In this paper, experimental data of non-arcing circuit breaking phenomena in electrical contacts are presented. A dark bridge that is a non-luminous bridge between electrical contacts is an effective factor for the non-arcing circuit break. A facility of a cantilever system was established to precisely control a position of an electrode. By using this facility, dark bridges between contacts were made and the dark bridges were observed by a microscopic camera system.

The packet error rate (PER) performance of multi-hop STBC based cooperative and diversity relaying systems are studied. These systems consist of a source, a destination, and two relay stations in each hop. From in-lab experiments, it is confirmed that the cooperative relaying system has better PER performance than the diversity relaying system with highly correlated channels.

In this prompt report, we present the basic performance evaluation of Intel Optane Data Center Persistent Memory Module (Optane DCPMM), which is the first commercially-available, byte-addressable non-volatile memory modules released in April 2019. Since at the moment of writing only a few reports on its performance were published, this letter is intended to complement other performance studies. Through experiments using our own measurement tools, we obtained that the latency of random read-only access was approximately 374 ns. That of random writeback-involving access was 391 ns. The bandwidths of read-only and writeback-involving access for interleaved memory modules were approximately 38 GB/s and 3 GB/s, respectively.

An electrical capacitance tomography (ECT) method was used to detect fretting wear behavior of electrical connectors. The specimens used in this study were contacts of type-M round two-pin electrical connectors. The experiments consisted of running a series of vibration tests at each frequency combined with one g levels. During each test run, the measured capacitance per pair of electrodes was monitored as a performance characteristic, which is induced by the wear debris generated by the fretting wear of electrical connectors. The fretted surface is examined using scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS) analysis to assess the surface profile, extent of fretting damage and elemental distribution across the contact zone and then compared to the capacitance values. The results exhibit that with the increase of the fretting cycles or the vibration frequency, the characteristic value of the wear debris between the contacts of electrical connector gradually increases and the wear is more serious. Measured capacitance values are consistent with SEM and EDS analysis.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

Periodic sequences, used as keys in cryptosystems, plays an important role in cryptography. Such periodic sequences should possess high linear complexity to resist B-M algorithm. Sequences constructed by cyclotomic cosets have been widely studied in the past few years. In this paper, the linear complexity of n-periodic cyclotomic sequences of order 2 and 4 over 𝔽p has been calculated, where n and p are two distinct odd primes. The conclusions reveal that the presented sequences have high linear complexity in many cases, which indicates that the sequences can resist the linear attack.

For high definition (HD) videos, the 3D-High Efficiency Video Coding (3D-HEVC) reference algorithm incurs dramatically highly computation loads. Therefore, with the demands for the real-time processing of HD video, a hardware implementation is necessary. In this paper, a reconfigurable architecture is proposed that can support both median filtering preprocessing and mean filtering preprocessing to satisfy different scene depth maps. The architecture sends different instructions to the corresponding processing elements according to different scenarios. Mean filter is used to process near-range images, and median filter is used to process long-range images. The simulation results show that the designed architecture achieves an averaged PSNR of 34.55dB for the tested images. The hardware design for the proposed virtual view synthesis system operates at a maximum clock frequency of 160MHz on the BEE4 platform which is equipped with four Virtex-6 FF1759 LX550T Field-Programmable Gate Array (FPGA) for outputting 720p (1024×768) video at 124fps.

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.

Difference systems of sets (DSSs) introduced by Levenstein are combinatorial structures used to construct comma-free codes for synchronization. In this letter, two classes of optimal DSSs are presented. One class is obtained based on q-ary ideal sequences with d-form property and difference-balanced property. The other class of optimal and perfect DSSs is derived from perfect ternary sequences given by Ipatov in 1995. Compared with known constructions (Zhou, Tang, Optimal and perfect difference systems of sets from q-ary sequences with difference-balanced property, Des. Codes Cryptography, 57(2), 215-223, 2010), the proposed DSSs lead to comma-free codes with nonzero code rate.

Figure-ground (FG) segregation has been considered as a fundamental step towards object recognition. We explored plausible mechanisms that estimate global figure-ground segregation from local image features by investigating the human visual system. Physiological studies have reported border-ownership (BO) selective neurons in V2 which signal the local direction of figure (DOF) along a border; however, how local BO signals contribute to global FG segregation has not been clarified. The BO and FG processing could be independent, dependent on each other, or inseparable. The investigation on the differences and similarities between the BO and FG judgements is important for exploring plausible mechanisms that enable global FG estimation from local clues. We performed psychophysical experiments that included two different tasks each of which focused on the judgement of either BO or FG. The perceptual judgments showed consistency between the BO and FG determination while a longer distance in gaze movement was observed in FG segregation than BO discrimination. These results suggest the involvement of distinct neural mechanism for local BO determination and global FG segregation.

An LLC converter has high efficiency and low noise and has thus recently attracted attention in the field of power supplies for use in information and communication systems. A planar transformer is thought to be particularly effective in a high-frequency switching power supply because an ideal primary-secondary interleave structure can be formed by the multilayer structure, and the alternating-current (AC) resistance can be reduced. Based on these facts, we investigated the use of planar transformers in LLC converters. However, high-frequency oscillation, which is not observed in a normal winding transformer, appears in the secondary side current, and the power supply loss is also higher. Our investigation found that the current oscillation and an increase in loss were caused by a primary-secondary capacitance of the transformer. This paper presents countermeasures used to reduce the capacitance between the primary and secondary windings, and a new layer structure for the transformer that reduces the capacitance. The loss is calculated through a simulation and experimentally, and good agreement is obtained. The proposed transformer offers the high efficiency of 98.1% in a 200 W, 12 V output power supply.

The neural network has been one of the most useful techniques in the area of speech recognition, language translation and image analysis in recent years. Long Short-Term Memory (LSTM), a popular type of recurrent neural networks (RNNs), has been widely implemented on CPUs and GPUs. However, those software implementations offer a poor parallelism while the existing hardware implementations lack in configurability. In order to make up for this gap, a highly configurable 7.62 GOP/s hardware implementation for LSTM is proposed in this paper. To achieve the goal, the work flow is carefully arranged to make the design compact and high-throughput; the structure is carefully organized to make the design configurable; the data buffering and compression strategy is carefully chosen to lower the bandwidth without increasing the complexity of structure; the data type, logistic sigmoid (σ) function and hyperbolic tangent (tanh) function is carefully optimized to balance the hardware cost and accuracy. This work achieves a performance of 7.62 GOP/s @ 238 MHz on XCZU6EG FPGA, which takes only 3K look-up table (LUT). Compared with the implementation on Intel Xeon E5-2620 CPU @ 2.10GHz, this work achieves about 90× speedup for small networks and 25× speed-up for large ones. The consumption of resources is also much less than that of the state-of-the-art works.

Semi-bent functions have almost maximal nonlinearity. In this paper, two classes of semi-bent functions are constructed by modifying the supports of two quadratic Boolean functions $f_1(x_1,x_2,cdots,x_n)=igopluslimits^{k}_{i=1}x_{2i-1}x_{2i}$ with $n=2k+1geq3$ and $f_2(x_1,x_2,cdots,x_n)=igopluslimits^{k}_{i=1}x_{2i-1}x_{2i}$ with $n=2k+2geq4$. Meanwhile, the algebraic normal forms of the newly constructed semi-bent functions are determined.

We measured eye movements at gaze points while subjects performed calculation tasks and examined the relationship between the eye movements and fatigue and/or internal state of a subject by tasks. It was suggested that fatigue and/or internal state of a subject affected eye movements at gaze points and that we could measure them using eye movements at gaze points in real time.

This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a simultaneous wireless information and power transfer (SWIPT) system, wherein a legitimate user (LU) exploits the power splitting (PS) scheme for simultaneous information decoding (ID) and energy harvesting (EH). To prevent interference from eavesdroppers on the LU, artificial noise (AN) is incorporated into the confidential signal at the transmitter. We maximize the secrecy energy efficiency (SEE) by joining the power of the confidential signal, the AN power, and the PS ratio, while taking into account the minimum secrecy rate requirement of the LU, the required minimum harvested energy, the allowed maximum radio frequency transmission power, and the PS ratio. The formulated SEEM problem involves nonconvex fractional programming and is generally intractable. Our solution is Lagrangian relaxation method than can transform the original problem into a two-layer optimization problem. The outer layer problem is a single variable optimization problem with a Lagrange multiplier, which can be solved easily. Meanwhile, the inner layer one is fractional programming, which can be transformed into a subtractive form solved using the Dinkelbach method. A closed-form solution is derived for the power of the confidential signal. Simulation results verify the efficiency of the proposed SEEM algorithm and prove that AN-aided design is an effective method for improving system SEE.

Media Access Control (MAC) is critical to guarantee different Quality of Service (QoS) requirements for Unmanned Aerial Vehicle (UAV) networks, such as high reliability for safety packets and high throughput for service packets. Meanwhile, due to their ability to provide lower delay and higher data rates, more UAVs are using frequently directional antennas. However, it is challenging to support different QoS in UAV networks with directional antennas, because of the high mobility of UAV which causes serious channel resource loss. In this paper, we propose CU-MAC which is a MAC protocol for Centralized UAV networks with directional antennas. First, we design a mobility prediction based time-frame optimization scheme to provide reliable broadcast service for safety packets. Then, a traffic prediction based channel allocation scheme is proposed to guarantee the priority of video packets which are the most common service packets nowadays. Simulation results show that compared with other representative protocols, CU-MAC achieves higher reliability for safety packets and improves the throughput of service packets, especially video packets.

The carrier-phase multipath effect can seriously affect the accuracy of GPS-based positioning in static short baseline applications. Although several kinds of methods based on time domain and spatial domain techniques have been proposed to mitigate this error, they are still limited by the accuracy of the multipath model and the effectiveness of the correction strategy. After analyzing the existing methods, a new method based on adaptive thresholding wavelet packet transform (AW) and time domain bootstrap spatial domain search strategy (TB) is presented (AWTB). Taking advantage of adaptive thresholding wavelet packet transform, we enhance the precision of the correction model and the efficiency of the extraction method. In addition, by adopting the proposed time domain bootstrap spatial domain strategy, the accuracy and efficiency of subsequent multipath correction are improved significantly. Specifically, after applying the adaptive thresholding wavelet packet method, the mean improvement rate in the RMS values of the single-difference L1 residuals is about 27.93% compared with the original results. Furthermore, after applying the proposed AWTB method, experiments show that the 3D positioning precision is improved by about 38.51% compared with the original results. Even compared with the method based on stationary wavelet transform (SWT), and the method based on wavelet packets denoising (WPD), the 3D precision is improved by about 26.94% over the SWT method and about 22.96% over the WPD method, respectively. It is worth noting that, although the mean time consumption of the proposed algorithm is larger than the original method, the increased time consumption is not a serious burden for overall performance.

This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.

Logic encryption is an IC protection technique which inserts extra logic and key inputs to hide a circuit's functionality. An encrypted circuit needs to be activated with a secret key for being functional. SAT attack and Removal attack are two most advanced decryption methods that have shown their effectiveness to break most of the existing logic encryption methods within a few hours. In this paper, we propose SMARTLock, a SAT attack and reMoval Attack-Resistant Tree-based logic Locking method, for resisting them simultaneously. To encrypt a circuit, the method finds large AND and OR functions in it and encrypts them by inserting duplicate tree functions. There are two types of structurally identical tree encryptions that aim to resist SAT attack and Removal attack, respectively. The experimental results show that the proposed method is effective for encrypting a set of benchmarks from ISCAS'85, MCNC, and IWLS. 16 out of 40 benchmarks encrypted by the proposed method with the area overhead of no more than 5% are uncrackable by SAT attack within 5 hours. Additionally, compared to the state-of-the-art logic encryption methods, the proposed method provides better security for most benchmarks.