Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Clock Synchronization Scheme for Wireless Sensor Networks against DoS Attacks

This paper investigates the clock synchronization problem of wireless sensor networks with time-varying noises and malicious denial of service (DoS) attacks. A robust and secure clock synchronization scheme based on filter is proposed in intermittent DoS attack case, which is composed of three steps: relative skew estimation, clock skew compensation and software clock reading compensation. Through the clock skew compensation, the synchronization error of the clock skew can be constrained into a bounded range. Through the software clock reading compensation, the possible error of the software clock reading can be compensated and the clock synchronization can be achieved completely. In addition, extensive simulations are conducted which validate that the proposed scheme outperforms the existing ones.
Cascade sliding mode control based on extended state observer method for a DC-DC buck converter

A cascade sliding mode control (CSMC) based on extended state observer (ESO) is proposed for a pulse width modulation based DC-DC buck converter system subject to disturbances in this paper. First, a novel tracking error state space model is introduced to define the matched and mismatched disturbances as a uniform matched disturbance. Second, an extended state observer is designed to estimate the system states and total disturbances, including load disturbances and input voltage variation. Third, the novel cascade sliding mode controller is constructed for the DC-DC buck converter based on the estimate values. In addition, the detailed theoretical stability analysis of the proposed controller is analyzed by a Lyapunov stability criterion. Finally, compared with traditional sliding mode control based on ESO (SMC-ESO) method, simulation results show that the CSMC-ESO method not only has the advantage of easy parameter tuning, but also performs a better disturbance rejection ability.
Preassigned Finite-Time Control of High-Order Nonlinear Systems with Time-Varying Powers and Full-State Constraints

This paper studies adaptive preassigned finite-time control of high-order nonlinear systems with unknown timevarying powers and full-state constraints. Neural network approximation is employed to remove frequently used assumptions imposed on unknown system nonlinearities. By introducing lower and higher powers, finite-time performance functions and nonlinear transformed functions into dynamic surface control design, full-state constrained controller is designed without imposing feasibility conditions on virtual controllers in traditional barrier Lyapunov function-based control methods. It is proved that all the closed-system signals are semi-globally bounded, full-state constraints are not violated, and system states converge to prescribed small regions around zero after a preassigned finite-time.
Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

With the development of Internet of Things (IoT) technologies, environmental monitoring systems using wireless sensor networks (WSNs) have received considerable attention. Reliable object detection and tracking is an important research issue in various WSN applications, such as environment and disaster monitoring, disaster propagation tracking, and intruder monitoring and tracking. Generally, because batteries are used as energy sources for sensors in WSNs, a highly energy-efficient operation is needed to prolong the life of the sensors and networks. To save energy, sensors usually manage multi-mode sensing operations, in which they periodically switch between active and inactive periods. A tradeoff exists between object detection accuracy and energy efficiency when we select a sensing schedule. Depending on the object speed, direction, and sensor deployment topology, different sensing schedules should be dynamically applied to individual sensors. In this paper, we propose a novel recurrent neural network (RNN)-based dynamic duty cycle control method for sensor nodes. For RNN training, a target optimal duty cycle for a given network condition is derived from the proposed digital twin-space analytic solution. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.
Battlefield Situation Deduction and Maneuver Decision Using Deep Q-Learning

As the pace and complexity of modern warfare accelerates, it is of great significance to apply intelligent technology to defense decision-making. Due to the high dynamics and randomness of the aircraft, traditional methods are difficult to solve the optimal control strategy. The characteristics of reinforcement learning match the difficulty of the problem. In this paper, the situation of hypersonic aircraft is deduced, and the deep reinforcement learning method is used to make autonomous penetration decisions in the reentry phase. The model of aircraft and environment is established, and the maneuvering decision-making model is established based on deep Q-learning and its optimization algorithm. Through a large number of simulation training, this method can effectively give the real-time decision output of the agent and make a good prediction of the situation. It has the ability of short-range accurate operation and long-term planning and prediction. This method can improve the probability of successful penetration, and can be used as the decision-making basis of glider penetration.
Research on Model for Vital Signs Estimation Based on Convolutional Neural Network*

This paper introduces a model for vital signs estimation, mainly, the heart rate, breathing rate and body movement, based on convolutional neural network (CNN). Due to the limitation of hardware acquisition system and detection method, the traditional measurement method with ballistocardiogram(BCG) signals or electrocardiogram(ECG) signals is difficult to meet the requirements of high accuracy and use convenience at the same time. Aiming at this problem, this paper, which uses high precision BCG data as label value, improves the data forecast model, including the use of deep learning approach for data prediction, and the appropriate pretreatment process data, using piezoelectric ceramic signal, which records the BCG signals, to carry on the forecast, building a forecasting model for vital signs using CNN. Low complexity and good precision of the model, making it accessible for both the convenience and data accuracy of the entire vital signs monitoring system.
An End-to-End Trajectory Planning Strategy for Free-floating Space Robots

The traditional trajectory planning methods of free-floating space robots have the dynamic singular problem and the difficulty of accurate modeling. Although learning-based approaches have achieved remarkable performance on such task, they mainly focus on single modular design such as the perception, planning, or control part. Optimization-based end-to-end method can well combine perception, planning and control, which not rely on parameters of the dynamic model and reduce the difficulty of manually adjusting modular controllers’ parameters. Therefore, we developed an end-to-end trajectory planning strategy based on optimization with multiple constraints. The whole strategy consists of several multi-layer neural networks and is optimized by a deep reinforcement learning algorithm based on maximum entropy. The results of visualization show that our strategy can capture the information of robotic arm from vision directly. Moreover, we evaluate the kinematic and dynamic features of the system and testify our strategy in anti-interference experiments. The performance of our strategy demonstrates the availability and robustness of the system.
Multi-sensor Measurement Information Fusion for Maneuvering Target Recognition and Tracking

This paper proposes multi-sensor information fusion algorithm for maneuvering target recognition and tracking based on mobile sensor networks (MSNs). In order to improve target recognition accuracy, the modified evidence combination rule is designed to obtain the identity information of target based on the Dempster-Shafer (DS) algorithm. The target recognition process is designed by two stages, namely, sensing stage and fusion stage. During the first stage, each sensor node in the MSNs can obtain the feature information of the target, which makes up the measurement information. In the information fusion stage, each sensor combine the information exchanged from neighboring nodes with local measurement information to obtain the high-precise identity information of the maneuvering target based on the improved DS algorithm. Then, the identity information can be used to obtain the accurate state estimation of the maneuvering target. For the purpose of obtaining the accurate state estimation of target, the target tracking process is designed by local filtering stage and fusion stage. Finally, a numerical simulation is designed to illustrate the effectiveness of the algorithm.
Parametrization and Core Temperature Estimation of Lithium-Ion Batteries for Thermal Management

The analysis of battery core temperature is critical for the battery management system (BMS) to protect the li-ion batteries from thermal runaway reactions which can lead the batteries to potential damage, but directly it is difficult to measure. This experimental study proposed a method to estimate the core temperature based on the two-state lumped thermal model using a Kalman filter. As an improvement, the effect of changes in entropy and overpotential on heat generation are analyzed. The constant current charge/discharge experiments are carried out using lithium-ion batteries to determine the parameters of the thermal model and to estimate the core temperature. For estimated results validation and model construction, temperatures are measured with thermocouples built inside and outside of the battery cells. The accuracy of the core and surface estimated results are validated with a mean absolute error of around 0.1 Celsius and 1.6 Celsius respectively.
Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

The resource-constrained camera integrated Visual Sensor Networks (VSN) have conquered numerous visual aided services from visual surveillance to habitat monitoring. VSN is capable of sensing, processing and communicating visual data wirelessly. These networks are built with inexpensive low power sensor motes with a lightweight processor, limited storage, and bandwidth. The huge amount of redundancy present in the images makes the processing and communication consume more energy than expected. The number of bits must be reduced using energy-efficient compression techniques for efficient transmission. Low computational energy and communication energy are always favored for an increased lifetime of the wireless sensor network. The highly sensitive and self-descriptive nature of images makes security in VSN even more critical. In this work, we propose an energy-efficient low bitrate secured image coder for resource-constrained VSN. Light weight design protocols are highly required in secured image transmission over VSN. Through this communication, we also propose a novel chaotic map using Pascal’s triangle. The system follows a unique interleaved fashion of compression and encryption process to consume less computational resources. A series of tests were carried out to validate the secured image coder’s ruggedness and its suitability in VSN. The performance and the strength of the low bitrate secured image coder are tested with compression efficiency and cryptanalysis tests. Simulations were carried out in Atmel’s ATmega128 processor for energy consumption analysis. The energy consumed by the proposed system for compression, encryption and transmission of an image of size 512 $\times512$ is 109.364mJ (milli Joules), which is only 4.57 % of the energy consumed by raw image transmission. In addition, the system is implemented in real time image sensor platform based on Arduino Due board integrated with OV7670 camera module for real time verification and the experimental results validated the suitability of the indicated scheme in real time VSN.
Lane Detection and Lane Departure Warning Using Front View Camera in Vehicle

There has been a significant change in trends in the automotive industry over the last ten years. Vehicles equipped with different Advanced Driver Assistance Systems (ADAS) are increasingly present on the roads, whereas the level of their autonomy increases over time. For autonomous vehicles to work properly, it is necessary to have reliable ADAS that process different input signals from distinct sensors. One of the most important ADAS algorithms is that intended for lane detection (LD) and lane departure warning (LDW). In this paper, a new algorithm for LD on the road and LDW, which processes only images obtained from the camera located at the front end of the vehicle, is proposed. The algorithm provides information on the number of detected lane lines in the image and their position on the image while marking the current driving lane and the first two adjacent lanes if they exist. If the vehicle is departing from the lane, a corresponding warning message is shown to the driver. The algorithm was tested on a set of 12 video sequences (17552 frames in total) recorded during day and night in different weather conditions. The results showed that the algorithm achieves high performance in most cases, while for some challenging cases there is room for further improvement.
An Improved Fast Search Multi-objective Genetic Algorithm for Airline Crew Scheduling Problems

Most of the existing studies about airline crew scheduling problems focus on single-objective optimization or multi-objective optimization under simple constraints. In this paper, we propose an airline crew scheduling model based on a large number of constraints in actual scenarios, with multiple objectives for both saving airline company’s cost and improving the balance of crew working time. We then introduce an improved fast search multi-objective genetic algorithm, which is able to quickly find the feasible domain around optimal solution when confronting with a large number of crew data and constraints. We compare the results of our algorithm with the multi-objective greedy algorithm, which verifies the effectiveness of our algorithm on solving the airline crew scheduling problem.
Laser Scan Matching based on Normal Distribution Transform using Efficient Point Cloud Segmentation

The Normal Distributions Transform (NDT) is currently a very practical laser scan matching algorithm. In this paper, a NDT variant algorithm is designed for SLAM which contains a ground point remover and a effective point cloud data segmentation. Its primary goal isto improve the robustness oftheNDTalgorithm. By extracting points belonging to the ground and segmenting 3D scan data into different objects, the proposed method solve the problem that the cost function of the standard NDT is not continuous enough. Simultaneously, the computation speed of the algorithm increased significantly. Final ly, experimental results are presented to illustrate the effectiveness of the proposed algorithm.
Minimum Fuel Consumption Strategy in Autonomous Adaptive Cruise Control Scenarios

Fuel consumption is a crucial challenge for ACC (Adaptive Cruise Control) scenarios. This paper focuses on a synthesized controller of ACC based on a PnG (Pulse-and-Gliding) strategy to reduce fuel consumption in a car-following situation. A switching logic module is proposed for real-time control. A gear shifting algorithm and braking logic are added to the controller concerning the real following situation and structure of powertrain. Simulations are implemented in Matlab/Simulink and the results demonstrate that, compared with a MPC (Model Predictive Control) based benchmark controller, the synthesized controller could save fuel up to 14.5%. The significant fuel saving attributes to keeping the working point at or around at the best fuel economy point of engine. The algorithm could be implemented to some heavy commercial vehicles.
Improved Distributed Optimization Algorithm for Continuous Time Multi-agent Systems With Time-Varying Cost Function

This paper mainly discusses the improved distributed optimization problem for first-order multi-agent system with time-varying cost function. The goal of this paper is to make all agent’s states reach agreement in finite time and to minimize the global objective function. Each agent is individual and only corresponds to a gradient information. Firstly, we improve the existing distributed optimization algorithm with time-varying cost function. Secondly, by constructing some new Lyapunov functions and using the methods of convex analysis, sufficient conditions for all agents to reach consensus and to be derived to optimal solution are given, where the optimal solution is a trajectory that changes over time. Finally, Some numerical simulation examples are given to verify the effectiveness of the theoretical results.
Dual-Beam Leaky-Wave Antenna Based on Dual-Mode Spoof Surface Plasmon Polaritons

In this letter, a dual-beam leaky-wave antenna (LWA) based on dual-mode longitudinal asymmetry (LA) spoof surface plasmon polaritons (SSPP) is presented. The dispersion curves of the LA SSPP unit cell are extracted and analyzed. On the basis of obtained dispersion curves, it is found that two different modes can be simultaneously excited using the LA SSPP unit cell and each mode can be independently manipulated by groove depth at two sides. By exciting the LA SSPP unit cell, two radiation beams can be generated with proper modulation. Different from the traditional dual-beam LWA, whose two radiation beams relies on both −1st and −2nd space harmonics, the proposed design method can independently control two radiation beams by using only −1st space harmonic, avoiding the radiation beam, which is difficult to control generated by −2nd space harmonic. In details, the radiation angles of the two beams can be easily controlled by introducing different modulation period in the forward and backward regions respectively, which increases the flexibility of dual-beam applications. The detailed design process is presented in this letter and a prototype of the proposed dual-beam antenna is simulated and measured. The measured results are in good agreement with the simulated results, strongly indicating validity of proposed method.
A new metric for evaluating the importance of nodes in complex product structure networks

The importance of nodes is a significant metric for the structure of complex product networks. The existing metrics still have deficiencies such as relatively partial consideration of factors, subjective weight distribution among relevant factors, and lack of effective validation schemes. This paper proposed a node importance evaluation method based on Principal Component Analysis (PCA). The local, global, and location attributes of the product network are taken into account. Meanwhile, a node importance verification method based on Spearman’s correlation coefficient was designed by considering the correlation between node importance and its propagation strength. Finally, the proposed algorithm’s effectiveness was verified on ten artificial networks and one real product structure using the SI propagation model.
New Results in Stabilization of Uncertain Nonholonomic Systems: An Event-Triggered Control Approach

This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained form. In order to tackle the effects of drift uncertain nonlinearities, nonholonomic constraints and nonsmooth aperiodic sampling in event-based control, a novel systematic design scheme is proposed by integrating set-valued maps with state-separation and state-scaling techniques. The stability analysis of the closed-loop event-triggered control system is based on the cyclic-small-gain techniques that overcome the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems and enjoy inherent robustness properties due to the use of gain-based characterization of robust stability. More specifically, the closed-loop event-triggered control system is transformed into an interconnection of multiple input-to-state stable systems, to which the cyclic-small-gain theorem is applied for robust stability analysis. New self-triggered mechanisms are also developed as natural extensions of the event-triggered control result. The proposed event-based control design approach is new and original even when the system model is reduced to the ideal unperturbed chained form. Interestingly, the proposed methodology is also applicable to a broader class of nonholonomic systems subject to state and input-dependent uncertainties. The efficacy of the obtained event-triggered controllers is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in the orientation.
Input Power Range Extension Using Duty-Cycling and Granularity Reduction in a 5.8GHz RF Energy Harvester Dynamic Matching Circuit

Using a continuous dynamic matching circuit in an RF energy harvester ensures the desired output voltage is achieved over the input power range. However, the minimum harvestable power level is limited by the power overhead of the continuous dynamic matching circuit. In order to reduce this power overhead, the processing and actuating block of the control system are duty-cycled. In addition, reducing the granularity or the number of the matching circuit sets lessens the power overhead needed for sensing, processing, and actuating. The RF energy harvester was implemented using an intermittent dynamic matching circuit to improve its input power sensitivity. By reducing the power overhead of the dynamic matching circuit, the RF energy harvester’s lower input power limit was extended from -7dBm to -20dBm.
Enhancing Speech In Noisy Environment: A Review

Speech signal is the primary mode of communication amongst humans. It is also used for the communication between human and machine, i.e., human-machine interaction (IoTs), communication with a range of electronic devices, e.g., Internet of Things (IoTs), and even with virtual assistants (VAs) or online search engines, e.g., Google etc. The use of speech signals for the communication over mobile phones, and smart phones based virtual assistants such as Cortana, Siri, Alexa etc. is well known. Usually, this communication involves some communication medium, which is susceptible to noise. The speech or audio signal used for the communication may be adversely affected by the noise that is added unwantedly over the communication channel or medium. It not only degrades the quality of communication and intelligibility of the message or content, but also leads to poor experience. The quality of speech signal may be degraded significantly, due to changes in the characteristics of speech signal, during the process from transmission to reception and the signal conversions involved at both ends. Hence there is a need for algorithms, signal processing methods, acoustic features and classifiers etc. for noise detection and noise reduction during the speech signal communication process. In this paper, a comprehensive review of various speech signal databases, speech signal processing methods used, acoustic features extracted and the classifiers used in different studies is presented. A comprehensive and comparative view of results obtained in different studies, and the applications is also presented. This study should be helpful to all the researchers working in the domains of speech signal processing in noisy environments or degraded speech etc.