthetic data with original training data. Furthermore, in another experiment involving the detection of new parking-slots with synthetic symbols, we achieve an accuracy of 96.82% and a recall of 93.83%. In order to evaluate the interference caused by synthetic data, we conduct semantic segmentation experiments on another benchmark dataset. Under the presence of synthetic parking-slots with the same markings as lane markers, the MIoU (Mean Intersection over Union) and the IoU of lane markers decrease by 0.23 and 0.57 respectively.
AMF: Efficient Browser Interprocess Communication Fuzzing

With the popularity of computers and mobile devices and the development of the Internet, browsers (applications used to retrieve and display information resources on the World Wide Web) are often included by default and have become an indispensable software. Therefore, research on browser security issues is essential for protecting information assets. Among many browsers in the industry, Chrome, as a cross-platform web browser developed by Google, occupies a large market share in desktop browsers, and its security risks are further amplified as its kernel is used by many other browsers. Therefore, the research on the security issues of Chrome browser is critical for browser security.This paper focuses on the vulnerability detection of the process communication interface in Chrome browser, and designs and implements a fuzzing framework, auto-mojo-fuzz (AMF). The fuzzing process mainly designs a sample optimization technique to ensure the effectiveness of input samples and improve the efficiency of fuzzing. After implementing the AMF solution, we evaluate the generated test samples to demonstrate the effectiveness of the sample optimization technique. We also prove the possibility of discovering more vulnerabilities with AMF, and tests it with the latest version of Chrome browser, finding five unique crashes, four of which are verified as security vulnerabilities, effectively proving the automatic and efficient ability of this framework to discover vulnerabilities in the process communication interfaces in browsers.
Skin Cancer Classification using Levy Stable Based Ensemble and It’s Real-Time Implementation on OpenVINO Toolkit

Accurate and reliable cancer categorization is crucial for informing medical decisions and improving patient care. Deep learning algorithms have emerged as a promising approach due to their ability to extract intricate patterns and correlations from large clinical datasets. In this paper, we propose a novel ensemble technique based on the Levy Stable probability density function (PDF) and deep learning methodologies for cancer classification, aiming to enhance the accuracy of cancer subtype prediction. Levy Stable model employs a robust ranking mechanism and optimizes for real-time hardware inferencing. This approach is evaluated on the Ham10k dataset. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in cancer classification. To optimize the model for real-time hardware inferencing, we utilize the OpenVINO toolkit, achieving high-performance inference rates of 110FPS and 91FPS for float16 and float32 precision, respectively, on an Intel i7 CPU.
Strengthening NoC Security: Leveraging Hybrid Encryption for Data Packet Protection

The increasing complexity and scale of modern computing systems have led to the emergence of System-on-Chip (SoC) architectures, with Network-on-Chip (NoC) architectures serving as a communication infrastructure within these systems. Due to rigid time-to-market constraints, recent SoC designs involve using third-party IPs. This outsourcing can lead to security vulnerabilities in SoC, especially in N oC packet transmission. Considering security attacks, this paper addresses the need for cost-effective secure packet transmission in N oC. We propose a hybrid encryption framework by discussing the challenges associated with symmetric and asymmetric encryption techniques to achieve a balance between security and efficient data transmission in NoC. By combining the strengths of both encryption methods, our approach aims to provide enhanced protection against security threats while minimizing performance overhead.
A QoS-Aware DRL-Based Service Switching Approach for Vehicular Edge Computing

To meet the requirements of low latency and high reliability of vehicular computing, vehicular services need to be offloaded to multi-access edge computing (MEC) servers. However, due to the mobility of connected vehicles and the limited coverage of roadside units (RSUs), vehicular services need to be adaptively switched. In order to ensure the quality of service (QoS) in the process of vehicular service switching, a service switching approach based on deep reinforcement learning is proposed in this paper. Firstly, the optimization problem model is established by considering the QoS, service migration cost, and latency. Secondly, the problem is decomposed into several subproblems and transformed into Markov decision processes (MDPs). Finally, a deep reinforcement learning-based algorithm is employed to find the optimal service migration strategy. Simulation results indicate that the proposed approach can effectively ensure the QoS compared to the baseline approaches.
Design and Analysis of Intelligent Reflecting Surface for 5G Applications

This paper presents a novel design and analysis of a unit cell for reconfigurable reflective intelligent surfaces for 5G wireless communications. The design is based on the use of Reconfigurable Intelligent Meta-Surfaces (RIMS) to manipulate electromagnetic waves by shifting the phase of the incident signal. The unit cell consists of a double negative metamaterial structure consisting of a modified split ring combined with a thin wire called “MSRR”, both on the same side of an FR-4 substrate. This cell is equipped with two PIN diodes to switch the refractive index of the incident electromagnetic wave. The refractive index of the proposed structure can be controlled between -2 and 5 and operates around the 3.5 GHz frequency of the sub-6 GHz band intended for 5G applications. Through this, the research addresses the key challenge of RIS surface design and control, offering low realization cost and easy control of the RIS surface to monitor user movement in real-time to improve signal quality. This advance has great potential for 5G applications.
Reconfigurable Rectifier for RF Energy Harvesting System at WiFi-6 Frequency Band for 2.5 V

This paper presents a novel RF energy harvesting system designed to operate within the WiFi-6 frequency band. The system incorporates reconfigurable rectifier stages, offering flexibility and adaptability. Along with the rectifier stages, it includes a comparator, a ring counter, and a Low Dropout Oscillator (LDO). The system uses TSMC 180 nm technology at 2.4 GHz and maintains a steady output voltage of 2.3 V. In this design, the rectifier stages are precisely adjusted to ensure that the input voltage for the Low Dropout Oscillator hovers around 2.5 V, accounting for a 200 mV dropout voltage. This adjustment significantly enhances the efficiency of the Low Dropout Oscillator. Additionally, the rectifier’s conversion ratio is configured to compensate for any impedance mismatch caused by variations in the input power. The impedance of the rectifier is derived through a large signal SP analysis, allowing for the determination of appropriate impedance matching network values. As a result, the system achieves a sensitivity of -14 dBm while maintaining an output voltage of 2.3 V.
Construction of Variable-Resolution Maps and Incremental Localization Techniques in Extreme Environments

In the field of autonomous driving, map construction is an extremely important technological method that provides a solid foundation for subsequent tasks such as navigation and planning. Therefore, map construction and localization have always been the key and difficult points in the field of autonomous driving. The objective of this study is to use the sensor characteristics of millimeter wave radar to achieve different levels of positioning accuracy based on the complexity of the environment in specific environments such as haze, rain, snow, and smoke, thus achieving high-reliability positioning and navigation. This study proposes a new method for map creation and navigation, which achieves variable-resolution positioning based on the complexity of the environment. This method reduces the computational complexity of map construction and maintenance and reduces the storage space of the map. The method proposed in this article significantly reduces the positioning error. For example, when k=2, the error is 26.13. However, after incorporating sequential constraints, the error decreases to 4.25. This clearly demonstrates the effectiveness of the method.
Take-Off Trajectory Optimization of a Pigeon-Inspired Flapping Wing Robot

Birds take off to provide a rapid transition between ground and airborne for predator avoidance and flight initiation. This approach could extend the range of applications for flapping-wing robots. The hard-to-model, unsteady aerodynamics lead to less accurate control of robots. In addition, the success of a take-off is predicated on the need to choose an optimal flight path while sustain the body attitude. In this paper, we present a trajectory optimization algorithm for implementing the take-off of a flapping-wing robot. To accurately model the unsteady aerodynamics, the amplitude of the passive twisting angle was estimated using load cell experiment. Subsequently, a trajectory optimization of the robot's take-off at a height of 1 m was performed with minimum energy consumption as the objective function. We validated this method using a non-linear optimization simulation and demonstrated a flight of a 280 g flapping-wing robot. The average error between the simulation and the actual flight is less than 10%, demonstrating the effectiveness of the trajectory optimization algorithm. Overall, this work paves the way towards the application of flapping-wing robots for autonomous outdoor flight.
Matrix Converters: Challenges and Solutions

The advantages of high efficiency, reliability, low weight, and loss make matrix converters an attractive option for AC/AC power conversion. This article discusses principles and techniques associated with matrix converter technology, with the view of their application to mining industry. From comparison of known control schemes, Comprehensive control is selected, for its potential to simultaneously achieve accurate load side tracking, supply side active damping and power factor correction. The article then presents a new and advanced version of Comprehensive control, with significantly improved transient response, which makes it suitable for highly dynamic mining industry applications. Advantages of the proposed control method are validated by simulation and experiment.
Applying FB-Prophet Forecasting Method on Electric Grid Systems Day-Ahead Order

The fact that power generation is one of the main sources of producing emissions and extra greenhouse gasses such as carbon dioxide and methane in the earth's atmosphere, requires power section procedures to be optimized. We have utilized a newly developed marketing tool to forecast the day-ahead order for smart grid systems. The open-source FB-Prophet forecasting model captures the main factors involved in electricity demand to forecast the power demand for the same period next day. Trend, seasonality, and holidays of the previous four years of Tennessee region power demand (TVA) is taken as a case study to apply and verify the model in the fifth year. This user-friendly tool can receive the grid characteristics and demand-related data as input and provide the user with day-ahead order amount. Optimizing the day-ahead order amount consequently minimizes the real-time power generation costs, use of fossil fuels, and low efficient real-time power generation emission.