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.

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.

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.

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.

A consecutive-k-out-of-n:G system consists of n components which are arranged in a line and the system works if and only if at least k consecutive components work. This paper discusses the optimization problems for a consecutive-k-out-of-n:G system. We first focus on the optimal number of components at the system design phase. Then, we focus on the optimal replacement time at the system operation phase by considering a preventive replacement, which the system is replaced at the planned time or the time of system failure which occurs first. The expected cost rates of two optimization problems are considered as objective functions to be minimized. Finally, we give study cases for the proposed optimization problems and evaluate the feasibility of the policies.

The interval in ℕ composed of finite states of the stream version of asymmetric binary systems (ABS) is irreducible if it admits an irreducible finite-state Markov chain. We say that the stream version of ABS is irreducible if its interval is irreducible. Duda gave a necessary condition for the interval to be irreducible. For a probability vector (p,1-p), we assume that p is irrational. Then, we give a necessary and sufficient condition for the interval to be irreducible. The obtained conditions imply that, for a sufficiently small ε, if p∈(1/2,1/2+ε), then the stream version of ABS could not be practically irreducible.

Device-to-device (D2D) content delivery reduces the energy consumption of frequent content retrieval in future content-centric cellular networks based on proximal content delivery. Compared with unicast, multicast may be more efficient since it serves the content requests of multiple users simultaneously. The serving efficiency mainly depends on the selection of multicast transmitter, which has not been well addressed. In this letter, we consider the match degree between the multicast content of transmitter and the required content of receiver based on social relationship between transceivers. By integrating the effects of communication environments and match degree into the selection procedure, a multicast UE selection scheme is proposed to improve the number of benefited receivers from D2D multicast. Simulation results show that the proposed scheme can efficiently improve the performance of D2D multicast content delivery under different communication environments.

This paper proposes a successive interference cancellation (SIC) of independent component analysis (ICA) aided spatial division multiple access (SDMA) for Gaussian filtered frequency shift keying (GFSK) in Bluetooth low energy (BLE) systems. The typical SDMA scheme requires estimations of channel state information (CSI) using orthogonal pilot sequences. However, the orthogonal pilot is not embedded in the BLE packet. This fact motivates us to add ICA detector into BLE systems. In this paper, focusing on the covariance matrix of ICA outputs, SIC can be applied with Cholesky decomposition. Then, in order to address the phase ambiguity problems created by the ICA process, we propose a differential detection scheme based on the MAP algorithm. In practical scenarios, it is subject to carrier frequency offset (CFO) as well as symbol timing offset (STO) induced by the hardware impairments present in the BLE peripherals. The packet error rate (PER) performance is evaluated by computer simulations when BLE peripherals simultaneously communicate in the presence of CFO and STO.

The conventional digital video encoding and transmission are inefficient for crowdsourced multi-view video uploading due to its high power consumption, and undesirable quality degradation in unstable wireless channel. Soft video delivery scheme known as SoftCast skips digital video encoding and transmission to decrease power consumption in video encoding and transmission. In addition, it achieves graceful quality improvement with the improvement of wireless channel quality by directly sending linear-transformed video signals. However, there are two typical issues to apply conventional soft video delivery to crowdsourced multi-view video uploading. First, since soft video delivery has been designed for direct path between each contributor and the access point (AP), it may suffer low video quality when a contributor uploads its video to the AP over unstable direct wireless path. Second, conventional soft video delivery may suffer low video quality due to the redundant transmission of correlated videos because it does not exploit inter-camera correlations existed in multi-view videos. In this paper, we propose a cluster-based redirect video uploading scheme for high-quality and low-power crowdsourced multi-view video streaming. The proposed scheme integrates the four approaches of network clustering, delegate selection, soft video delivery, and four-dimensional discrete consine transform (4D-DCT) to redirectly upload the captured videos to the AP. Specifically, network clustering and delegate selection leverage the redirect path between the contributors and the AP. Soft video delivery removes power-hungry digital encoding and transmission by directly sending frequency-domain coefficients using multi-dimensional DCT and near-analog modulation. 4D-DCT exploits the content correlations between the contributors to reduce redundant transmissions. Evaluation results show that our proposed scheme outperforms the conventional soft video delivery scheme when the channel quality difference between the direct and redirect paths increases. In addition, our scheme outperforms the digital-based video uploading schemes in terms of both video quality and power consumption. For example, the proposed scheme yields graceful quality improvement with the improvement of wireless channel quality, however, the digital-based schemes suffer from sudden quality degradation due to synchronization errors in decoding.

A lumped element kinetic inductance detector (LeKID) relying on a superconducting resonator is a promising candidate for sensing high energy particles such as neutrinos, X-rays, gamma-rays, alpha particles, and the particles found in the dark matter owing to its large-format capability and high sensitivity. To develop a high energy camera, we formulated design rules based on the experimental results from niobium (Nb)-based LeKIDs at 1 K irradiated with alpha-particles of 5.49 MeV. We defined the design rules using the electromagnetic simulations for minimizing the crosstalk. The neighboring pixels were fixed at 150 µm with a frequency separation of 250 MHz from each other to reduce the crosstalk signal as low as the amplifier-limited noise level. We examined the characteristics of the Nb-based resonators, where the signal decay time was controlled in the range of 0.5-50 µs by changing the designed quality factor of the detectors. The amplifier noise was observed to restrict the performance of our device, as expected. We improved the energy resolution by reducing the filling factor of inductor lines. The best energy resolution of 26 for the alpha particle of 5.49 MeV was observed in our device.

Superconducting nanowire single-photon detectors(SSPDs or SNSPDs) can detect single photons in a wide spectrum range from ultraviolet to mid-infrared wavelengths. We developed SSPDs for the light wavelength of 900-1100 nm, where it is difficult to achieve high detection efficiency by either Si or InGaAs avalanche photodiodes. We designed and fabricated the SSPD with non-periodic dielectric multilayers (DMLs) composed of SiO2 and TiO2 to enhance the optical absorptance in the wavelength range of 900-1100 nm. We measured the detection efficiency (DE) in the wavelength range of 800-1360 nm using a supercontinuum light source and found that the wavelength dependence of DE was in good agreement with the simulated spectrum of the optical absorptance of the nanowire device on the designed DML. The highest system DE was 81.0% for the wavelength of 980 nm.

A new design approach of broadband RF power amplifier (PA) is introduced in this work with combination of large signal X-parameter and Real-Frequency Technique (RFT). A theoretical analysis of large signal X-parameter is revisited, and a simplification method is introduced to determine the optimum large signal impedances of a Gallium Nitride HEMT (GaN HEMT) device. With the optimum impedance extraction over the wide frequency range (0.3 to 2.0 GHz), a wideband matching network is constructed employing RFT and the final design is implemented with practical mixed-lumped elements. The prototype broadband RF PA demonstrates an output power of 40 dBm. The average drain efficiency of the PA is found to be more than 60%; while exhibiting acceptable flat gain performance (12±0.25 dB) over the frequency band of (0.3-2.0 GHz). The PA designed using the proposed approach yields in small form factor and relatively lower production cost over those of similar PAs designed with the classical methods. It is expected that the newly proposed design method will be utilized to construct power amplifiers for radio communications applications.

In this paper, a Happiness Cups (H-cups) system is proposed to bi-directionally convey the holding-cup motions of paired cups between two remote users. To achieve this goal, the H-cups system uses three important components. Firstly, paired cups are embedded with accelerometers and gyro sensors to transmit the three-dimensional acceleration and angular signals to a motion recognizer application. This application is designed on an Android phone. The sensors then receive the remotely recognized motions and flash a specific color on the local cup's RGB-LED via Bluetooth. Secondly, the application considers holding-cup motion recognition from the cup based on long short-term memory (LSTM) and sends the local recognized motion through an intermediate server to transmit to the remote paired cup via the internet. Finally, an intermediate server is established and used to exchange and forward the recognized holding-cup motions between two paired cups, in which five holding-cup motions, including drinking, horizontal shaking, vertical shaking, swaying and raising toasts are proposed and recognized by LSTM. The experimental results indicate that the recognition accuracy of the holding-cup motion can reach 97.3% when using our method.

As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.

For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the Web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.

Side-channel Attack, such as simple power analysis and differential power analysis (DPA), is an efficient method to gather the key, which challenges the security of crypto chips. Side-channel Attack logs the power trace of the crypto chip and speculates the key by statistical analysis. To reduce the threat of power analysis attack, an innovative method based on random execution and register randomization is proposed in this paper. In order to enhance ability against DPA, the method disorders the correspondence between power trace and operands by scrambling the data execution sequence randomly and dynamically and randomize the data operation path to randomize the registers that store intermediate data. Experiments and verification are done on the Sakura-G FPGA platform. The results show that the key is not revealed after even 2 million power traces by adopting the proposed method and only 7.23% slices overhead and 3.4% throughput rate cost is introduced. Compared to unprotected chip, it increases more than 4000× measure to disclosure.

Linear feed-forward/feedback shift registers are used as an effective tool of testing circuits in various fields including built-in self-test and secure scan design. In this paper, we consider the issue of testing linear feed-forward/feedback shift registers themselves. To test linear feed-forward/feedback shift registers, it is necessary to generate a test sequence for each register. We first present an experimental result such that a commercial ATPG (automatic test pattern generator) cannot always generate a test sequence with high fault coverage even for 64-stage linear feed-forward/feedback shift registers. We then show that there exists a universal test sequence with 100% of fault coverage for the class of linear feed-forward/feedback shift registers so that no test generation is required, i.e., the cost of test generation is zero. We prove the existence theorem of universal test sequences for the class of linear feed-forward/feedback shift registers.