Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Asynchronous Control for Artificial Delayed Markovian Jump Systems via Output Feedback Sliding Mode Approach

A novel asynchronous control for a class of Markovian jump systems (MJSs) via output feedback sliding mode approach with an artificial time-delay is proposed. The asynchronous control strategy is adopted owing to the nonsynchronization between the controlled system and the controller. In some practical applications, the state variables are often difficult to be measured directly from the outside of the system, which makes the implementation of state feedback technique more complex. However, the output information is always accessible to the system. Therefore, an asynchronous output feedback sliding controller, where an artificial time-delay is introduced in the synthesis of the sliding surface, for MJSs is designed to guarantee the sliding mode dynamics satisfying the reaching condition, and a sufficient condition is derived to ensure the resultant system exponentially stable. Besides, a program of optimization is given to optimize the artificial delay-time. Finally, a numerical simulation and a practical application are given to validate the effectiveness of the proposed technique.
RATCHETA: Memory-Bounded Hybrid Byzantine Consensus for Cooperative Embedded Systems

Cooperative autonomous systems gain increasing popularity nowadays. Most of these systems demand for high fault-resilience, otherwise a single faulty node could render the whole system useless. This essentially calls for a Byzantine fault-tolerant consensus. However, in such algorithms typically only (n-1)/3 faulty nodes can be tolerated in a group of n nodes and the message complexity is high. Even worse, systems with only 3 nodes are too small to even tolerate a single Byzantine node. In this work we present a novel consensus algorithm, RATCHETA. On the one hand it increases the maximum tolerable faulty nodes to (n-1)/2 and lowers the message complexity. This is achieved by assuming a hybrid fault model, which features the use of a small trusted subsystem that hosts a pair of monotonic counters for message authentication to prevent equivocation. Moreover, it can ensure an upper bound of the memory usage and message size, which is not addressed by most other hybrid consensus algorithms. On the other hand RATCHETA is tailored for wireless embedded systems. It uses multicast to reduce the communication overhead, and it does not rely on any packet loss detection or retransmission mechanisms. We implemented RATCHETA with its trusted subsystem built on top of ARM TrustZone. Our experimental results show that RATCHETA can tolerate both Byzantine faults and a certain amount of omission faults. With 20% message omissions, a 10- node group needs less than 1 second on average to reach a consensus. If 4 nodes out of 10 become Byzantine, the consensus latency is only about 1-3.6 seconds even under rough network conditions.
On State-Dependent Discretization of Stable Homogeneous Systems

Conditions for the existence and convergence to zero of numeric approximations with state-depend step of discretization to solutions of asymptotically stable homogeneous systems are obtained for the explicit and implicit Euler integration schemes. It is shown that for a sufficiently small discretization step the convergence of the approximating solutions to zero can be guaranteed globally in a finite or a fixed time, but in an infinite number of discretization iterations. It is proven that the absolute and relative errors of the respective discretizations are globally bounded functions. Efficiency of the proposed discretization algorithms is demonstrated by the simulation of the super-twisting system.
A Board Omnidirectional Coplanar Waveguide Antenna with Choke Sleeve

A board omnidirectional coplanar waveguide (CPW) antenna is designed with a choke sleeve in this paper to realize high gain and good radiation performances. The antenna employs four λg/2 long units which are staggerconnected to achieve in-phase current on each unit. In this way, a high gain omnidirectional antenna can be obtained. By loading the designed choke sleeve which is aimed at reducing surface current on the coaxial feeder outer conductor and an additional infinite ground, this board antenna performance would not be affected by an even longer coaxial line. The simulation results show that the operating frequency with reflection coefficient less than -10 dB ranges from 5.71 GHz to 6.05 GHz. The maximum gain at the center frequency of 5.8GHz in the boresight is 5.3 dBi. The out-of-roundness of the omnidirectional radiation pattern in the azimuthal plane (XOY plane) is less than 1.3 dBi. Also, the reflection coefficient, gain and radiation patterns are showed with and without choke sleeve for comparison. Radiation patterns could be improved by the added choke sleeve. The proposed antenna with simple configuration, low profile, compactness and low fabrication cost becomes a candidate as a board antenna for WiMAX applications.
A 6-13.5GHz Wideband Single-Core LC VCO with Lookup Table Algorithm

A wideband LC voltage controlled oscillator using a single core is presented in this paper. The proposed VCO covers frequency range from 6GHz to 13.5GHz and will be implemented in 65nm CMOS process. Switched capacitance arrays and MOS varactors acting as coarse and fine tune units determine the wide tuning range 76.9%. By dividing the whole frequency range into 8 sub frequency bands, lookup table (LUT) algorithm is proposed to achieve the fast lock of the PLL. The tuning gain Kvco is adjusted smaller to acquire better phase noise at high frequency and Kvco is digital controlled between 100 MHz/V to 600MHz/V at 13GHz, therefore the phase noise of the VCO is -113dBc/Hz at 1MHz offset and differences are less than 1dBc/Hz@1MHz at all frequency bands. The power supply is 1.2V and programmable biasing current varies from 4mA to 26mA.
A Supervised Learning Approach using the Combination of Semantic and Lexical Features for Arabic Community Question Answering

In this paper we address the problem of Community Question Answering (CQA) for Arabic language. We mainly explore the direction of combining both lexical and semantic features for enhancing the retrieval task of possible answers to a posted question. We show a comprehensive evaluation on the SEMEval2017 CQA dataset for Arabic language. Our Mean Average Precision (MAP) achieves 62.85% when using a supervised machine learning approach (linear SVM). We outperformed best reported results on such dataset. This is achieved by defining a mix of word embedding, latent semantic similarity features and other lexical similarity features.
A 65-nm CMOS 6-bit 2.5-GS/s 7.5-mW 8 $\times$  Time-Domain Interpolating Flash ADC With Sequential Slope-Matching Offset Calibration

A 6-bit 2.5-GS/s $8\times $ dynamic interpolating flash analog-to-digital converter (ADC) with an offset calibration technique for interpolated voltage-to-time converters (VTCs) is presented for high-speed applications. The dynamic-amplifier-structured VTC enables linear zero-crossing (ZX) interpolation in the time domain with an interpolation factor of 8, which reduces the number of front-end VTCs to one-sixth the original structure. The reduced number of VTCs lowers the power consumption, load capacitance to the track-and-holder (T/H), and overhead of VTC offset calibration. The sequential slope-matching offset calibration scheme is proposed not only for VTC offset but also for interpolated ZX accuracy. The prototype 6-bit 2.5-GS/s flash ADC was implemented in a 65-nm CMOS process and occupies a 0.12 mm2 chip area, including offset calibration circuitry. The measured differential non-linearity (DNL) and integral non-linearity (INL) after offset calibration are 0.68 and 0.65 LSB, respectively. With a 1.23 GHz input, the measured signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) are 33.84 and 45.07 dB, respectively, with power consumption of 7.5 mW under a supply voltage of 0.85 V. The prototype ADC achieves a figure of merit (FoM) of 74.7 fJ/conversion step at 2.5 GS/s.
Microstrip-Fed Surface-Wave Antenna for Endfire Radiation

This communication presents a microstrip-fed surface-wave antenna for endfire radiation. The antenna has the advantages of high endfire gain, wide endfire gain bandwidth, and stable endfire radiation pattern. It consists of a microstrip line with air media and a dielectric cuboid. The half-mode dielectric waveguide, which is made up by the dielectric cuboid and the air substrate introduced by the microstrip line, is the radiating structure of the antenna. Fed by a microstrip line, the half-mode dielectric waveguide couples the energy continuously with a moderate phase constant, which is helpful for the antenna to realize a high endfire gain and a stable endfire radiation pattern. With a low dielectric constant, the half-mode dielectric waveguide has a stable radiation performance within a wide bandwidth. With a length of 5.76 wavelength, the antenna achieves a maximum endfire gain of 13.3 dBi at 4.75 GHz. The 1 and 3 dB gain bandwidths of the antenna is 18.1% and 37.8%, respectively. Good matching is obtained from 3 to 6.5 GHz.
Recognition Effects of Deep Convolutional Neural Network on Smudged Handwritten Digits

Deep convolutional neural network (CNN) is known to be the first truly successful deep learning approach for image processing and understanding, e.g., the handwritten digits discrimination. However, in real applications such as handwritten zip code recognition, the collected images are commonly with smudged background. In this paper, we study the recognition effects of CNN on smudged digits and compared the results with three-layered perceptron. Experimental results based on MNIST dataset with smudged background (simulated by salt-and-pepper and gaussian noises) show that a drastic decline of recognition accuracy is observed for CNN, suggesting that the extracted features by convolutional operation and max pooling is very sensitive to the noise.
MDC-Cast: A Total-Order Broadcast Protocol for Multi-Datacenter Environments

The recent Total-Order Broadcast protocols that have been designed to sustain high throughput and low latency target fully switched environments, such as small datacenters and clusters. These protocols fail to achieve good performance in multi-datacenter environments, that are characterized by non-uniform network connectivity among a set of remote datacenters. More precisely, machines within a datacenter are connected using a fully switched network, whereas machines across datacenters use shared inter-datacenter network cables. This paper presents a novel Total-Order Broadcast protocol, called MDC-cast that specifically targets multi-datacenter environments.
Learning Compliant Manipulation Tasks from Force Demonstrations

Robots work around human beings not only on manufacturing production lines, but also in work scenes such as kitchen, operating table and other unstructured environments. Preprogramming stiff position control is not enough. To exploit how to perform compliant manipulations around human beings without having to preprogram them is meaningful. In this paper, we aimed at endowing robot with the ability of understanding and learning compliant manipulation from demonstrations. In demonstration phase, the interaction forces and the end effector trajectory are recorded through a simple teaching interface. Furthermore, a learning framework is proposed to exploit the interaction force to estimate time-varying stiffness and damping profiles of the impedance controller in x, y and z directions. We validate the proposed approach with two experiments on a 4-DoF Barrett WAM, where the robot is able to learn time-varying compliance levels in three directions from demonstrations.
Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

This paper considers a passive localization scenario relying on a single transmitter, several receivers, and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation–maximization-based passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér–Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér–Rao bound and significantly improve the localization performance compared with the conventional methods.
Traffic Signal Management using Maximum Flow Approach for Consecutive Intersections

Urban streets are characterized by continuous intersections. When there are traffic jams, wide areas are affected. Among many attempts to solve this problem, one is Traffic Signal Management. Our approach focuses on traffic signal control for consecutive intersections. First, we transform intersections to a flow network and modify density of streets to capacity in a flow network. Then, we calculate a maximum flow. After that, we calculate the flow which passes outbound streets of each inbound street and set how long a green light signal turn on at each inbound street. After many experiments, when at least half of inbound streets are high-density, we found that using our approach results in higher traffic flow than fixed-time and video processing approach.
Influence of Arc Discharge on Contact Erosion and Contact Resistance in a Hybrid DC Switch

Contact erosion due to an arc discharge is a critical problem particularly for a DC switch or a DC circuit breaker. Longer duration arc causes higher degradation of their reliability and lifetime. A hybrid DC switch composed of mechanical switch and semiconductor device decreases the duration of the arc, comparing to conventional mechanical switch. The aim of this work is to examine the influence of arc-discharge duration on the electrode erosion and resistance in opening contacts of hybrid DC switch. In experiment, DC power source and SiC-MOSFET device were connected in parallel with the contact pair. The main current flowing through the contacts varied from 200 to 800 A. When the contacts started separating and the SiC-MOSFET turned on simultaneously, the arc occurred between the contacts and commutated the circuit current from the contacts to the SiC-MOSFET until it extinguished. The arc voltage and current waveforms together with the contact resistance were measured. The contact surface erosion was observed by a scanning electron microscope.
Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges, and Countermeasures

Location-based services (LBSs) have gained popularity as a result of the advances in mobile and communication technologies. LBS provide users with relevant information based on their location. In spite of the desirable features provided by LBS, the geographic locations of users are not adequately protected. Location privacy is one of the major challenges in vehicular and mobile networks. In this paper, we analyze the security and privacy requirements for LBS in vehicular and mobile networks. Specifically, this paper covers privacy enhancing technologies and cryptographic approaches that provide location privacy in vehicular and mobile networks. The different approaches proposed in literature are compared and open research areas are identified.
A Quantitative Approach to Design Special Purpose Systems to Measure Hacking Skills

Accurate measure of hacking skills level of a person allows to develop a response plan, whether it is for a cyber security learners or facing an adversary. Numerous attempts have been made to rank hackers, qualitatively. We introduce a quantitative approach that calculates the probability of performing certain cyber security actions and match them to well-known hackers ranks. The calculated probabilities are used to design a special-purpose system that measures the hackers' skills level and rank them. Our system uses a machine-learning approach - it starts with statistically calculated or observed initial values, then it can be trained using data generated either by calculating a weighted sum using a neural networks or by further statistics-based estimation and observation. The initial values were gathered and calculated against the participants of a number of hacking competitions. The probabilistic system is controlled in a manner that it allows the admin/owner to design tasks and challenges with a specific difficulty to examine the systems users skills. Alternatively, it can be designed in an open-end manner which can rank the user's skill based on the level reached. To demonstrate our system, we analysed the hacking skills for 8 university students based on their training results.
Estimation of Multiple Flexibilities of an Articulated System Using Inertial Measurements

For many articulated systems (i.e. systems composed of several mechanically connected objects), the assumption of full rigidity is only a mere approximation. The various flexibilities of the structure, if not accounted for, all hinder the positioning ability of the device, by generating biases in the estimations determined from rigid models. In this paper, we propose a sensor-based methodology for estimating the flexibilities of an open kinematic chain. To estimate the real position and orientation of the elements of the system, we reconcile data from Inertial Measurement Units (IMU) with the kinematics of the rigid system. We show that, under a model of punctual, spring-like deformations, this methodology allows one to observe all the deformations, if one IMU is installed downstream of each deformation in the chain. We design and test such an observer in simulation and on an exoskeleton, where it proved a suitable way of estimating the position of the flying foot. Experimental results, validated against a motion capture device, demonstrate the ability of this observer to fully capture the dynamics induced by these flexibilities.
Robust 2D Indoor Localization Through Laser SLAM and Visual SLAM Fusion

An approach of robust localization for mobile robot working in indoor is proposed in this paper. A novel method for laser SLAM and visual SLAM fusion is introduced to provide robust localization. This architecture can be applied to a situation where any two kinds of laser-based SLAM and monocular camera-based SLAM can be fused together instead of being limited to single specific SLAM algorithm. While laser-based SLAM and monocular camera-based SLAM have their own strengths and drawbacks, the integration of these two kinds of SLAM algorithm can then promote the algorithmic effectiveness. Instead of using feature matching methods to achieve fusion procedure, trajectories matching is proposed with an attempt to achieve the generalization over all different kinds of SLAM algorithms, since localization is a natural function associated with any SLAM algorithm. It turns out that the hereby proposed approach is very lightweight during the run time, and the calculation can run in real-time without unnecessary computation waste. The experimental results show the localization error in terms of the real distance can be less than 5%. Furthermore, through the experiment the proposed system can be shown able to improve the localization when the sensors are not very powerful.
Multi-stream Convolutional Neural Networks for Action Recognition in Video Sequences Based on Adaptive Visual Rhythms

Advances in digital technology have increased event recognition capabilities through the development of devices with high resolution, small physical dimensions and high sampling rates. The recognition of complex events in videos has several relevant applications, particularly due to the large availability of digital cameras in environments such as airports, banks, roads, among others. The large amount of data produced is the ideal scenario for the development of automatic methods based on deep learning. Despite the significant progress achieved through image-based deep networks, video understanding still faces challenges in modeling spatio-temporal relations. In this work, we address the problem of human action recognition in videos. A multi-stream network is our architecture of choice to incorporate temporal information, since it may benefit from pre-trained deep networks for images and from handcrafted features for initialization. Furthermore, its training cost is usually lower than video-based networks. We explore visual rhythm images since they encode longer-term information when compared to still frames and optical flow. We propose a novel method based on point tracking for deciding the best visual rhythm direction for each video. Experiments conducted on the challenging UCF101 and HMDB51 data sets indicate that our proposed stream improves network performance, achieving accuracy rates comparable to the state-of-the-art approaches.
Deep Learning-Based Beam Management and Interference Coordination in Dense mmWave Networks

Due to severe signal pathloss in millimeter wave (mmWave) band, beamforming enabled directional transmission is critical to overcome the attenuation challenge in future mmWave communication systems. Furthermore, in order to improve signal coverage of mmWave networks, network densification has to be used at the same time. However, the concurrent use of directional transmission and network densification will make the radio resource management (RRM) of dense mmWave network dramatically more complicated than that of microwave network. In order to maximize the sum-rate of the entire network, tedious and complex RRM algorithms are usually needed to obtain good results, which require high complexity of computation. To address this challenge, we proposed a deep learning-based beam management and interference coordination (BM-IC) method in dense mmWave network, through which the conventional complex BM-IC algorithm is transformed into a deep neural network (DNN)-based approximation. Because DNN only requires a series of simple calculations (e.g., some additions and multiplications), the complexity of computation is greatly reduced. Simulation results show that the proposed deep learning-based BM-IC approach can obtain comparable sum-rate to conventional BM-IC algorithm, but with much less computation time. Thus, deep learning could be a powerful tool to mitigate the complexity of RRM problems in dense mmWave networks.