Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Delayed Output Feedback of Linear Time-Varying Systems With Applications to Spacecraft Magnetic Attitude Control

This paper studies the low-pass filter (LPF) design in the delayed output feedback control of linear time-varying systems. In the case of linear time-invariant systems, the safe implementation of the distributed delay (integral terms) needs to introduce an LPF in the control loop and the LPF can be designed by using the pole assignment method. However, the parameters of LPF in linear time-varying systems are time-varying and the existing design methods are not applicable. This paper provides a new method to design the LPF for time-varying systems. The designed LPFs are such that, if the closed-loop system without LPF is uniformly asymptotically stable, then the closed-loop system with LPF is also uniformly asymptotically stable, while the later is robust with respect to approximated numerical implementation. Finally, simulations are carried out on a magnetic attitude control system and the results verify the effectiveness of the methods proposed in this paper.
Improve Multi-objective Ant Lion Optimizer Based on Quasi-oppositional and Levy Fly

In order to solve the problem that classic ant lion algorithm is easy to fall into local optimum, a quasi-opposite Levy fly multi-objective ant lion algorithm is proposed. In this algorithm, Levy fly is used to replace the ant lion random walk mechanism in the original algorithm. The combination of Brownian motion and jumping flight is used to improve the ability of local exploitation and global exploration. At the same time, after each time iterate, the ant population is optimized by quasi-opposite learning strategy. The original population and its quasi-opposite individuals are mixed and the best individuals are selected as the new population. In this way, the diversity of the population is increased, as well as, the computational efficiency of the algorithm is improved. Finally, typical benchmarks are selected to compare the algorithm with the original ant lion algorithm and other swarm intelligence algorithms. Experimental results show that both convergence and distribution of the proposed algorithm are greatly improved. The quasi-opposite Levy fly multi-objective ant lion algorithm has good adaptability and effectiveness in solving the two-objective optimization problem.
Cell Fault Management Using Machine Learning Techniques

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this.
A New Semantic Segmentation Method of Point Cloud Based on PointNet and VoxelNet

Three-dimensional laser point cloud technology has been widely used in machine vision and three-dimensional reconstruction engineering, but scene recognition and semantic segmentation of three-dimensional point cloud is still developing and improving. With the gradual maturity of artificial neural networks of deep learning in the field of computer vision, it is possible to identify and segment three-dimensional point cloud scenes. However, most methods perform slowly and inaccurately in real-world outdoor scenes that are large and noisy. This paper proposes a new scenario segmentation method based on PointNet and VoxelNet to improve the efficiency and accuracy of semantic segmentation. Firstly, the voxel meshing method is used to downsample the point cloud scene while keeping the original information of the point cloud. Then the point cloud data is filtered by multiple methods to remove noises and smooth the surface of point clouds. Finally, the processed point cloud scene is trained and the ideal semantic segmentation result is obtained. The results of our method are evaluated on the datasets of BDCI and our own datasets.
A Three-joint Coupled, Parallel and Self-adaptive Robot Hand

In this paper, a three-joint robot hand which combines multiple grasping modes is design. Recently, for extending the function of robot hand, the robot hand with two or even more grasping modes has become the focus of the research field. The parallel, coupled and self-adaptive robot (COPASA) hand integrates characters of three grasping modes, thus it is not only suitable for grasping objects on the plane, but also has good pinching ability and high adaptability for different objects. Moreover, three-joint humanoid robot hand has the advantages of higher flexibility and better grasping stability compared with the traditional two-joint robot hand. Force analysis is performed to verify the grasping ability and grasping adaptability of COPASA hand.
Extended Wavelength Responsivity of a Germanium Photodetector Integrated With a Silicon Waveguide Exploiting the Indirect Transition

Photo-detection in the wavelength range 1850 to 2000 nm using evanescently-coupled germanium detectors grown on silicon waveguides is described. Devices were fabricated at a silicon photonics foundry using a process flow associated with operation in the O, C and L bands, and as such offer a solution for extended wavelength detection which is readily available. Intrinsic sensitivity is via indirect band transitions, which is enhanced by tensile strain and we postulate that it may be further enhanced by defects which arise from the thermal processes associated with Ge on Si growth. The responsivity of p-i-n detectors is 20 mA/W at 1850 nm falling to 5 mA/W at 2000 nm, for a detector length of 50 μm. Responsivity is suppressed by electrical doping in the germanium detector which provides parasitic absorption from free carriers. Modifications to the current design are suggested such that integrated germanium p-i-n detectors, directly grown on silicon waveguides would be suitable for high-bandwidth photo-detection up to at least a wavelength of 2000 nm. A Separate-Absorption-Charge-Multiplication Avalanche Photo-Detector is fabricated exploiting the same indirect transition. This detector has a responsivity of 0.31 A/W at 1850 nm and 0.08 A/W at 1970 nm, for a detector length of only 14 μm.
A Photon Counting Underwater NOMA Wireless Optical Communication System

In recent years, Underwater wireless optical communication (UWOC) has become one of hot spots of human exploration of the unknown deep-sea space. In this paper, a shot noise limited interleaver iterative non-orthogonal multiple access (NOMA) UWOC system with photon counting receiver is proposed. The characteristics of underwater wireless optical channel are studied. In addition to single receiver scheme, a diversity receiving scheme is also considered. According to the result of simulation, the proposed multi-user system can perform well in very low receiving signal energy, which achieves the bit error rate (BER) performance of 2*10e-5 at 40 photon number per bit for 4-user scenario with 1/10 repetition coding rate. Moreover, this system also has the rapid convergence and good receiving diversity property.
Synthesis and Use of Bio-Based Dielectric Substrate for Implanted Radio Frequency Antennas

Equipped with precision sensors/antenna modules combined with integrated processing and telemetry circuitry, wireless implants that are both biocompatible and biodegradable are important devices for monitoring patient’s conditions and patient’s safety. In this article we report on the development, design, and testing of a bio-based monopole radio frequency (RF) sensor/antenna module for potential use in human health applications. The module is built on a dielectric substrate biocomposite made of 0.5:1.0 ratio of polylactic acid (PLA) to sunflower carbon substrate (SCS) produced via pyrolysis of seeds shells. Findings for the SCS include optimized reactor yields around 7.9 wt. % at 500°C, a 0.27:1.0 fixed to elemental carbon content, dielectric constant near 3.4, loss factor between 0.0 and 0.4 measured in the 1 to 6 GHz frequency range. The PLA-SCS biocomposite exhibited comparable dielectric properties to those of pure SCS, a 17% elastic modulus increase, and over 500% increase in hardness. Numerical simulation of the designed sensor/antenna module agreed fairly well with the experimental validation results. Tests of the fabricated sensor/antenna module on water, soil and muscle tissue phantom, as well as implanted inside muscle tissue phantom, via the reflection coefficient (S11) and in a communication link via the transmission coefficient (S21) confirmed use and applicability of the developed antenna.
Prof. dr. Andrej Marušič: Za duševno zdravje jutri in dan zatem

Sklop, zasnovan na podlagi sedmih avtorskih idej, predstavlja življenje in delo prof. dr. Andreja Marusica, psihiatra in psihologa, ki je s svojim predanim angažmajem pomembno sooblikoval podrocje javnozdravstvene politike dusevnega zdravja doma in v svetu. Ves cas svojega delovanja je posebno pozornost namenjal razvojnemu napredku suicidologije, saj ga je problematika samomorilnosti strokovno posebej zanimala. Zavzemal se je za celostno integracijo suicidologije kot vede, ki bi temeljila na splosno sprejeti in enopomenski nomenklaturi. Zakonsko ureditev podrocja dusevnega zdravja je razumel kot na najvisjih ustavnopravnih, strokovnih in eticnih nacelih temeljec sistem zascite in pomoci uporabniku, torej osebi z dusevno motnjo. Sklop se koncuje z doslej se neobjavljenim avtorskim clankom na temo zdravja kot ene same celote, prepleta telesnega in dusevnega.
Fast Trajectory Planning for Off-Road Autonomous Driving with a Spatiotemporal Tunnel and Numerical Optimal Control Approach *

This paper proposes a unified and fast trajectory planning method for an autonomous vehicle with avoidance of static obstacles in the off-road scenarios. Nominally, an optimal control problem should be used to cover generic off-road planning cases. However, the scale of the formulated optimal control problem is intractable, because the ego vehicle has to avoid collision with every obstacle at every moment. To address this issue, we aim to discard the redundant collision-avoidance constraints, because the vehicle may not really have chances to collide with all of the obstacles at every moment. Concretely, with the help of a reference trajectory derived by hybrid* algorithm, we develop a spatiotemporal tunnel-based strategy such that the ego vehicle is restricted to move within a sequence of tunnels during different periods of the entire dynamic process. Through this, the collision-avoidance constraints are replaced by the within-tunnel constraints, thereby making the scale of the optimal control problem completely irrelevant to the complexity of the environment.
River Water Quality Parameters Prediction Method Based on LSTM-RNN Model

This paper investigates the characteristics of dynamic nonlinearity and correlation of water quality parameter information, as well as the gradient disappearance and gradient explosion caused by the training data of traditional RNN network model, etc. The long short-term memory network structure (LSTM) is introduced to optimize the structure of RNN network and the connection weight and threshold of hidden layer. A new water quality parameter prediction model of LSTM-RNN network based on improved RNN network structure is proposed by setting the number of storage units in the hidden layer of the network, the number of structural layers of the network model, and adjusting the time window size of the data training set. Combined with the water quality monitoring data of the River in Shanghai, the model is used to predict and verify the main pollutant index COD (potassium permanganate index) in the River. The simulation results show that compared with the traditional GM (grey model) and RNN network water quality prediction model, the sample approximation accuracy and generalization ability of the training prediction based on LSTM-RNN network model is higher and better than that of the traditional GM (grey model) and RNN network model. Good comprehensive prediction performance of river water quality is presented.
The Rise of Radar for Autonomous Vehicles: Signal processing solutions and future research directions

Automotive radar is the most promising and fastest-growing civilian application of radar technology. Vehicular radars provide the key enabling technology for the autonomous driving revolution that will have a dramatic impact on everyone's day-to-day lives. They play a central role in the autonomous sensing suit because of the significant progress in the radio-frequency (RF) CMOS technology that enables high-level radaron-chip integration and thus reduces the automotive radar cost to the level of consumer mass production. However, this would not be sufficient without high spatial resolution performance, which can be obtained by multiple-input, multiple-output (MIMO) and cognitive approaches at a lower cost.
Epileptic State Classification for Seizure Prediction with Wavelet Packet Features and Random Forest

The epileptic state classification for preictal prediction of seizure is essential for preventing the damages caused by seizure. Existing research mainly focus on the epileptic seizure detection while few of them investigate the preictal state prediction. In this paper. a novel electroencephalograph signal (EEG) based prediction algorithm for the preictal state of seizure is developed. The one hour preictal state before the seizure is divided into three non-overlapped segments and a multi-class classification is then performed for preictal state prediction. The wavelet packet based features (WPFs), including the subband energy ratio and three wavelet entropies, are extracted for the multi-channel EEG signal representation. The Random Forest algorithm (RF) is adopted for the preictal state prediction. Experiments on the benchmark CHB-MIT EEG database are conducted for performance analysis. Comparisons with methods based on popular EEG features and classifiers are provided to show the superiority of the proposed WPFs+RF based preictal state prediction algorithm.
The Route Planning for AMR Based on Combined Ant Colony and Genetic Algorithm

AMR(Autonomous Mobile Robots) have developed rapidly recently and have been implemented into various industrial fields. AMR have become more practical , systematic and intelligent. Navigation technology is the core of AMR. This paper studies the route planning for AMR in the wireless sensor networks based on the combined ant colony and genetic algorithm. Firstly the system model for the control design of AMR is described . secondly the application of Genetic algorithm for route planning is introduced . Thirdly ,the paper analyses the most popular method ACO(Ant Colony Optimization) . and combine the GA(Genetic Algorithm) and ACO in the specific phase to develop a new combined method . Simulation and experiment results both show the combined algorithm has the better performance.
Robust Adaptive Trajectory Tracking Control of Mobile Robots with Actuator Faults

This paper addresses the trajectory tracking control problem of a class of faulty mobile robot systems with actuator faults. The bias actuator faults are studied without knowing any fault information of actuators. Firstly, an adaptive scheme is proposed to estimate the effects of bias actuator faults. Then a state feedback control strategy based on adaptive estimations is constructed by using backstepping control technology. Through Lyapunov stability theory, it is proved that the azimuthal angle and forward speed of the wheeled mobile robot can track the desired trajectories under the influence of bias actuator faults. The validity of the design is verified by an example of mobile robot system.
A Framework for Large-Scale Incentive-Based Residential Demand Response using Aggregators

Smart homes and appliances are mature concepts in the smart grid. Controlling home energy assets can provide reliability and sustainability benefits, however this concept increases the complexity of electricity grid operations. Aggregators are introduced to facilitate and control the growing number of energy assets in the residential sector using demand response (DR). The aggregators interface the independent-system operator and end-users to provide DR services for all stakeholders. This paper proposes a framework for large-scale incentive-based residential energy management via the aggregator that benefits all entities. Case studies of aggregator DR are reviewed and challenges are listed.
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks

Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.
Machine Learning for Data Processing in Vessel Telemetry System: Initial Study

In 2016, there are more than 20.000 commercial vessel (ship/boat) in operation inside Indonesian Maritime Zone. Global competition has pushed Vessel owner and Fleet Management to continuously improve their efficiency and effectiveness of their Vessel Operations. One common way of improvement is by implementation of Vessel Monitoring System which help Fleet Management to locates and monitors their vessel out in the open sea in real time. Vessel Monitoring System usually monitor basic GPS data only, like location, heading, and speed. The recent trend is the implementation Vessel Telemetry System (VTeS), which delivers more data from vessel, such as engine status, propeller status, and fuel consumption. While more parameters are delivered to ground station, a deeper analysis is required to understand the current condition of vessel, hence it requires more time and effort. The lack of person or expert who understand how to analyze those data only make the VMS system underutilized and become useless, therefore it's very important to provide a decision support system to resolve this problem. This research is an initial study on how to build a Decision Support System using machine learning to interpret VTeS data. The selected case is how to detect fuel consumption anomaly in vessel operations. Early study indicates that this system is possible, and in the end of paper we propose a machine learning pipeline model for this system.
Peak Detection Algorithm for Gait Segmentation in Long-Term Monitoring for Stride Time Estimation using Inertial Measurement Sensors

Stride time is an important indicator for the assessment of gait stability in long-term measurements using inertial measurement sensors. Peak detection is a commonly used method for gait segmentation and stride time estimation. Standard peak detection algorithms often fail due to additional movement components and measurement noise. This paper proposes a novel algorithm for robust peak detection in inertial sensor signals. The multi-stage implementation includes frequency analysis, reference signal synthesis, correlation and statistical analysis and a successive peak detection. The algorithm is validated on different inertial measurement datasets and compared to commonly known peak detection algorithms for periodic signals. In a movement laboratory setup of both healthy elderly and high risk fallers, we achieved satisfying detection results with a $\boldsymbol{F}_{1}$-measure of 95.5%, which exceeded all additionally implemented algorithms for peak detection in gait signals based on inertial sensor measurements. In addition, we evaluated the stride time estimation performance using the proposed algorithms applied to different sensor positions and found sufficiently high correlation with optical reference data.
An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack

As the amount of data and computational power explosively increase, valuable results are being created using machine learning techniques. In particular, models based on deep neural networks have shown remarkable performance in various domains. On the other hand, together with the development of neural network models, privacy concerns have been raised. Recently, as privacy breach attacks on training datasets of neural network models have been proposed, research on privacy-preserving neural networks have been conducted. Among the privacy-preserving approaches, differential privacy provides a strict privacy guarantee, and various differentially private mechanisms have been studied for neural network models. However, it is not clear how appropriate privacy parameters should be chosen, considering the model’s performance and the degree of privacy guarantee. In this paper, we study how to set appropriate privacy parameters to preserve differential privacy based on the resistance to privacy breach attacks in neural networks. In particular, we focus on the model inversion attack for neural network models, and study how to apply differential privacy as a countermeasure against this attack while retaining the utility of the model. In order to quantify the resistance to the model inversion attack, we introduce a new attack performance metric, instead of a survey-based approach, by leveraging a deep learning model, and capture the relationship between attack probability and the degree of privacy guarantee.
An Improved Rocker-based Inverted Pendulum Walk Model for Human Gait

It is well-known that feet play an important role in human walking. Foot functions include heel-rocker, ankle-rocker, forefoot-rocker as well as toe-rocker, they produce a wheel-like rolling motion to make forward progression. This paper presents an improved rocker-based inverted pendulum walk model. From the static approaches, put out a new equation of roll factor, and take the vertical excursion of COM (Center Of Mass) as the criterion. Through experimental comparison, it is verified that the new model and the new equation of roll factor are more accurate. And it demonstrated that foot functions are related to roll factor. Furthermore, the gait characteristics (step length, cadence, or gait speed) can be a function of only roll factor. The range of roll factor is 1.4 to 1.7. The vertical excursion of COM and the angular motion of ankle joint are strongly related to roll factor. Thus, roll factor, which reflects foot function in walk, can be a useful parameter to evaluate walking state. The new rocker-based inverted pendulum walk model is appropriate to the soft wearable walking assistive suit.