Miracle-dz/UniFAR-FTUs huggingface dataset:
PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry

Low-feature environments are one of the main Achilles’ heels of geometric computer vision (CV) algorithms. In most human-built scenes often with low features, lines can be considered complements to points. In this paper, we present a multi-robot cooperative visual-inertial navigation system (VINS) using both point and line features. By utilizing the covariance intersection (CI) update within the multi-state constraint Kalman filter (MSCKF) framework, each robot exploits not only its own point and line measurements, but also constraints of common point and common line features observed by its neighbors. The line features are parameterized and updated by utilizing the Closest Point representation. The proposed algorithm is validated extensively in both Monte-Carlo simulations and a real-world dataset. The results show that the point-line cooperative visual-inertial odometry (PL-CVIO) outperforms the independent MSCKF and our previous work CVIO in both low-feature and rich-feature environments.
Low Rank Approximation Approach for Microwave Radar Coincidence Imaging

A low rank approximation algorithm for the Microwave Radar Coincidence Imaging (MRCI) is proposed in this paper. As a novel method of the radar imaging, the MRCI has the characteristics of high resolution and strong antiinference. Nevertheless, the imaging result will be aggravated when the Signal Noise Ratio (SNR) is low due to the illcondition of the reference matrix. Hence, a low rank approximation approach combined with conventional sparse reconstruction algorithm is proposed to improve the performance of the MRCI in the low SNR environment. First, the reference matrix is decomposed into the product of two matrices and the ranks of two matrices are analyzed. Then the low rank approximation approach is adopted and a new approximate imaging equation is estimated. Combined with SBL algorithm, the performance of reconstructed image is improved especially when the SNR is low. Simulation results are presented to validate the whole analysis and the proposed approach.
P* Admissible Thermal-Aware Matrix Floorplanner for 3D ICs

Over the past decade, three-dimensional (3D) integrated circuits (ICs) have matured as a promising solution for high-performance computing systems. Achieving high power density (>1 W/mm2), however, poses a critical thermal management challenge, as heat is prone to be trapped within the layers of the 3D structure. Early-stage power-aware floorplanning is crucial to address thermal challenges, supporting the evaluation of multiple design alternatives. In this paper, a P* admissible thermally aware floorplanning algorithm for 3D ICs is proposed. The operation principle of the algorithm is based on storing and manipulating the data of the functional blocks in a matrix form, supporting polynomial optimization and packing time. Simulation results on standard benchmarks (MCNC and GSRC) exhibit a significant improvement in key performance metrics. A reduction in area, temperature, and runtime of up to, respectively, 10.1%, 17.7%, and 89.6% are observed, as compared to the state-of-the-art.
Predictive Digital Twins for Autonomous Ships*

A predictive digital twin of an autonomous ship is a continuously updated virtual representation of the physical ship that can be used to predict and prevent potential failures. By utilizing sensor data, diagnosis algorithms, and time series prediction methods, the digital twin can predict component failure inside the ship, allowing for early detection and mitigation. In this paper, the predictive digital twin is developed in three steps: (i) by describing the dynamics behaviour of the ships based on mechanistic models, (ii) by continuously updating the mechanistic models using data from sensor systems, and (iii) by predicting the faults and visualizing the results. To this end, the mechanistic model is derived from the first principles, while the updating algorithm is developed based on an adaptive exogenous Kalman filter. Experimental validation using a small ship platform is conducted, for which the results show the method can potentially increase safety and reliability of the ship during operation.
Research on Double Closed Loop Equalization Control Strategy of Lithium-ion Battery in Electric Vehicle Based on Multivariable Coordination

In trying to address the discrepancy between single batteries in series connection of power li-ion battery packs for electric vehicles, a multivariable coordinated equalization control scheme is proposed according to the OCV -SOC curve, which is characterized by “steep at both ends and gentle in the middle”. When the State of Charge (SOC) is no less than 20% and no more than 90%, SOC is nominated as a variable in the course of equilibrium; When the SOC is less than 20% or more than 90%, voltage is selected as a variable in the equalization process. Therefore, on the basis of the cascaded cuk equalization circuit, a double-closed-loop equalization control strategy based on multivariable coordination is designed. The outer loop adopts variable theory division fuzzy control, and the inner loop adopts fuzzy PID control. The balance current is dynamically adjusted by modifying the duty ratio of MOSFET in the balance circuit. The analog results revealed that the equalization strategy can accurately control the equalization current under three different conditions of resting, charging and discharging, to achieve effective equalization of the battery group and the desired balancing effect.
Smart Contract Based Sensitive Data Hashing for Security in Healthcare Environment

A Big Data environment is a robust ecosystem in Healthcare data analysis to extract sensitive information from Medical Personal Healthcare Records (MPHR) to preserve privacy to protect sensitive information. From the data analysis context, the sensitive and non-sensitive information is identical in behavioral approach, so those sensitive items have a similar frequency of access in all security roles, leading to privacy and security especially becoming crucial issues. Thus, the existing problems are the most non-sensitive approach to dealing with the privacy standard in the form of low performance and lead time complexity. Information regarding exposure and risk-response relationships is crucial for estimating the burden of illness caused by environmental variables. The world's efforts to promote sound preventative measures through existing policies, methods, measures, methods, and knowledge can be bolstered by a better grasp of the extent to which disease and ill health is attributable to adjustable risks related to the environment. To tackle these issues, we propose an MPHR- Sensitive data prediction system based on a Pragmatic attribute Identifier Using Advance Blockchain security to Secure the Sensitive data in the big data healthcare environment. Initially, the proposed technique pre-processes the PHR information to eliminate potential errors. Then Sensitive Scaling Impact Rate (SSIR) method is used to identify the sensitive and non-sensitive marginal values. Based on the marginal values, the proposed Pragmatic Sensitive Feature Clustering Algorithm (PSFCA) is used to analyze the importance of sensitive feature relations. Next, the sensitive relation is fed into the Densenet Convolutional Neural Network (Densnet-CNN) method to classify the sensitive and non-sensitive attributes. Further, the security principle is applied based on Smart Contract Master Aggregation (SCMA) based blockchain health care security. The proposed approach outperforms existing privacy preservation in sensitive data prediction systems in terms of blockchain privacy and security, according to the findings of the experiments.
Document Image Object Detection Algorithm Based on Transformer and Mixed-MLP Network

The feature extraction network based on convolutional neural network has a strong ability to extract local features, but it is insufficient to connect the information of different spatial regions in global scope. To solve this problem, a document image object detection model using Transformer as the feature extraction backbone network is proposed. On the one hand, Transformer extracts image features and utilizes self-attention mechanism to establish global information connections. On the other hand, Mixed-MLP network consisting of channel mixing (C-Mix) layers and spatial mixing (S-Mix) layers explores information communication in spatial dimension and channel dimension. The extracted image features are represented by multi-scale hierarchical network, and feature pyramid is built to detect objects of different scales. The experimental results show that the precision of detection on PubLayNet and ICDAR-POD are 0.958 mAP and 0.952 mAP respectively, which achieve competitive results.
Evaluating the Cerebral Alterations Induced by High-Definition Transcranial Direct Current Stimulation Over the Right Ventrolateral Prefrontal Cortex in the Three-Dimensional Attention

Currently, three-dimensional (3D) virtual reality (VR) exists technical limitations, which always induces mind-wandering and impedes VR's more widespread applications. In the previous study, we have confirmed that high-definition direct current stimulation in the right ventrolateral prefrontal cortex could significantly enhance this regional neural arousal and effectively ameliorate the sustained attentional capacity of participants in the 3D VR. However, the after-effects of electrical modulation on the holistic neural connectivity are still not clarified. In this study, we further analysed the electroencephalography recordings during the 3D attentional paradigm using graph theory to explore the HD-tDCS-induced overall cerebral topological alterations.
A Simplified Model and Calculation Method for Low-Frequency and Near-Field Electromagnetic Scattering from Underwater Complex Targets

The study of low-frequency and Near-field electromagnetic scattering for complex underwater targets has high theoretical and engineering application potential in the field of underwater target detection. Currently, methods for electromagnetic scattering of complex targets are focused on high-frequency methods, and there are relatively few studies on electromagnetic scattering of complex underwater targets, and so far, there is no set of methods that can accurately and efficiently calculate the electromagnetic scattering characteristics of complex models. To address this issue, this paper first accurately calculates the electromagnetic scattering field of moving targets based on the moment method, which can well characterize the structural characteristics of complex underwater targets. Based on this, by approximating the target model to a more regular geometry for high-order approximation and reducing the spatial complexity, the method ensures accurate characteristics while greatly reducing the computational complexity. Simulation results confirm the feasibility and effectiveness of the method.
Ground Radar Target Classification Algorithm Based on Enhanced-Squeeze-and-Excitation-Multiscale Wide Residual Network

We propose a ground radar target classification algorithm based on the ESE-MWRN. The attention mechanism is introduced to improve the classification performance of the Multiscale Wide Residual Network (MWRN). The algorithm has good classification performance for human, vehicle, and UAV samples, with an overall classification rate as high as 98%.
A Review of Techniques and Policies on Cybersecurity Using Artificial Intelligence and Reinforcement Learning Algorithms

Cybersecurity is a critical process that safeguards networks, systems, and applications against cyber-attacks, wherein digital information is targeted for unauthorized access, manipulation, or destruction. As attackers continually evolve their tactics, addressing cybersecurity challenges has become paramount, especially in sensitive domains like the military and defense industries. This article delves into the challenges that artificial intelligence (AI) faces in the military domain, specifically focusing on defense applications. We review AI algorithms relevant to defense, examining their potential applications and benefits: much of this study revolves around cybersecurity in defense applications, particularly within cyber-physical systems (CPS). We explore reinforcement learning (RL) and deep RL (DRL) algorithms in CPS, aiming to enhance understanding of the cybersecurity implications in this domain. In this context, we present RL and DRL algorithms employed in cyber-attacks and their potential threats and vulnerabilities. Furthermore, we discuss how RL and DRL algorithms can be effectively leveraged for cyber-attack detection and defense applications, providing usable insights into bolstering CPS cybersecurity. By addressing both technical aspects and ethical considerations, this article offers a comprehensive view of the challenges and opportunities surrounding cybersecurity in defense applications.
Intelligent English Translation Evaluation System Based on Internet Automation Technology

To reduce manual evaluation work and improve evaluation performance, this article develops an intelligent English translation evaluation system based on Internet automation technology. The system of English translation evaluation has been developed, including the translation of the data collection, the special data extraction module, the analysis model design, and the evaluation results module. A language was created for the evaluation of English translation, and the classification of some sentences and conjunctions was calculated by the model and influenced by the users' books and documents interpret the data. As a result of feature extraction, the similarity of key points is calculated and optimized BP network is used for calculation by particle optimization. Finally, a smart test of the English translation is used. According to the experimental results, the difference between the evaluation time of the system developed in this paper is smaller than that of the existing evaluation, which shows that the work on a more sustainable design. The evaluation time of the proposed method is 4.7 seconds, and the evaluation time of the existing evaluation is 6.1 seconds; The evaluation time of the proposed method and the existing evaluation is 4.9 seconds and 5.9 seconds, respectively. The results have shown that the internet automation technology designed in this article can achieve effective English translation, and the response speed and accuracy have achieved the expected goals.
Implementation of Adaptive Kernel Kalman Filter in Stone Soup

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing–only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.
Radar Based Fall Detection with Imbalance Data Handling and Data Augmentation

Radar-based fall detection is helping seniors to live independently. In this paper, a new method for fall detection from human activities is proposed using data augmentation and class imbalance handling. Data collected from a radar signal is processed and a time series is obtained by aggregating the individual time series in the fast-time of the radar returns. This time series is used as input to several classifiers to distinguish fall from non-fall activities. To this end, we augment the radar time series data using a Mu-Sigma method. To handle the imbalanced data, we apply the synthetic minority over-sampling technique and class weighting strategy. A comprehensive study is performed to build a supervised learning method with or without data augmentation and imbalanced data handling. The performance of the proposed method is compared with some of the other existing methods using different classifiers including k-nearest neighbors, decision trees, naive Bayes, support vector machine and multi-layer perceptron. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of providing higher accuracy, precision, sensitivity and specificity values.
A Dual-Band Circularly Polarized Textile Antenna with Gain Enhancement and Bandwidth Extension

A dual-band circularly polarized flexible wearable antenna, which exhibits gain enhancement and axial ratio bandwidth extension in the high-frequency range, has been presented for off-body communication. The antenna operates in the 3.5 GHz WiMAX band and the 5 GHz WBAN band. The antenna achieves dual-band operation by exciting two pairs of odd modes, TM10/TM01 and TM30/TM03. In order to expand the axial ratio bandwidth at high frequencies, a pair of circular arc-shaped parasitic patches are loaded. Meanwhile, a metasurface structure loaded on the top of the patch antenna can be equivalently seen as a high permittivity layer to enhance the gain at high frequencies. The proposed dual-band circularly polarized antenna has a −10dB impedance bandwidth of 9.4% (3.25-3.58GHz) and 30% (4.91-6.58GHz) respectively, with an axial ratio bandwidth of 2% (3.34-3.41GHz) and 9.3% (5.52-6.03GHz), and a peak gain of 7.2dBi and 9.7dBi, respectively. The proposed antenna is made of flexible textile material and exhibits a low SAR value, making it suitable for human body applications.
Technological Evaluation and Software Bug Training using Genetic Algorithm and Time Convolution Neural Network (GA-TCN)

One crucial step in addressing bugs is assigning a proper severity rating to each report. New bugs are being reported at such a high rate that the bug repository has grown significantly in size. The bug triaging procedure has become more subjective as the size of the bug repository has grown. Therefore, the bug triaging procedure requires a classification of the severity of a problem report. Several machine learning approaches to automatically rank bugs by severity have been proposed. These system performance falls short because insufficient feature patterns are extracted for classifier training. The proposed approach consists of preprocessing, normalization and training the model. For training the model the proposed approach uses GA-TCN.
LFAC-DRU-HVDC Based Offshore Wind Power Delivery Solution

In the transmission of power from offshore wind farms (OWFs) to onshore grids, there are two main methods: power frequency high-voltage AC (HVAC) transmission and flexible DC transmission. However, these methods have limitations, such as limited transmission distance and high costs. As a result, low-frequency AC (LFAC) power transmission has become the preferred choice for far-sea wind power transmission. The diode rectifier unit (DRU) offers a low-cost and highly reliable solution to rectify the alternating current transmitted by distant offshore wind turbines into direct current. The grid-side converter of the wind turbine (WT) can supply the commutation voltage to the DRU, and the voltage and frequency of the offshore wind farm can be established. The direct current can be transmitted to the land-based AC system via an onshore modular multilevel converters (MMC) inverter. This paper presents a complete transmission scheme for transmitting power from offshore wind farms to onshore grids, which has been verified through MATLAB/Simulink simulations.
A Standard Cell Memory Based on 2T Gain Cell DRAM for Memory-Centric Accelerator Design

In contemporary System-on-a-Chip (SoC) designs, memory components typically consume a significant portion of the energy supplied to the system. To effectively reduce overall energy consumption without relying on the slowing process shrink of CMOS, energy-saving techniques that attempt to dynamically adapt to the feasible minimum voltage of the system can be adopted. Besides, near-memory and in-memory computing has also been widely adopted to reduce the significant data movement cost between the processor and the memory. However, conventional SRAMs become the bottleneck since they cannot function properly under low supply voltage. On the other hand, traditional DRAMs cannot be utilized in near-memory or in-memory scenarios due to logic incompatibility issues. One potential solution is to implement standard cell memories (SCMs) instead. SCMs can operate consistently over a wider range of voltages and are logic-compatible. However, existing SCM approaches need a large latch-based bit-cell and implement many multiplexers for word selection in the read-out circuitry, resulting in significant area overhead compared to SRAMs. In this paper, we propose a two-transistor (2T) Gain Cell DRAM based SCM for memory-centric accelerator design. The proposed SCM is fully functional under near-threshold supply voltage and maintains acceptable performance while greatly improving area cost. Gain Cells are DRAM bit cells that consist of only transistors, adopted recently in on-chip memory designs for their high-density, logic-compatible, and low-power characteristics. Our experimental results demonstrate that the proposed SCM can operate at a supply voltage as low as 0.37 V and have higher flexibility as SCM while outperforming existing SRAM and SCM in terms of area cost (1.7x to 8.2x smaller) and energy consumption (up to 27.2x).
A Reinforcement-Learning-based Agent to discover Safety-Critical States in Smart Grid Environments

The complexity of industrial systems and processes has grown significantly in recent years, due to the integration of Information Technology (IT) and Operational Technology (OT) to monitor and control interconnected equipment in critical infrastructures, improving their production processes. Smart Grids are one of the possible examples of technology enabled by IT/OT integration. However, such energy distribution systems are exposed to several vulnerabilities that make them particularly susceptible to cyber threats with critical implications for human safety. Sophisticated attacks against OT infrastructures show few observable indicators in a large timeframe and traditional fault detection methods are ineffective in discovering safety-critical states, especially in large observation and action spaces.This paper presents a methodology to identify safety-critical command patterns within a Smart Grid ICS network. In particular, a modular simulation framework that embeds physical processes simulation, industrial-specific control protocols virtualization, as well as a Reinforcement Learning algorithm has been developed. The preliminary results in training and test cases have demonstrated the modeling and learning capability of the proposed approach.