The liquid metal current limiter (LMCL) is a possible alternative to limit the short current of power system due to its special merits. This paper is devoted to the investigation of the arc behavior in liquid metal GaInSn for current limiting application. Firstly, the arc evolution including arc initiation, arc expansion and arc extinguish is observed through an experimental device. The resistance of arc and the self healing property of liquid metal are described. Subsequently, the arc erosion on electrodes is presented with its causes analyzed. Finally, the arc characteristics with the influence of rise rate of prospective current and channel diameter are discussed in details.

Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.

Robot motion planning is an important part of the unmanned supermarket. The challenges of motion planning in supermarkets lie in the diversity of the supermarket environment, the complexity of obstacle movement, the vastness of the search space. This paper proposes an adaptive Search and Path planning method based on the Semantic information and Deep reinforcement learning (SPSD), which effectively improves the autonomous decision-making ability of supermarket robots. Firstly, based on the backbone of deep reinforcement learning (DRL), supermarket robots process real-time information from multi-modality sensors to realize high-speed and collision-free motion planning. Meanwhile, in order to solve the problem caused by the uncertainty of the reward in the deep reinforcement learning, common spatial semantic relationships between landmarks and target objects are exploited to define reward function. Finally, dynamics randomization is introduced to improve the generalization performance of the algorithm in the training. The experimental results show that the SPSD algorithm is excellent in the three indicators of generalization performance, training time and path planning length. Compared with other methods, the training time of SPSD is reduced by 27.42% at most, the path planning length is reduced by 21.08% at most, and the trained network of SPSD can be applied to unfamiliar scenes safely and efficiently. The results are motivating enough to consider the application of the proposed method in practical scenes. We have uploaded the video of the results of the experiment to https://www.youtube.com/watch?v=h1wLpm42NZk.

Supporting quality-of-service (QoS) of multimedia communications over IEEE 802.11 based ad hoc networks is a challenging task. This paper develops a simple 3-D Markov chain model for queuing analysis of IEEE 802.11 MAC layer. The model is applied for performance analysis of voice communications over IEEE 802.11 single-hop ad hoc networks. By using the model, we finish the performance optimization of IEEE MAC layer and obtain the maximum number of voice calls in IEEE 802.11 ad hoc networks as well as the statistical performance bounds. Furthermore, we design a fully distributed call admission control (CAC) algorithm which can provide strict statistical QoS guarantee for voice communications over IEEE 802.11 ad hoc networks. Extensive simulations indicate the accuracy of the analytical model and the CAC scheme.

An empirical Bayesian method was used to obtain a point estimator for the reliability function of a bivariate exponential distribution associated to a two-component parallel electronic system.

In order to realize an Internet-of-Things (IoT) with tiny sensors integrated in our buildings, our clothing, and the public spaces, battery lifetime and battery size remain major challenges. Power reduction in IoT sensor nodes is determined by both sleep mode as well as active mode contributions. A power state machine, at the system level, is the key to achieve ultra-low average power consumption by alternating the system between active and sleep modes efficiently. While, power consumption in the active mode remains dominant, other power contributions like for timekeeping in standby and sleep conditions are becoming important as well. For example, non-conventional critical blocks, such as crystal oscillator (XO) and resistor-capacitor oscillator (RCO) become more crucial during the design phase. Apart from power reduction, low-voltage operation will further extend the battery life. A 2.4GHz multi-standard radio is presented, as a test case, with an average power consumption in the µW range, and state-of-the-art performance across a voltage supply range from 1.2V to 0.9V.

A gold coated carbon nanotubes composite was used as a contact material in Micro-Electrical-Mechanical-System (MEMS) switches. The switching contact was tested under typical conditions of MEMS relay applications: load voltage of 4 V, contact force of 1 mN, and load current varied between 20-200 mA. This paper focuses on the wear process over switching lifetime, and the dependence of the wear area on the current is discussed. It was shown that the contact was going to fail when the wear area approached the whole contact area, at which point the contact resistance increased sharply to three times the nominal resistance.

Wi-Fi-based person identification (PI) tasks are performed by analyzing the fluctuating characteristics of the Channel State Information (CSI) data to determine whether the person's identity is legitimate. This technology can be used for intrusion detection and keyless access to restricted areas. However, the related research rarely considers the restricted computing resources and the complexity of real-world environments, resulting in lacking practicality in some scenarios, such as intrusion detection tasks in remote substations without public network coverage. In this paper, we propose a novel neural network model named SimpleViTFi, a lightweight classification model based on Vision Transformer (ViT), which adds a downsampling mechanism, a distinctive patch embedding method and learnable positional embedding to the cropped ViT architecture. We employ the latest IEEE 802.11ac 80MHz CSI dataset provided by [1]. The CSI matrix is abstracted into a special “image” after pre-processing and fed into the trained SimpleViTFi for classification. The experimental results demonstrate that the proposed SimpleViTFi has lower computational resource overhead and better accuracy than traditional classification models, reflecting the robustness on LOS or NLOS CSI data generated by different Tx-Rx devices and acquired by different monitors.

We present a new method to detect weak linear frequency modulated (LFM) signals in strong noise using the chaos oscillator. Chaotic systems are sensitive to specific signals yet immune to noise. With our new method we firstly use the Radon-Wigner transform to dechirp the LFM signal. Secondly, we set up a chaotic oscillator sensitive to weak signals based on the Duffing equation, and poising the system at its critical state. Finally, we input the dechirped sequence into the system as a perturbation of the driving force. A weak signal with the same frequency will lead to a qualitative transition in the system state. The weak signal in the presence of strong noise can then be detected from the phase transition of the phase plane trajectory of the chaotic system. Computer simulation results show that LFM signals with an SNR lower than -27 dB can be detected by this method.

We present the design of a secure identifier-based inter-domain routing, SIR, for the identifier/locator split network. On the one hand, SIR is a distributed path-vector protocol inheriting the flexibility of BGP. On the other hand, SIR separates ASes into several groups called trust groups, which assure the trust relationships among ASes by enforceable control and provides strict isolation properties to localize attacks and failures. Security analysis shows that SIR can provide control plane security that can avoid routing attacks including some smart attacks which S-BGP/soBGP can be deceived. Meanwhile, emulation experiments based on the current Internet topology with 47,000 ASes from the CAIDA database are presented, in which we compare the number of influenced ASes under attacks of subverting routing policy between SIR and S-BGP/BGP. The results show that, the number of influenced ASes decreases substantially by deploying SIR.

More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.

In this paper, we propose a high-throughput binary arithmetic coding architecture for CABAC (Context Adaptive Binary Arithmetic Coding) which is one of the entropy coding tools used in the H.264/AVC main and high profiles. The full CABAC encoding functions, including binarization, context model selection, arithmetic encoding and bits generation, are implemented in this proposal. The binarization and context model selection are implemented in a proposed binarizer, in which a FIFO is used to pack the binarization results and output 4 bins in one clock. The arithmetic encoding and bits generation are implemented in a four-stage pipeline with the encoding ability of 4 bins/clock. In order to improve the processing speed, the context variables access and update for 4 bins are paralleled and the pipeline path is balanced. Also, because of the outstanding bits issue, a bits packing and generation strategy for 4 bins paralleled processing is proposed. After implemented in verilog-HDL and synthesized with Synopsys Design Compiler using 90 nm libraries, this proposal can work at the clock frequency of 250 MHz and takes up about 58 K standard cells, 3.2 Kbits register files and 27.6 K bits ROM. The throughput of processing 1000 M bins per second can be achieved in this proposal for the HDTV applications.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

Point spread function (PSF) estimation plays a paramount role in image deblurring processing, and traditionally it is solved by parameter estimation of a certain preassumed PSF shape model. In real life, the PSF shape is generally arbitrary and complicated, and thus it is assumed in this manuscript that a PSF may be decomposed as a weighted sum of a certain number of Gaussian kernels, with weight coefficients estimated in an alternating manner, and an l1 norm-based total variation (TVl1) algorithm is adopted to recover the latent image. Experiments show that the proposed method can achieve satisfactory performance on synthetic and realistic blurred images.

Ni nanodots (NDs) used as nano-scale top electrodes were formed on a 10-nm-thick Si-rich oxide (SiO$_{mathrm{x}}$)/Ni bottom electrode by exposing a 2-nm-thick Ni layer to remote H$_{2}$-plasma (H$_{2}$-RP) without external heating, and the resistance-switching behaviors of SiO$_{mathrm{x}}$ were investigated from current-voltage ( extit{I--V}) curves. Atomic force microscope (AFM) analyses confirmed the formation of electrically isolated Ni NDs as a result of surface migration and agglomeration of Ni atoms promoted by the surface recombination of H radicals. From local extit{I--V} measurements performed by contacting a single Ni ND as a top electrode with a Rh coated Si cantilever, a distinct uni-polar type resistance switching behavior was observed repeatedly despite an average contact area between the Ni ND and the SiO$_{mathrm{x}}$ as small as $sim$ 1.9 $ imes$ 10$^{-12}$cm$^{2}$. This local extit{I--V} measurement technique is quite a simple method to evaluate the size scalability of switching properties.

MapReduce still suffers from a problem known as skew, where load is unevenly distributed among tasks. Existing solutions follow a similar pattern that estimates the load of each task and then rebalances the load among tasks. However, these solutions often incur heavy overhead due to the load estimation and rebalancing. In this paper, we present DynamicAdjust, a dynamic resource adjustment technique for mitigating skew in MapReduce. Instead of rebalancing the load among tasks, DynamicAdjust adjusts resources dynamically for the tasks that need more computation, thereby accelerating these tasks. Through experiments using real MapReduce workloads on a 21-node Hadoop cluster, we show that DynamicAdjust can effectively mitigate the skew and speed up the job completion time by up to 37.27% compared to the native Hadoop YARN.

A Bayesian technique was used to obtain point estimators for the parameters of a bivariate exponential distribution associated to a two-component parallel electronic system and a point estimator for the system reliability function.

This paper proposes a novel greedy algorithm, called Creditability-Estimation based Matching Pursuit (CEMP), for the compressed sensing signal recovery. As proved in the algorithm of Stagewise Orthogonal Matching Pursuit (StOMP), two Gaussian distributions are followed by the matched filter coefficients corresponding to and without corresponding to the actual support set of the original sparse signal, respectively. Therefore, the selection for each support point is interpreted as a process of hypothesis testing, and the preliminarily selected support set is supposed to consist of rejected atoms. A hard threshold, which is controlled by an input parameter, is used to implement the rejection. Because the Type I error may happen during the hypothesis testing, not all the rejected atoms are creditable to be the true support points. The creditability of each preliminarily selected support point is evaluated by a well-designed built-in mechanism, and the several most creditable ones are adaptively selected into the final support set without being controlled by any extra external parameters. Moreover, the proposed CEMP does not necessitate the sparsity level to be a priori control parameter in operation. In order to verify the performance of the proposed algorithm, Gaussian and Pulse Amplitude Modulation sparse signals are measured in the noiseless and noisy cases, and the experiments of the compressed sensing signal recoveries by several greedy algorithms including CEMP are implemented. The simulation results show the proposed CEMP can achieve the best performances of the recovery accuracy and robustness as a whole. Besides, the experiment of the compressed sensing image recovery shows that CEMP can recover the image with the highest Peak Signal to Noise Ratio (PSNR) and the best visual quality.

Small cell networks have been promoted as an enabling solution to enhance indoor coverage and improve spectral efficiency. Users usually deploy small cells on-demand and pay no attention to global profile in residential areas or offices. The reduction of cell radius leads to dense deployment which brings intractable computation complexity for resource allocation. In this paper, we develop a semi-distributed resource allocation algorithm by dividing small cell networks into clusters with limited inter-cluster interference and selecting a reference cluster for interference estimation to reduce the coordination degree. Numerical results show that the proposed algorithm can maintain similar system performance while having low complexity and reduced information exchange overheads.

We have analyzed the reflection characteristic of a T junction composed of two parallel plate waveguides in which the one is the anisotropic plasma filled waveguide and the other is the exciting waveguide. The fields in two waveguides can be expanded by mode functions and the matching of the E and H fields at the junction in the Fourier transform leads to a linear simultaneous equation for the reflection coefficients. We solved the equation and obtained the reflection coefficients numerically.