This paper analyzes the conventional unequal erasure protection (UXP) scheme for scalable video transmission, and proposes a dynamic hybrid UXP/ARQ transmission framework to improve the performance of the conventional UXP method for bandwidth-constrained scalable video transmission. This framework applies automatic retransmission request (ARQ) to the conventional UXP scheme for scalable video transmission, and dynamically adjusts the transmission time budget of each group of picture (GOP) according to the feedback about the transmission results of the current and previous GOPs from the receiver. Moreover, the parameter of target video quality is introduced and optimized to adapt to the channel condition in pursuit of more efficient dynamic time allocation. In addition, considering the play-out deadline constraint, the time schedule for the proposed scalable video transmission system is presented. Simulation results show that compared with the conventional UXP scheme and its enhanced method, the average peak signal to noise ratio (PSNR) of the reconstructed video can be improved significantly over a wide range of packet loss rates. Besides, the visual quality fluctuation among the GOPs can be reduced for the video which has much movement change.

A unified construction for transforming binary sequences of balance or unbalance into quaternary sequences is presented. On the one hand, when optimal and balanced binary sequences with even period are employed, our construction is exactly the same Jang, et al.'s and Chung, et al.'s ones, which result in balanced quaternary sequences with optimal autocorrelation magnitude. On the other hand, when ideal and balanced binary sequences with odd period N are made use of, our construction produces new balanced quaternary sequences with optimal autocorrelation value (OAV), in which there are N distinct sequences in terms of cyclic shift equivalence, and includes Tang, et al.'s and Jang, et al.'s ones as special cases. In addition, when binary sequences without period 2n-1 or balance are employed, the transformation of Jang, et al.'s method is invalid, however, the proposed construction works very good. As a consequence, this unified construction allows us to construct optimal and balanced quaternary sequences from ideal/optimal balanced binary sequences with arbitrary period.

This letter presents three methods for producing 8-QAM+ sequences. The first method transforms a ternary complementary sequence set (CSS) with even number of sub-sequences into an 8-QAM+ periodic CSS with both of the period and the number of sub-sequences unaltered. The second method results in an 8-QAM+ aperiodic CSS with confining neither the period nor the number of sub-sequences. The third method produces 8-QAM+ periodic sequences having ideal autocorrelation property on the real part of the autocorrelation function. The proposed sequences can be potentially applied to suppression of multiple access interference or synchronization in a communication system.

A zig-zag Gardner algorithm with parallel architecture is presented in this letter. This algorithm performs timing adjustment in each individual burst independently for high speed wireless burst communication with a short guard. Over sampling data are stored in RAM initially and read forward and backward alternately later. The proposed algorithm has distinct symmetric characteristic in the forward and backward process, which makes the alternate sequences achieve nearly the same effect as a continuous sequence. The performance of the proposed algorithm is very close to the theoretical curve.

As semiconductor manufacturing processing scaling down, leakage current of CMOS circuits is becoming a dominant contributor to power dissipation. This paper provides an efficient leakage current reduction (LCR) technique for low-power and low-frequency circuit designs in terms of design rules and layout parameters related to layout dependent effects. We address the LCR technique both for analog and digital circuits, and present a design case when applying the LCR techniqe to a successive-approximation-register (SAR) analog-to-digital converter (ADC), which typically employs analog and digital transistors. In the post-layout simulation results by HSPICE, an SAR-ADC with the LCR technique achieves 38.6-nW as the total power consumption. Comparing with the design without the LCR technique, we attain about 30% total energy reduction.

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

General purpose computing on GPU (GPGPU) has become a popular computing model for high-performance, data-intensive applications. Accordingly, there is a strong need to develop highly efficient data structures to ease the development of GPGPU applications. In this work, we proposed an efficient concurrent queue data structure for GPU computing. The GPU based provably correct, lock-free FIFO queue allows a massive number of concurrent producers and consumers. Warp-centric en-queue and de-queue procedures are introduced to better match the underlying Single-Instruction, Multiple-Thread execution model of modern GPUs. It outperforms the best previous GPU queues by up to 40 fold. The correctness of the proposed queue operations is formally validated by linearizability criteria.

Based on quadriphase perfect sequences and their cyclical shift versions, three families of almost perfect 16-QAM sequences are presented. When one of two time shifts chosen equals half a period of quadriphase sequence employed and another is zero, two of the proposed three sequence families possess the property that their out-of-phase autocorrelation function values vanish except one. At the same time, to the other time shifts, the nontrivial autocorrelation function values in three families are zero except two or four. In addition, two classes of periodic complementary sequence (PCS) pairs over the 16-QAM constellation, whose autocorrelation is similar to the one of conventional PCS pairs, are constructed as well.

In this letter, we explored the usage of spatio-temporal information in one unified framework to improve the performance of multichannel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.

This paper improves the family size of quadrature amplitude modulation (QAM) complementary sequences with binary inputs. By employing new mathematical description: B-type-2 of 4q-QAM constellation (integer q ≥ 2), a new construction yielding 4q-QAM complementary sequences (CSs) with length 2m (integer m ≥ 2) is developed. The resultant sequences include the known QAM CSs with binary inputs as special cases, and the family sizes of new sequences are approximately 22·2q-4q-1(22·2q-3-1) times as many as the known. Also, both new sequences and the known have the same the peak envelope power (PEP) upper bounds, when they are used in an orthogonal frequency-division multiplexing communication system.

We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.

With the development of blockchain technology, the automatic generation of smart contract has become a hot research topic. The existing smart contract automatic generation technology still has improvement spaces in complex process, third-party specialized tools required, specific the compatibility of code and running environment. In this paper, we propose an automatic smart contract generation method, which is domain-oriented and configuration-based. It is designed and implemented with the application scenarios of government service. The process of configuration, public state database definition, code generation and formal verification are included. In the Hyperledger Fabric environment, the applicability of the generated smart contract code is verified. Furthermore, its quality and security are formally verified with the help of third-party testing tools. The experimental results show that the quality and security of the generated smart contract code meet the expect standards. The automatic smart contract generation will “elegantly” be applied on the work of anti-disclosure, privacy protection, and prophecy processing in government service. To effectively enable develop “programmable government”.

Practical, low complexity time of arrival (TOA) estimation method with high accuracy are attractive in ultra wideband (UWB) ranging and localization. In this paper, a generalized maximum likelihood energy detection (GML-ED) ranging method is proposed and implemented. It offers low complexity and can be applied in various environments. An error model is first introduced for TOA accuracy evaluation, by which the optimal integration interval can be determined. Aiming to suppress the significant error created by the false alarm events, multiple pulses are utilized for accuracy promotion at the cost of extra energy consumption. For this reason, an energy efficiency model is also proposed based on the transmitted pulse number. The performance of the analytical research is evaluated and verified through practical experiments in a typical indoor environment.

In this paper, a Many Integrated Core Architecture (MIC) accelerated parallel method of moment (MoM) algorithm is proposed to solve electromagnetic problems in practical applications, where MIC means a kind of coprocessor or accelerator in computer systems which is used to accelerate the computation performed by Central Processing Unit (CPU). Three critical points are introduced in this paper in detail. The first one is the design of the parallel framework, which ensures that the algorithm can run on distributed memory platform with multiple nodes. The hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) programming model is designed to achieve the purposes. The second one is the out-of-core algorithm, which greatly breaks the restriction of MIC memory. The third one is the pipeline algorithm which overlaps the data movement with MIC computation. The pipeline algorithm successfully hides the communication and thus greatly enhances the performance of hybrid MIC/CPU MoM. Numerical result indicates that the proposed algorithm has good parallel efficiency and scalability, and twice faster performance when compared with the corresponding CPU algorithm.

Type-II Z-complementary pairs (ZCPs) play an important role in suppressing asynchronous interference in a wideband wireless communication system where the minimum interfering-signal delay is large. Based on binary Golay complementary pairs (BGCPs) and interleaving technique, new construction for producing Z-optimal Type-II even-length quadriphase ZCPs (EL-QZCPs) is presented, and the resultant pairs have new lengths in the form of 2 × 2α10β26γ (α, β, γ non-negative integers), which are not included in existing known Type-II EL-QZCPs.

Sequences that attain the smallest possible absolute sidelobes (SPASs) of periodic autocorrelation function (PACF) play fairly important roles in synchronization of communication systems, Large scale integrated circuit testing, and so on. This letter presents an approach to construct 16-QAM sequences of even periods, based on the known quaternary sequences. A relationship between the PACFs of 16-QAM and quaternary sequences is established, by which when quaternary sequences that attain the SPASs of PACF are employed, the proposed 16-QAM sequences have good PACF.

Based on the non-standard generalized Boolean functions (GBFs) over Z4, we propose a new method to convert those functions into the 16-QAM Golay complementary sequences (CSs). The resultant 16-QAM Golay CSs have the upper bound of peak-to-mean envelope power ratio (PMEPR) as low as 2. In addition, we obtain multiple 16-QAM Golay CSs for a given quadrature phase shift keying (QPSK) Golay CS.

Based on the known quadriphase zero correlation zone (ZCZ) sequences ZCZ4(N,M,T), four families of 16-QAM sequences with ZCZ are presented, where the term "QAM sequences" means the sequences over the quadrature amplitude modulation (QAM) constellation. When the quadriphase ZCZ sequences employed by this letter arrive at the theoretical bound on the ZCZ sequences, and are of the even family size M or the odd width T of ZCZ, two of the resulting four 16-QAM sequence sets satisfy the bound referred to above. The proposed sequences can be potentially applied to communication systems using 16-QAM constellation as spreading sequences so that the multiple access interference (MAI) and multi-path interference (MPI) are removed synchronously.

The shift-and-add property (SAP) of a p-ary m-sequence {ak} with period N=pn-1 means that this sequence satisfies the equation {ak+η}+{ak+τ}={ak+λ} for some integers η, τ and λ. For an arbitrarily-given p-ary m-sequence {ak}, we develop an algebraic approach to determine the integer λ for the arbitrarily-given integers η and τ. And all trinomials can be given. Our calculation only depends on the reciprocal polynomial of the primitive polynomial which produces the given m-sequence {ak}, and the cyclotomic cosets mod pn-1.

Software refactoring is an important process in software development. During software refactoring, code smell is a popular research topic that refers to design or implementation flaws in the software. Large class is one of the most concerning code smells in software refactoring. Detecting and refactoring such problem has a profound impact on software quality. In past years, software metrics and clustering techniques have commonly been used for the large class detection. However, deep-learning-based approaches have also received considerable attention in recent studies. In this study, we apply graph neural networks (GNNs), an important division of deep learning, to address the problem of large class detection. First, to support the extensive data requirements of the deep learning task, we apply a semiautomatic approach to generate a substantial number of data samples. Next, we design a new type of directed heterogeneous graph (DHG) as an input graph using the methods similarity matrix and software metrics. We construct an input graph for each class sample and make the graph classification with GNNs to identify the smelly classes. In our experiments, we apply three typical GNN model architectures for large class detection and compare the results with those of previous studies. The results show that the proposed approach can achieve more accurate and stable detection performance.