Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Study on Simultaneous Localization and Mapping (SLAM): Types, Challenges and Applications

Simultaneous Localization and Mapping (SLAM) has emerged as a pivotal research area within the realm of artificial intelligence mobile robots. Its significance lies in the ability to facilitate self-exploration of unknown environments without the aid of human intervention. The unique and distinctive aspect of SLAM is its ability to map and localize the robot’s surroundings recursively and continuously. With the advent of SLAM, numerous algorithms have been proposed to address the challenges encountered in real-world applications of this technique. The main aim of this research is to provide a comprehensive and thorough analysis of the theoretical underpinnings, recent advancements, distinctive features, implementation strategies, and emerging issues in the domain of SLAM. This exploration intends to enhance the understanding and promote the advancement of this critical research area.
Developing a Mixed Reality-Based Game for Post-Stroke Motor Rehabilitation: Combining Training and Assessment

The present study aims to develop a mixed reality-based game for post-stroke rehabilitation. The game design follows one popular game, Fruit Ninja, and stroke patients move their hands in air to cut the virtual fruit shown on the screen. Meanwhile, the patients can watch their upper limb and hand movements on the screen. The game not only supports the stroke elderly to take motor training for the upper limb but also assesses the movement quality simultaneously. The assessment focuses on three factors: range of motion, compensatory motion, and smooth motion. The assessment results are compared with the evaluation results inputted by the therapist. The value of the intra-group correlation coefficient indicates a significant correlation between the two results to verify the effectiveness of the proposed assessment method. The paper concludes with the main findings and future studies.
Prescribed-Time Tracking Control for Robotic Systems with Uncertain Dynamics

This paper presents a prescribed-time tracking control scheme for robotic systems with unknown dynamics. One salient feature is that the robotic systems can achieve prescribed-time stability with the prescribed transient performance, which is different with the traditional work that achieves uniformly ultimately bounded (UUB) and/or asymptotic stability. To realize this purpose, the prescribed performance dynamics (PPD) instead of the traditional prescribed performance function (PPF) is suggested, by which both the transient and steady-state tracking performance can be guaranteed within a specified region in-priori. To avoid using the function approximators (i.e., neural networks (NNs), fuzzy logic systems (FLSs)), an approximation-free-based prescribed-time controller is established with the inertial matrix of robotic systems and the finite-time function merely, which can not only address the system unknown uncertainties but also regulate the control error to zero in a prescribed-time. Furthermore, the Lyapunov-based theoretical analysis is conducted to prove the prescribed-time stability of the closed-loop system. Finally, comparative numerical simulation results are provided to demonstrate the effectiveness of the proposed method.
A New Method to Improve the Detection Sensitivity of Differential Magnetic-Field Probe for Near-Field Scanning

In this communication, a new method is proposed to improve the detection sensitivity of the differential magnetic-field probe for near-field scanning. In order to prove the rationality of this method, two differential magnetic-field probes (Probes A and B) with multiple parasitic loops are investigated. First, a conventional differential magnetic-field probe is theoretically analyzed and studied. Second, a pair of parasitic loops are introduced into a differential magnetic-field probe (Probe A) to enhance the detection sensitivity. Third, two pairs of parasitic loops are inserted into the differential magnetic-field probe (Probe B). The corresponding analysis is also given and discussed. Moreover, in order to verify the performance of these probes (Probes A and B), two of the simulated models are printed and manufactured based on six-layered and eight-layered printed circuit boards (PCBs), respectively. A standard microstrip line is applied for calibrating and characterizing these probes. The measured and simulated results reveal that the proposed method could significantly improve the detection sensitivity of differential magnetic-field probes.
Reinforcement Learning Based Demand Response Approach Incorporating Thermostatically Controlled Loads and Battery Loads

Currently, most demand response (DR) algorithms are static, which don't allow for dynamic demand side management based on current states of load. To solve above questions, we proposed a deep reinforcement learning (DRL) based algorithm of demand response, mainly using Actor-Critic (AC) algorithm. Firstly, we model the non-responsive load, thermostatically controlled load and battery load in buildings. Then, we establish a deep reinforcement learning environment based on Markov reward process according to the load model of buildings. Then we train and update the parameter of actor and critic network with a dataset containing 36 buildings in Hubei province. Finally, we validate the effectiveness of the DR algorithm on a sub-dataset containing 9 buildings, resulting in a significant reduction in load volatility and load power ramp in the region. Electricity consumption in these 9 buildings was reduced by 6%. In general, the proposed DR algorithm has the ability to help power systems to achieve significant power-smoothing effectiveness to increase the operational efficiency of the main grid.
A Declaration Strategy for Independent Energy Storage Considering the Coupling Relationship between Power Energy and Frequency Modulation

In order to solve the problem of electric energy storage participating in electric power spot market and electric power frequency modulation market, the coupling characteristics between electric energy service provided by electric energy storage and electric frequency modulation service were studied, and a strategy of electric energy storage participating in electric power market declaration considering the coupling characteristics of electric energy and frequency modulation was proposed. The proposed application strategy fully considers the coupling characteristics of independent energy storage participating in electric energy and frequency modulation market trading and puts forward the expected income evaluation model of electric energy storage participating in electric power spot market and electric power frequency modulation market. The optimal capacity declaration decision model of electric energy storage in electric energy and frequency modulation market is constructed with the optimization goal of maximizing expected comprehensive income. Finally, an example is given based on the actual data of an independent electric energy storage to verify the effectiveness of the proposed method.
Solderless Component Assembly: Novel Ecological Approach to Electronics Production

This work aimed to evaluate a new approach to electronics manufacturing using recycled and recyclable 3D-printed polymers as an insulation substrate and printing of conductive silver ink as a conductive pattern. Furthermore, connection to the board is realized via embedding the components into the substrate and the ink’s overprints. The first results from the measurements showed that this type of connection is comparable to conductive adhesive joints regarding contact resistance. Also, the behavior of the joints during accelerated aging by thermal shocks is relatively acceptable from a reliability point of view. A significant advantage of this manufacturing method is the absence of the high-temperature heating processes and, thus, energy savings compared to conventional production processes. Also, the final product is easily recyclable after its functional life. On the other hand, the overall quality is lower than that of standard printed circuit boards consisting of FR4 substrate and the copper conductive layer. The range of available and suitable components is also much smaller. The proposed solution could find applications primarily in low-cost electronics or prototyping.
Energy Efficient 2D-DOA Based Precoding Approach for 5G Multicell Massive MIMO System

This article presents an efficient downlink zeroforcing (ZF) precoding technique that utilizes the twoDimensional Direction of Arrival (2D-DOA) beam-spaced method to maximize the energy efficiency of multi-cell massive- MIMO systems. The approach for estimating the critical elevation and azimuth angles of detected signals from distributed sources in multi-cell massive-MIMO systems is also detailed. A mathematical model for energy efficiency of downlink ZF precoding is developed using these critical angles, and its performance is evaluated and compared to conventional 2D-DOA methods. Simulation results demonstrate that the proposed method significantly improves energy efficiency with a gain of approximately 98.3% compared to the deterministic ZF precoding method.
LNKDSEA: Machine Learning Based IoT/IIoT Attack Detection Method

The Internet of Things (IoT) brings together more devices that can communicate with one another while requiring little user input. IoT is one of the computer disciplines that is expanding rapidly, but the fact is that with the increasingly intimidating Internet world, IoT is susceptible to different kinds of cyberattacks. Practical defenses against this, including network anomaly detection, must be built to secure IoT networks. Attacks cannot be completely prevented forever, but practical defense depends on the ability to identify an attack as soon as possible. IoT systems cannot be protected by conventional high-end security solutions because IoT devices have a limited amount of storage and processing capability. This suggests the need for the creation of smart network-based solutions for cyberattacks, such as Machine Learning (ML). Although the application of ML methods in detecting attacks has numerous studies in recent years, attack detection in IoT networks has received less attention. The major goal of this study is to create and evaluate a hybrid ensemble algorithm called LNKDSEA (Logistic regression, Naïve Bayes, K-nearest neighbor, Decision tree, and Support vector machine-based Ensemble Algorithm). The proposed approach can efficiently identify IoT network attacks including DDoS, information gathering, Malware, Injection attacks, and Man-in-The-Middle- Attack. The edge-IIoTset dataset is used to evaluate the proposed model. During the implementation stage, the proposed technique is evaluated by employing binary and multi-class (6 and 15 Class) classifications of cyberattacks, and high performance is accomplished.
Study on optimization of image lag process based on 4T CMOS image sensor

The trap energy level and electric potential deviations caused by the presence of non-ideal effects in CMOS image sensor pixel cells during the manufacturing process often lead to incomplete charge transfer during the transfer of photo-generated signal charge from the PPD to the FD, resulting in image lag from the residual charge trapped in the PD, thus affects the sensor imaging effect. The commonest method to improve the image lag phenomenon is to optimize individual process parameters. In this paper, a more systematic process parameter optimization method which is simulated and optimized for the image lag aimed at two aspects: the threshold voltage implantation adjustment of TG channel and the implantation dose of N-type impurities in PPD is proposed. The optimal process state and operating conditions for minimizing image drag are obtained as follows: TG channel implantation dose 8.0e12 cm-2 and PPD N impurity implantation dose 3.0e12 cm-2. The research method in this paper provides theoretical guidelines for optimizing and improving the performance of 4T pixel design for CMOS image sensor.
A Low-Profile Broadband Dual-Polarized Omnidirectional Antenna for LTE Applications

In this letter, a broadband dual-polarized antenna with a very low profile and omnidirectional gain patterns is designed for 1.7–2.7 GHz Long-Term Evolution (LTE) mobile communications. The designed antenna is constituted by a miniaturized tapered monopole for vertical polarization (VP) and four planar dipoles arranged in a ring for horizontal polarization (HP). The HP antenna is used as the metal ground of the VP antenna, which can avoid the feeding intersection and solve the problem of impedance matching degradation when distance between the HP antenna and the metal ground of the VP antenna is close. By sharing the partial structure, the designed antenna achieves a compact size of 110 × 110 mm × 13 mm and an extremely low height of 0. 07λmin (λmin is the free-space wavelength at the minimum operating frequency). The design achieves an overlapping bandwidth of 45% (1.7–2.7 GHz) with isolation higher than 20 dB. High efficiency, low cross polarization, and good omnidirectivity in the horizontal plane are obtained. The designed antenna is fabricated and measured, and it performs a great consistency between the simulation and measurement.
A Brief Review of Machine Learning Methods used in Mental Health Research

Machine Learning (ML) is a sub-domain of Artificial Intelligence, and it focuses on the statistical methods to analyse data. Analysis of data can help in understanding the hidden patterns. As the internet is growing, one can expect a plethora of data getting generated. Almost every field, be it medicine, education, businesses across the world, stock exchange, agriculture etc., all are contributing to this data generation. Research is going on unprecedentedly on data collected for useful insights. Mental Health is one of the fields where ML is being used for understanding the patients’ behavior, symptoms, effectiveness of the treatments used and helps the medical practitioners in decision making. The presented study aims to showcase the overview of the machine learning technologies used in health care majorly concerned to mental health and depression along with their pitfalls and future directions. The presented analysis laid a foundation for future work in the domain of mental health and depression analysis using machine learning techniques. The study focuses mainly on analyzing how innovation and health could be inter-related. The challenge is to find such techniques that minimize the incorrect outcomes by the machine learning models and help the medical practitioners to take timely decisions.
Usability Enhanced Thumbnail-Preserving Encryption Based on Data Hiding for JPEG Images

As an increasing number of people tend to upload personal images to cloud platforms such as iCloud, Google Drive, and Baidu Cloud, the issue of image privacy protection in clouds has attracted widespread attention. Thumbnail-preserving encryption (TPE) is a newly proposed technology especially for balancing usability and privacy of images stored in clouds. The TPE-encrypted image does not reveal anything other than the thumbnail and size of the original one. Although many well-designed TPE schemes have been put forward, they almost all operate in the spatial domain and are not compatible with JPEG images. The only two schemes that can be applied in the frequency domain were proposed by Wright et al. and Marohn et al., but both of them have disadvantages and do not consider non-visual usability. In this letter, we propose a usability enhanced TPE scheme especially for JPEG images by utilizing reversible data hiding and image encryption in the frequency domain. We first transform the original image into the frequency domain and select some specific locations for extra information recording in every block. The original coefficient values at the selected locations are then collected and embedded into the rest of the block before encryption. Finally, the extra information is expected to be recorded into the selected locations of every block for non-visual usability. Experiments confirm that the proposed scheme achieves the target of balancing enhanced usability and privacy.
Indoor Location and Data Security of Traffic Flow based on Fingerprint Database Filtering

With the development of urbanization, the number of vehicles is gradually increasing, and the vehicle is gradually developing toward the direction of intelligence. Internet of vehicles technology is gradually entering millions of homes. In this paper, traffic flow data location and security algorithm are designed, and node data verification is carried out. This paper mainly studies the influence of fingerprint database filtering and matching methods on WiFi indoor locations based on RSSI. The results of this paper show that the designed system can transmit traffic flow data safely and accurately.
From Experiments to Insights: A Journey in 5G New Radio Localization

The 3GPP has recently made the inclusion of positioning services a priority in future generations of cellular networks, thus providing an opportunity for these networks to serve as fertile ground for the emergence of new service-based applications. With the release of 3GPP Release 16 there has been an extensive standardization effort aimed at improving positioning capabilities in 5G networks, particularly in terms of physical layer signals, measurement techniques, and network architecture. Despite these advances, the experimental evaluation of 5G positioning in real-world scenarios is still lagging behind, due to factors such as the slow integration of positioning technologies into open-source 5G frameworks, the complexity of setting up a testbed for 5G positioning with multiple base stations (BSs), and the high cost associated with such a deployment. This article offers a comprehensive review of these factors and provides a state-of-the-art overview of 5G positioning and its support in SDR-based platforms. In addition, we present guidance on the proper setup and configuration of a testbed for SDR-based multi-BS synchronized 5G positioning, the lessons we have learnt in the process, and through a series of time-based real-over-the-air measurements, we demonstrate how localization design choices can be effectively evaluated even with limited-size and low-cost SDR-based testbed.
A Variational Bayesian Dictionary Learning for Process Monitoring

Industrial process data acquisition is inevitably disturbed by noise, and data contamination makes process data carry too much redundant information, which will greatly limit the interpretation capability of data-driven modeling approaches. Given that sparse representations can effectively handle noise, a process monitoring method for variational Bayesian dictionary learning (VBDL) is developed in this work. Conventional dictionary learning requires a priori knowledge of the assumed noise variance and sparsity levels, which is not available in real industrial processes. The derived VBDL is built on a Bernoulli distribution with the beta distribution as the conjugate prior, the number of dictionary atoms and their relative importance can be inferred nonparametrically with the iterative update of the variational inference. Since the collected data exhibits temporal correlation, the large noise interference makes dynamic analysis infeasible. A low-rank vector autoregression is developed to dynamically analyze the reconstructed samples, thereby improving the robustness of the model to noise. To illustrate the feasibility and efficacy, the proposed algorithm is verified by a numerical simulation and the CSTR simulation.
XNOR-XOR based Full Adder Using Double Pass Transistor Logic

The XNOR-XOR circuit is a vital part of several arithmetic circuits. An 8T full-adder circuit and a 4T XNOR-XOR circuit are presented in this study. Due to the proposed circuits, extremely low output capacitance and minimal short-circuit power dissipation, the power consumption and area are exceptionally low. To compare quantitatively and with fewer transistors, the proposed full adder is implemented at an elaborate transistor level. The proposed full adder design framework utilizes an optimized simultaneous XNOR-XOR circuit with double pass transistor logic. The design is simulated in Cadence Virtuoso using 45nm CMOS technology. The results highlight that the proposed design consumes less power and area compared to conventional methods with optimal design.
Finding approximately Gaussian clusters via robust improper maximum likelihood

The RIMLE method is introduced for robust multivariate clustering finding approximately Gaussian clusters. It maximizes a pseudo-likelihood defined by adding a component with improper constant density for accommodating outliers to a Gaussian mixture. Existence, consistency and a breakdown point result is shown. The constant can be chosen dependently on the data so that the fitted clusters are optimally close to a Gaussian mixture. Covariance matrix constraints and computation of the RIMLE are also treated. MSC2010 code. 62H30, 62F35.
TLRNet: Tibetan Lip Reading Based on ResNet and BiGRU

Lip reading, also known as visual speech recognition, is a way of human-computer interaction based on visual information. At present, the research on lip reading mainly focuses on English and Mandarin Chinese, and there are relatively few studies on Tibetan, a low-resource minority language. Therefore, the present study proposes a specific deep learning model named the TLRNet for word-level visual speech recognition for Tibetan. The model comprises the ResNet-18 architecture, which is a residual neural network, and the BiGRU layer, a bi-directional gated recurrent unit. We train and evaluate it on the TLRW-50 dataset, which consists of fifty common Tibetan words. Our proposed model achieves Top-1 and Top-5 classification accuracies of 41.82% and 59.37%, respectively, demonstrating its potential effectiveness in recognizing Tibetan spoken words based on visual cues.
Global Optimization of Long-Term Average Proportional Fair Throughput via Convex Reformulation

Long-term average proportional-fair (LTAPF) throughput optimization through power control is a popular resource allocation problem which is typically approximated by weighted sum-rate (WSR) maximization. WSR optimization is non-convex and strongly NP-hard in general. In this letter, we demonstrate that, in fact, the original sum-log-average-throughput power control problem can be recast as a convex program and thus solved to global optimality efficiently. We also generalize our result to show that the long-term average <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-fair utility maximization problem can be recast as convex for <inline-formula><tex-math notation="LaTeX">$\alpha \in (1,\infty)$</tex-math></inline-formula>. Numerical results demonstrate a substantial gain in LTAPF throughput compared to state-of-the art algorithms used to solve the max-WSR problem.