Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Event Analysis of Urban Environmental Data based on Deep CNN of Spatiotemporal Raster Images

Torrential rains, the complicated network of roads, and the high density of vehicles contribute partly the number of traffic accidents. In order to understand the association between these factors towards building a risk map that can alert drivers of dangerous zones, the visual patterns reasoning system is proposed. By converting sensing data collected from different factors to raster images, the associations can be treated as visual patterns that can conserve their spatiotemporal information. Deep convolutional neural networks (Deep-CNN) are utilized to build a model based on these raster images towards detecting accidents based on the association between factors. Image clustering is applied to learn a representation of each type of associations. Thus, the visual pattern of high-probability traffic accidents can be reasoned in the natural language format. Both 2D and 3D raster images are investigated to examine the spatial and spatiotemporal associations between factors with Deep-CNN models. We use both transfer learning and fine-tuning approach to build our model due to the small size of the positive samples dataset. The evaluation shows the initial but promising results of our method. We also discuss the potential applications and various research directions can be investigated using our proposed method.
Gait Phase Estimation for FES Based on Pelvic Movement of a Novel Gait Rehabilitation Robot

Up to now, an increasing number of stroke patients have difficulties in ambulatory activities. Existing therapeutic treatment has limits in efficiently restoring normal ambulatory function, especially in hemiplegia. Therefor, a novel system and strategy for gait training of hemiplegic patients is proposed here. It is consisted of a functional electrical stimulation (FES) device and a pelvic support robot system. FES is automatically controlled by pelvis motion during gait cycle. A relationship between active lateral motion and pelvic gait was constructed by simulating the walking of patients with hemiplegia. Based on the constructed relationship, an algorithm of gait cycle estimation is proposed. FES control time can be calculated by estimating gait stage of lateral movement of pelvis. Finally, the feasibility of the algorithm was verified by simulating the experimental results of hemiplegic patients and combining the FES control strategy.
A Position-Based Friction Error Model and Its Application to Parameter Identification

Most existing methods rely on the torque information of a servo drive to predict the tracking error caused by the friction force (friction error), but the torque information is not available in certain systems. This paper proposes a position-based modeling method for the friction error. Two examples are given to verify the practicality of the proposed friction error model on the systems when torque information is not available. The experimental results show that the proposed method can predict the friction error accurately and the proposed online parameter identification method exhibits better performance. The proposed position-based friction error model can also be easily applied to commercial CNC systems because only the position information is required.
Action Selection Methods in a Robotic Reinforcement Learning Scenario

Reinforcement learning allows an agent to learn a new task while autonomously exploring its environment. For this aim, the agent chooses an action to perform among the available ones for a certain state. Nonetheless, a common problem for a reinforcement learning agent is to find a proper balance between exploration and exploitation of actions in order to achieve an optimal behavior. This paper compares multiple approaches to the exploration/exploitation dilemma in reinforcement learning and, moreover, it implements an exemplary reinforcement learning task within the domain of domestic robotics to show the performance of different exploration policies on it. We perform the domestic task using ∊-greedy, softmax, VDBE, and VDBE-Softmax with online and offline temporal-difference learning. The obtained results show that the agent is able to collect larger and faster reward by using the VDBE-Softmax exploration strategy with both Q-learning and SARSA.
Estudo de caso: neuropatia auditiva/dessincronia auditiva

The aim of this study was to report a case of Auditory Neuropathy/Dys-synchrony (AN). Auditory Neuropathy/Dys-synchrony is a disorder identified only twenty years ago due to diagnosis failure, patients with this disorder were diagnosed as hearing impaired. AN diagnosis was made possible upon the appearance of otoacoustic emissions (OAE) recordings and its presence in hearing evaluation battery. The individual was evaluated in audiological tests: imitanciometry, audiometer evaluation, otoacoustic emissions and Auditory Brainstem Response. The results suggest that individual to show an suggestive case of AN and alert the need of long term follow up in the cases of suspected disorder.
Information Forwarding in LDPC Decoding for Markov Sources

Belief propagation decoding of binary LDPC codes combines three independent probability estimates, a-priori, intrinsic, and extrinsic information, iterations along the Tanner graph are estimating the value of the received bits, the likelihood of the values increasing with each iteration. The a-priori estimate of the information bits is a measure of the source statistics for generating bit values 0 and 1 (mapped to ±1), reflecting bias and redundancy in the information sequence itself. In this paper, we have modified the a-priori estimate to incorporate memory properties present in the transmitted information sequence resulting from a Markov source. We consider two principle alternatives for decoding. The first option is to use the Markov dependencies directly as further links between variable nodes in the Tanner graph of the LDPC code. The second alternative is to see the Markov source and the LDPC code as a serial concatenation asking for a Turbo iterative decoding between the two corresponding decoders. The latter comes with significant complexity compared to the direct embedding into the LDPC Tanner graph. Especially, one modification applying Jensen’s inequality leads to a very low decoding complexity at no performance loss. The Turbo scheme’s performance depends on the scheduling. Superior performance can be achieved with sufficient iterations in the LDPC decoder itself.
On the Orbital Pursuit-Evasion Games with Low Constant Thrust-to-Mass Ratio

Much research has been done on the topic of pursuit-evasion games, which are of interest in diverse fields. Orbital pursuit-evasion game is more complex to some extent compared with other kinds of pursuit-evasion game due to the unique orbital dynamics and environment. Assuming a low constant thrust-to-mass ratio and that the thrust pointing direction is the only control option, this paper presents an efficient orbital pursuit-evasion model based on the relative state differential equations in LVLH (Local Vertical, Local Horizontal) coordinate system and a method to calculate the saddle equilibrium solution. Moreover, by analyzing the orbital pursuit-evasion model about the terminal condition and behavior learning framework, this paper reaches two useful and efficient conclusions, showing that the maneuver capability of the pursue spacecraft should be better than the evader spacecraft, or the role of each other will be exchanged, and the saddle equilibrium solution can't guarantee an optimal strategy in an imperfect information situation, and the behavior learning framework is an efficient way to handle that.
Generating Compact Test Patterns for DC and AC Faults Using One ATPG Run

A novel test pattern generation flow for both DC and AC faults is presented. All faults to be processed are transformed into stuck-at faults with some constraints in a proposed two-timeframe circuit model such that all considered faults can be represented utilizing the user-defined fault model which is supported by most commercial ATPG tools. This makes it possible to generate all required patterns for both DC and AC faults in one ATPG run with no need to modify the ATPG tool. A highly compact pattern set thus can be obtained which requires smaller test data volume and shorter test application time. The fault models considered in this paper include stuck-at faults, bridging faults and transition faults. Experiments on ISCAS‘89, IWLS‘05 and ITC‘99 benchmark circuits show that, compared to the most efficient conventional methods, on average our method can reduce test pattern counts by 14.55%, 11.26% and 13.69% and reduce test application time by 25.93%, 24.47% and 31.67%, respectively, without degrading fault coverage.
A LIDAR SLAM Based on Point-Line Features for Underground Mining Vehicle

This paper presents an original 2D LIDAR SLAM based on point and line features to the underground tunnel environment. In our algorithm, the point features are extracted by using Curvature detector with range information weights. The line features detection algorithm contains segmentation by two hierarchical levels and line fitting. We present the approach used in our prototype of underground mining truck which achieves real-time localization and mapping at a 5cm resolution. We provide experimental results and comparisons to other approaches. The results show that this proposed method has good robustness and accuracy.
Design-for-Error-Detection in Implementations of Cryptographic Nonlinear Substitution Boxes Benchmarked on ASIC

Achieving different security properties through lightweight cryptography has been the focus of recent research efforts. A wide range of criteria should be taken into account when designing lightweight ciphers, such as both the linear and non-linear properties, and resilience to active side-channel attacks (SCA), e.g., fault analysis attacks mounted on VLSI implementations. This paper motivates the urgency and highlights the importance of considering reliability and error detection as a design factor beforehand rather than an afterthought. In this paper and through case studies, we motivate the urgency of “design-for low-cost reliability and fault diagnosis” for future work to thwart fault analysis attacks “before” the algorithms are designed. In this paper, we focus on the design criteria of S-boxes transformations (we note that for different ciphers, these transformations might be denoted by other yet similar terms), which provide diffusion and confusion properties in block ciphers, stream ciphers, and hash functions. We also present the results of our implementations for a set of S-boxes used in various block ciphers benchmarked on application-specific integrated circuit (ASIC). The proposed assessments for reliability and error detection of lightweight ciphers is a step forward towards design for error detection while achieving secure implementations.
Sparse FIR Filter Design Using Artificial Bee Colony Algorithm

In this paper, sparse linear-phase FIR digital filters are designed using artificial bee colony (ABC) algorithm. Sparse digital filters can be used in applications where computational cost and complexities are of concern as zero-valued coefficients eliminate multiplications required for implementation. In this method, sparse digital filters are designed using minimax optimization by ABC algorithm and successive elimination. In contrast to methods which minimize insignificant coefficient values, this method eliminates insignificant coefficients by setting them to zero. The sparse linear-phase FIR filters designed using ABC algorithm are compared to the partial l1-norm optimization design, the minimum increase design, and the smallest coefficient design to illustrate the effectiveness of each design method.
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.
Performance Analysis of Frequency Spreading FBMC in Mobile Radio Channel

Filter Bank based Multicarrier (FBMC) is a promising waveform contender for 5G systems. In this paper, we have interested to use Frequency Spreading (FS) approach in order to generate and evaluate the performance of FMBC with different mobile radio channel. The simulations of this system show that both FS-FBMC and Orthogonal Frequency Division Multiplexing (OFDM) presents same performance in flat fading channel. However, FS-FBMC outperforms OFDM in high delay multipath channel scenario.
Sample-Efficient Learning of Soft Task Priorities Through Bayesian Optimization

In recent optimization task-space controller, hierarchical task prioritization can be made strict or soft within a given level. Soft hierachization is made using task weighting. Yet the latter is not automated and weights are set ad-hoc. This empirical approach could be time-consuming and even leads to an infeasible result. During a specific episode in order to approximate the evolution of the weight of a task, we assign a Radial basis function network(RBFN) to each of the tasks. We use the Bayesian Optimization procedure to regulate the RBFNs corresponding to different tasks based on performances indexes that are extracted for a fixed episode. We benchmark the proposed solution with a dual-arm manipulation simulation where multiple potentially conflicting tasks are involved. First of all We can find that the proposed approach outperforms a hand-tuned controller in terms of tracking errors. In comparison with tuning the weights using another stochastic optimization technique, i.e. CMA-ES, we can find that the proposed approach requires much less samples to evaluate.
A Novel4×4 Butler Matrix Connected to 8×1 Patch Array Antenna for Electronic Scanning Radar Application

The aim of this paper is to present a low cost steerable 8×1 antenna array system operating at 3GHz. Hence, a novel 4×4 butler matrix is proposed as a beam forming network responsible of redirecting the beams in different positions. The overall system uses microstrip line structure and is mounted on FR-4 substrate. The design is studied in details to obtain at the end four steerable beams.
A New Method on Estimating Free-Form Surface Profile Error of Blade

As the blade parts must conform to accurate shape and precise size, estimating the three-dimensional profile error of a blade is absolutely necessary. This paper proposed a new method to estimate free-form surface profile error integrating application of point cloud processing technology and iterative closest points (ICP) algorithm. A set of point cloud data, converted from a set of 3D profile measurement data of a blade acquired by laser scanner, can be aligned to the point cloud extracted from the blades CAD model. Then, calculating all correspondence points distance along normal direction between the correspondence points, the maximum value is found out and twice this value taken as surface profile error. Simulation example indicates that the result worked out by this method is very close to the theoretical result for the problem to solve transformation matrix. In addition, simulation experiment manifests that the maximum distance along normal direction between the points in measurement data and their matched closest points in the design model data is shorter than the direct distance.
Incremental Delta Sigma Modulation with Dynamic Weighted Integration

This paper presents a technique to reduce the required number of cycles to reach a given quantization resolution of conversion performed by incremental $\triangle \Sigma $ ADCs. Thanks to a specifically designed dynamic weighting of the modulator's coefficients combined with a dedicated digital filter, the OverSampling Ratio (i.e., OSR) can be reduced compared to conventional structures thanks to a more efficient quantization noise shaping. This Dynamic Weighted Integration (DWI) technique is first motivated in the context of a first order modulator supported with a mathematical proof. In the second part, the proposed technique is extended to the specific case of a complex fourth order modulator with four different examples of topology regarding the position of the dynamic weighting.
Sensorless Control of the PMSG in WECS Using Artificial Neural Network and Sliding Mode Observer

This paper proposes a sensorless control strategy for a Permanent Magnet Synchronous Generator (PMSG) in variable speed Wind Energy Conversion System (WECS). In order to increase efficiency and robustness of WESC, two kinds of algorithms, Artificial Neural Network (ANN) and Sliding Mode Observer (SMO), are designed. Consequently, the observation of the rotor speed and the rotor position are made, only, by measuring voltages and currents. The effectiveness of the proposed observers is verified using Matlab /Simulink software. Simulation results show the effectiveness of the proposed observers for the PMSG driven by WECS.
Stability Improvement of Mobile Robot with Mutative Driving Axial Distance Omni-Directional Wheels

The omni-directional mobile robot has three degrees of freedom in the plane. It has high flexibility and is also suitable for transport and operation in crowded and narrow environments. The work focuses on detailed analysis of the driving mode of the driving wheel and the changing process of the driving axial distance. In accordance with the driving axial distance, the omni-directional wheels are divided into two types: mutative driving axial distance wheels and fixed driving axial distance wheels. The mecanum wheel, alternate wheel and the MY3 wheel belong to the mutative driving axial distance wheel. The causes of the abrupt change of ground distance of the robot and the influence of the mutation on the stability of the robot are analyzed. In order to improve the stability, the relationship between the ground distance and the rotation angle of MY3 robot is analyzed. Two functions are proposed: The sinusoidal gradual- change function and The polynomial gradual-change function. Synthesizing the trajectory, kinematics and dynamics models, the minimum value of the gradual coefficient is obtained and the trajectory accuracy is improved. The simulation results show that both the functions can eliminate mutations of the speed and torque of the motor, but the trajectory errors of the sinusoidal gradual-change function are smaller.
On the Performance of Parallel Processing in Dynamic Resource Sharing Systems

Parallel processing has the potential of significantly decreasing the service time for a single computational task. Meanwhile, as each task occupies more resources, the number of simultaneously supported tasks declines. This tradeoff is interesting when resources are accessed by many users in a dynamic way, like in the case of cloud or fog computing. In this paper, we evaluate how the level of parallelization and the eventual overheads affect the response time in these dynamic resource sharing systems. We show the counterintuitive finding that even when parallelization has no overhead, the allocation of all resources to a task is suboptimal if the service times have large coefficient of variation. Moreover, we evaluate the scalability properties, and provide guidelines for the optimal level of parallelization under different types of overhead.
Mitigating Over-Permissible Transfer for Control Flow Integrity

Control flow integrity (CFI) is a promising defense mechanism for controlling hijacking attacks. In most situations, CFI prevents the control flow transfer from hijacking by the adversary. However, the over-permissible transfer problem remains a major issue in most CFI systems. By taking advantage of this problem, an adversary can still hijack control flows to the code not designed by the original programmer but intended for execution by the adversary. To eliminate over-permissible transfer, we propose transfer target analysis for the CFI system. With value set analysis and concolic execution, the transfer target analysis is able to construct the precise control flow graph. Given the fine-grained policy, over-permissible transfer can be prohibited. To save memory, the Bloom filter-based policy database is established to store a large number of policies in a compact data structure with low false positive rate. The evaluation uses benign programs from the SPEC CPU2006 benchmarks and the malign programs from the RIPE benchmark. The result shows that all the benign programs can execute normally but all the malign programs are blocked.
Robust Predictive Control for Boiler-Turbine Coordinated Control System

Boiler-turbine unit is a multi-input multi-output (MIMO) nonlinear system with large time-delay and a lot of disturbances, so that conventional boiler-turbine coordinated control system (CCS) cannot achieve satisfactory performance. In this paper, we consider an output-feedback robust predictive control strategy for boiler-turbine unit affected by immeasurable bounded disturbances. This control strategy combining moving horizon estimation with robust model predictive control is finally formulated as a min-max optimization problem solved by a primal-dual interior-point method. At each sampling time, the optimization criterion is maximized over worst-case estimates of initial state and disturbance sequence and minimized over optimal control sequence. In this way, the controller can effectively reject constrained disturbances. Simulation results verify that this control strategy could provide significant robustness under common disturbances such as slow time-variation of coal low heat value and dead zone characteristics of throttle valve position.
AnchorCAN: Anchor-Based Secure CAN Communications System

Vehicles become intelligent and connected for enhancing driving safety and comfort in the latest decade. However, those connectivity interfaces expose vehicles in publicly accessible networks. It gives ways for attackers remotely taking control over vehicles. One reason of the success of the attacks is lack of message authentication and encryption for in-vehicle communications. This article addresses the need of secure in-vehicle CAN communications by proposing a secure CAN communications system, called AnchorCAN, against sniffer, injection and replay attacks. AnchorCAN overcomes the challenges of short payload of CAN frames and occasional unstable CAN transmission without hardware modification on CAN transceivers and without using extra CAN frames for containing authentication information. Performance evaluation is conducted on automotive-grade evaluation boards, and the results show that it takes less than 0.5ms to generate or verify a CAN frame in AnchorCAN while a periodically safety-critical CAN frame is usually sent every 10ms.