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Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$, BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at \url{https://github.com/THUYimingLi/DVBW}., Comment: This paper is accepted by IEEE TIFS. 15 pages. The preliminary short version of this paper was posted on arXiv (arXiv:2010.05821) and presented in a non-archival NeurIPS Workshop (2020)

Network traffic classification is important to intrusion detection and network management. Most of existing methods are based on machine learning techniques and rely on the features extracted manually from flows or packets. However, with the rapid growth of network applications, it is difficult for these approaches to handle new complex applications. In this paper, we design a novel neural network, the Extended Byte Segment Neural Network (EBSNN), to classify network traffic. EBSNN first divides a packet into header segments and payload segments, which are then fed into encoders composed of the recurrent neural networks with the attention mechanism. Based on the outputs, another encoder learns the high-level representation of the whole packet. In particular, side-channel features are learned from header segments to improve the performance. Finally, the label of the packet is obtained by the softmax function. Furthermore, EBSNN can classify network flows by examining the first few packets. Thorough experiments on the real-world datasets show that EBSNN achieves better performance than the state-of-the-art methods in both the application identification task and the website identification task.

Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be maliciously changed if the hidden backdoor is activated by attacker-specified triggers. This threat could happen when the training process is not fully controlled, such as training on third-party datasets or adopting third-party models, which poses a new and realistic threat. Although backdoor learning is an emerging and rapidly growing research area, its systematic review, however, remains blank. In this paper, we present the first comprehensive survey of this realm. We summarize and categorize existing backdoor attacks and defenses based on their characteristics, and provide a unified framework for analyzing poisoning-based backdoor attacks. Besides, we also analyze the relation between backdoor attacks and relevant fields ($i.e.,$ adversarial attacks and data poisoning), and summarize widely adopted benchmark datasets. Finally, we briefly outline certain future research directions relying upon reviewed works. A curated list of backdoor-related resources is also available at \url{https://github.com/THUYimingLi/backdoor-learning-resources}., 17 pages. A curated list of backdoor learning resources in this paper is presented in the Github Repo (https://github.com/THUYimingLi/backdoor-learning-resources). We will try our best to continuously maintain this Github Repo

This article proposes a fixed switching frequency predictive control method for neutral point clamped (NPC) single-phase PWM rectifier to achieve global optimization of the current during the entire switching cycle. Through the optimization of the switching sequence, a three-stage switching sequence that can control the midpoint potential balance is obtained. The extreme point, the initial point, and the end point of the current error within the switching period are employed to establish an error matrix, and the error matrix l 2-norm minimality is considered as the optimization objective. This optimization problem is a convex optimization problem, and the optimal duty cycle of the switching sequence can be obtained by solving the convex problem. The optimal duty cycle of each feasible switching sequence is obtained, and then the switching sequence that minimizes the error matrix l 2-norm is selected as the system output among all feasible switching sequences. Finally, the experimental results show that the proposed control scheme has good performance.

The parameters of a $q$ -ary MDS Euclidean self-dual codes are completely determined by its length and the construction of MDS Euclidean self-dual codes with new length has been widely investigated in recent years. In this paper, we give a further study on the construction of MDS Euclidean self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The main idea of our construction is to choose suitable evaluation points such that the corresponding (extended) GRS codes are Euclidean self-dual. Firstly, we consider the evaluation set consists of two disjoint subsets, one of which is based on the trace function, the other one is a union of a subspace and its cosets. Then four new families of MDS Euclidean self-dual codes are constructed. Secondly, we give a simple but useful lemma to ensure that the symmetric difference of two intersecting subsets of finite fields can be taken as the desired evaluation set. Based on this lemma, we generalize our first construction and provide two new families of MDS Euclidean self-dual codes. Finally, by using two multiplicative subgroups and their cosets which have nonempty intersection, we present three generic constructions of MDS Euclidean self-dual codes with flexible parameters. Several new families of MDS Euclidean self-dual codes are explicitly constructed.

Repair locality has been an important metric in a distributed storage system (DSS). Erasure codes with small locality are more popular in a DSS, which means fewer available nodes participating in the repair process of failed nodes. Locally repairable codes (LRCs) as a new coding scheme have given more rise to the system performance and attracted a lot of interest in the theoretical research in coding theory. The particular concern among the research problems is the bounds and optimal constructions of LRCs. The problem of optimal constructions of LRCs includes the most important case of Singleton-optimal LRCs whose minimum distance achieves the Singleton-like bound, which is the core consideration in this paper. In this work, we first of all derive an improved and general upper bound on the code length of Singleton-optimal LRCs with minimum distance $d=5, 6$ , some known constructions are shown to exactly achieve our new bound, which verifies its tightness. For locality $r=2$ and distance $d=6$ , we construct three new Singleton-optimal LRCs whose code length $n=3(q+1)$ , $n=3(q+\sqrt {q}+1)$ and $n=3(2q-4)$ , respectively. Moreover, we obtain a complete characterization for Singleton-optimal LRCs with $r=2$ and $d=6$ . Such characterization has established an important connection between the existence of Singleton-optimal LRCs and that of a special subset of lines of finite projective plane $PG(2, q)$ , thus provides a methodology for constructing LRCs with longer length based on any advance on finite projective plane $PG(2, q)$ . In the end, we employ the well-known line-point incidence matrix and Johnson bounds for constant weight codes to derive tighter upper bounds on the code length. These new bounds further help us to prove that some of the previous Singleton-optimal constructions or their extensions achieve the longest possible code length for $q=3, 4, 5, 7$ . It’s worth noting that all of our Singleton-optimal constructions possess small locality $r=2$ , which are attractive in a DSS.

A new model predictive control scheme, optimized switching finite control set model predictive control, is studied in this article for neutral-point-clamped (NPC) single-phase three-level pulsewidth modulation (PWM) rectifiers. To ensure the actual stability of the power converter, the ac-side current error of single-phase PWM rectifier and the neutral point voltage of the converter are both converged to the bounded invariant set, and the adjustment of system performance is thus realized by setting the bounded invariant set. Based on the NPC single-phase three-level PWM rectifier model, the ac-side current, the neutral point voltage, and the current error in the future time can be predicted in advance. The above objectives are achieved by optimizing the selection of switch combinations. The advantage of the proposed method is that it can reduce the average switching frequency of the converter when the ac-side current error and the neutral point voltage of the converter are controlled within a certain range, which makes it suitable for the application of high-power single-phase PWM rectifier.

In large-scale distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training. In this paper we propose BML , a scalable, high-performance and fault-tolerant DML network architecture on top of Ethernet and commodity devices. BML builds on BCube topology, and runs a fully-distributed gradient synchronization algorithm. Compared to a Fat-Tree network with the same size, a BML network is expected to take much less time for gradient synchronization, for both low theoretical synchronization time and its benefit to RDMA transport. With server/link failures, the performance of BML degrades in a graceful way. Experiments of MNIST and VGG-19 benchmarks on a testbed with 9 dual-GPU servers show that, BML reduces the job completion time of DML training by up to 56.4% compared with Fat-Tree running state-of-the-art gradient synchronization algorithm.

All-pass-filter-based single-phase phase-locked loop (APF-PLL) is a widely used grid synchronization method in grid-connected power converters. However, the performance of APF-PLL degrades under distorted grid conditions and the estimated phase and frequency will contain low-frequency oscillation terms, which is not conducive to practical application. To deal with this problem, this paper proposes two advanced APF-PLLs, which have good ability to suppress harmonics and dc offset interference without sacrificing the dynamic response of the system. In the first one, a band-pass filter (BPF) is used as the prefilter of APF-PLL to process the input voltage signal, and in the second one, the APF-PLL input signal is processed by using a harmonic decoupling network (HDN) and multiple prefilters. In addition, their parameter design procedure is also described in detail and is easy to follow. The experimental results presented in this paper finally verify the superiority and effectiveness of the two proposed methods.

Locally repairable codes (LRCs) have important applications in distributed storage systems. In this paper, we study q-ary [n, k, d] LRCs with (r, t, δ)-information-locality, where each of the i-th (1 ≤ i ≤ k) information symbol is contained in t punctured subcodes with length ≤ r + δ - 1, minimum distance δ, and the i-th information symbol is the unique common code symbol of these t subcodes, furthermore, each subcode contains exactly δ - 1 parity symbols. Firstly, an upper bound on the minimum distance of such q-ary LRCs with (r, t, δ)-information-locality is given. Then, we propose a general construction framework of q-ary optimal LRCs with (r, t, δ)-information-locality and minimum distanced = t(δ-1)+1, where the required field size is just q ≥ r+δ-2. The proposed optimal LRCs can always repair a failed information node locally in case of at most tδ - 1 node failures. Moreover, multiple repair subcodes can support parallel readings of data, thus make the proposed codes attractive for distributed storage systems with hot data.

A fully integrated 25 Gb/s low-noise optical receiver is presented which integrates transimpedance amplifier (TIA), continuous-time linear equalizer (CTLE), high-gain and high-bandwidth limiting amplifier (LA), and clock and data recovery (CDR) circuit into a single die. The TIA employs an inverter-based pseudo-differential TIA scheme with input series-inductor peaking, cross-coupled negative Gm pair and negative capacitance to improve the bandwidth, and noise performance, while a MOSFET corner compensation (MCC) circuit compensates for CMOS corner variations. A gain control (GC) scheme is proposed which solves the group delay issue caused by TIA input impedance variations from small input to overload current. Finally, a 2x-oversampling CDR using a bang-bang phase detector is included. The receiver is fabricated in 40-nm CMOS process, and the 850 nm VCSEL-based full-link measurement results show that the optical receiver achieves 44 $\mu \text{A}_{\mathrm {pp}}$ (RSSI Current = 43 $\mu \text{A}$ , ER = 4.94 dB) optical modulation amplitude (OMA) sensitivity (BER

Expander recovery is an iterative algorithm designed to recover sparse signals measured with binary matrices with linear complexity. In the paper, we study the expander recovery performance of the bipartite graph with girth greater than 4, which can be associated with a binary matrix with column correlations equal to either 0 or 1. For such a graph, expander recovery is proved to achieve the same performance as the traditional basis pursuit recovery, as the signal is dissociated. Compared to random graphs widely used for expander recovery, the graph we study tends to present better empirical performance. Furthermore, its special structure enables reducing the iteration number of expander recovery from $O(n\log k)$ times to exactly $k$ times in serial recovery, and from $\mathcal {O}(\log k)$ times to exactly one time in parallel recovery.

Locally repairable codes (LRCs) are introduced in distributed storage systems due to their low repair overhead. An LRC is called optimal if its minimum distance attains the Singleton-like upper bound. Chen et al. (2018) recently studied the constructions of optimal $(r, \delta)$ -LRCs with length $n \mid (q+1)$ and $(r+\delta -1) \mid n$ , where many classes of optimal cyclic constructions were obtained. In this paper, by employing constacyclic MDS codes, we construct seven classes of optimal $(r, \delta)$ -LRCs with new parameters. After adding these new optimal LRCs via constacyclic codes, we have completely obtained all optimal $(r, \delta)$ -LRCs with length $n \mid (q+1)$ and $(r+\delta -1) \mid n$ for all possible parameters for the completeness in the coding theory. It is worth noting that the optimal constacyclic LRCs with new parameters provide more alternatives to cyclic LRCs in the practical demands of distributed storage systems, where specific values of $n$ , $k$ , $r$ , and $\delta $ are required. Moreover, constacyclic LRCs also possess the encoding and decoding efficiency as cyclic LRCs.

This letter presents a $4\times 25$ Gb/s optical transmitter in 40-nm CMOS. Low-cost vertical cavity surface-emitting lasers (VCSELs) with 10-GHz bandwidth (BW) are employed, which are then overdriven to 25 Gb/s by the proposed pre-emphasized quad-channel driver. Digitally assisted asymmetric-pulsed equalization technique is proposed to overcome the VCSEL modulation non-linearity and maximize the eye-diagram margin against the 100-GbE standard mask. The measurement results show >30% eye-mask margin and error-free (BER $ ) optical transmission for each 25-Gb/s channel, resulting in 280-mW power dissipation per channel under 1.2–/3.3-V dual supply.

This paper presents a developed 2-D analytical subdomain model for design and analysis of the double-stator permanent magnet linear synchronous machine (DS-PMLSM) to account for both the primary and secondary end effects. First, considering the time constraint, polar transformed method is adopted to establish the 2-D subdomain analytical model, the DS-PMLSM is deformed into a ring-segment PMLSM, and the analytical model is calculated in polar coordinates instead of Cartesian coordinates. Then, the subdomain method is adopted to analyze the analytical model by solving Laplace’s equation and Poisson’s equation, and 2-D relative permeance function is used to consider the slot effect. The flux density and back electromotive force are calculated based on the developed analytical model. Finally, the analytical results are verified by the finite-element method and experiment. The results show that the proposed analytical model can accurately predict the electromagnetic performance, which is useful for the initial design of the DS-PMLSM.

© 2012 IEEE. Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been criticized. In this paper, a novel RF framework is proposed, named Bernoulli RFs (BRFs), with the aim of solving the RF dilemma between theoretical consistency and empirical performance. BRF uses two independent Bernoulli distributions to simplify the tree construction, in contrast to the RFs proposed by Breiman. The two Bernoulli distributions are separately used to control the splitting feature and splitting point selection processes of tree construction. Consequently, theoretical consistency is ensured in BRF, i.e., the convergence of learning performance to optimum will be guaranteed when infinite data are given. Importantly, our proposed BRF is consistent for both classification and regression. The best empirical performance is achieved by BRF when it is compared with state-of-the-art theoretical/consistent RFs. This advance in RF research toward closing the gap between theory and practice is verified by the theoretical and experimental studies in this paper.

A field-modulated tubular linear generator (FMTLG) with quasi-Halbach magnetization is proposed for ocean wave energy conversion. The FMTLG, which is equipped with field-modulated technology and quasi-Halbach arrays, can overcome the disadvantages of heavy weight and inefficiency caused by low-speed ocean waves to some extent. To design and optimize the FMTLG in a short time, the open-circuit magnetic field distribution is established with an analytical method. Results of a finite-element method show that the back EMF of the FMTLG is 43% higher than that of a tubular linear permanent magnet generator (TLPMG) with quasi-Halbach magnetization. A novel assistant tooth of the FMTLG makes the cogging force eight times smaller than that generated by the TLPMG. Finally, a prototype is manufactured to verify the simulation results by experiments.

We present a simplified 2-D analytical subdomain model to predict the open-circuit magnetic field of a tubular linear permanent magnet oscillation generator (T-LPMOG) to account for its primary and secondary end effects. At present, the magnetic field calculations of the T-LPMOG are typically analyzed in cylindrical coordinates, and the end effect is neglected accordingly to apply the periodic boundary. In this paper, a simplified 2-D analytical subdomain model in polar coordinates is proposed to consider the end effect. First, a coordinate transformation method is adopted to establish the 2-D subdomain analytical model, the cylindrical coordinates are converted into polar coordinates, and the T-LPMOG is analyzed under this new coordinate system. Next, the subdomain method is used to analyze the analytical model by solving Laplace's equation and the Poisson equation. Based on the simplified analytical model, the flux density and back-electromotive force are obtained. The analytical results are verified using the finite element analysis (FEA) method, and the computational times compared with the FEA method are provided. Finally, a T-LPMOG prototype is manufactured and tested, and the results show that the proposed analytical model can be useful in the initial design and optimization of the T-LPMOG.

For an $m\times n$ binary matrix with $d$ nonzero elements per column, it is interesting to identify the minimal column degree $d$ that corresponds to the best recovery performance. Consider this problem is hard to be addressed with currently known performance parameters, we propose a new performance parameter, the average of nonzero correlations between normalized columns. The parameter is proved to perform better than the known coherence parameter, namely the maximum correlation between normalized columns, when used to estimate the performance of binary matrices with high compression ratios $n/m$ and low column degrees $d$ . By optimizing the proposed parameter, we derive an ideal column degree $d=\lceil \sqrt{m}\rceil$ , around which the best recovery performance is expected to be obtained. This is verified by simulations. Given the ideal number $d$ of nonzero elements in each column, we further determine their specific distribution by minimizing the coherence with a greedy method. The resulting binary matrices achieve comparable or even better recovery performance than random binary matrices.

In this paper, a permanent magnet (PM) slotless tubular linear generator based on magnet field analysis model is presented for wave energy conversion. To improve air flux density and efficiency of the generator, two improved Halbach PM Arrays (T-PM structure and claw-PM structure) applied in the slotless linear generator is proposed and compared with Halbach PM Arrays. Combined with axisymmetric dimensional finite-element method (FEM), no-load electromagnetic performance is analyzed. Finally, due to test and analysis model results of prototype concordant with the FEM results, the generator model and the analysis method are correct.

A two-body buoys power generation system using the field-modulated tubular linear permanent magnet generator (FM-TLPMG) is proposed for direct-drive wave energy conversion in this paper. The traditional linear generator has disadvantages of large weight and low-power density caused by the slow speed of ocean wave. The FM-TLPMG can overcome these disadvantages in some extent through accelerating the speed of the traveling magnetic field. In order to design a suitable FM-TLPMG for the direct-drive wave energy conversion in a short time, ocean wave theory is applied to analyze hydrodynamic problems of the two-body buoys and calculate the operating velocity of FM-TLPMG in the sea. A 2-D analytical model for FM-TLPMG design is presented to obtain the electromagnetic field distribution by solving the Laplace and Poisson equations for each region. The analytical result has been compared with the finite-element analysis and shows its validation for the design.

In distributed storage systems, locally repairable codes (LRCs) have attracted lots of interest recently. If a code symbol can be repaired respectively by $t$ disjoint groups of other symbols, each of which has size at most $r$ , we say that the code symbol has $(r,t) $ -locality. LDPC codes are linear block codes defined by low-density parity-check matrices. A regular $(\tau,\rho)$ -LDPC code has the parity-check matrix with uniform column weight $\tau$ and uniform row weight $\rho$ . In this letter, we employ regular LDPC codes to construct optimal binary LRCs with $(r,t) $ -locality for information symbols. After proposing construction frameworks, three detailed constructions of binary LRCs with information locality are obtained, all of which have a single parity symbol in each repair group. All our codes attain the distance bounds of LRCs when each repair group contains a single parity symbol and thus are optimal. For storage systems with hot data, the proposed binary LRCs seem promising for system implementations since the encoding, repairing, parallel reading, and data reconstruction can be performed by simple XOR operations.

Although widely used, TCP has many limitations in meeting the throughput and latency requirements of applications in wireless networks, high-speed data center networks, and heterogeneous multi-path networks. Instead of relying purely on retransmission upon packet loss, coding has potential to improve the performance of TCP by ensuring better transmission reliability. Coding has been verified to work well at the link layer but has not been fully studied at the transport layer. There are many advantages but also challenges in exploiting coding at the transport layer. In this article, we focus on how to leverage end-to-end coding in TCP. We reveal the problems TCP faces and the opportunities coding can bring to improve TCP performance. We further analyze the challenges faced when applying the coding techniques to TCP and present the current applications of coding in TCP.

In this paper, an image dimming perceptual model is proposed as the foundation of image compensation and quality evaluation. Specifically, under this model an ideal compensation function is derived out and objective quality metrics SSIM and PSNR are adopted to evaluate the compensation performance. A practical compensation function is obtained by modifying the ideal compensation function under the consideration both of visual perception and objective quality. By using the SSIM and PSNR metrics, the relationship between image mean values, backlight levels, and image quality is further investigated. Base on this a novel scheme of backlight level adaptive adjustment is presented. Numerous experimental results verify the effectiveness of the model and demonstrate the advantages of the compensation and backlight level adaptive adjustment techniques.

In this paper, we address a super-resolution (SR) problem that constructs a high-resolution (HR) frame/image from a short sequence of low-resolution (LR) frames/images. It is well known that SR is a difficult problem, especially when the number of LR inputs is small. In particular, our previous work involving multisurface fitting-based SR exhibits relatively poor performance in the above case. To cope with this problem, we take advantage of nonlocal pixels to fit local surfaces. The pixels from nonlocal spatial–temporal positions are selected and weighted based on patch similarity and outlier removal. With this method, the fitted surfaces become more elaborate so that more details can be retrieved in SR results. Experiments demonstrate that the proposed method is very effective in producing HR frames through a small number of LR inputs when compared with some state-of-the-art methods.

In the context of compressed sensing (CS), subspace pursuit (SP) is an important iterative greedy recovery algorithm which could reduce the recovery complexity greatly comparing with l 1 -minimization. Restricted isometry property (RIP) and restricted isometry constant (RIC) of measurement matrices which ensure the convergence of iterative algorithms play key roles for the guarantee of successful reconstructions. In this letter, we show that for the s-sparse recovery, the RIC is enlarged to for SP, which improves the known results significantly. The proposed result also applies to almost sparse signals and corrupted measurements.

In the context of compressed sensing, the nonconvex ${\ell_q}$ minimization with $0 has been studied in recent years. In this letter, by generalizing the sharp bound for ${\ell_1}$ minimization of Cai and Zhang, we show that the condition ${\delta_{({s^q} + 1)k}} in terms of restricted isometry constant (RIC) can guarantee the exact recovery of $k$ -sparse signals in the noiseless case and the stable recovery of approximately $k$ -sparse signals in the noisy case by ${\ell_q}$ minimization. This result is more general than the sharp bound for ${\ell_1}$ minimization when the order of RIC is greater than $2k$ and illustrates the fact that a better approximation to ${\ell_0}$ minimization is provided by ${\ell_q}$ minimization than that provided by ${\ell_1}$ minimization.

A fast islanding detection tool can help power dispatchers monitor and control power system operations. Frequency monitoring network (FNET) is a low cost and quickly deployable wide-area phasor measurement system at the distribution system level. The frequency disturbance recorder (FDR) in FNET is actually a single-phase phasor measurement unit (PMU) installed at ordinary 120 V outlets in the sense that it measures the voltage phase angle, amplitude, and frequency from a single-phase voltage source. Based on the data collected by the FDRs deployed in the North American power grid, two islanding detection methods, the frequency difference method and the change of angle difference method, are proposed. The nine real cases recorded, including islanding cases, generation trip cases, load shedding cases and oscillation cases, are presented to verify the proposed methods of islanding detection. Sensitivity analysis on the thresholds of the frequency deviation and angle deviation is done based on the real measurement data for obtaining the insensitive interval of two thresholds. The results show that the proposed methods can correctly detect power system islanding, and will not be falsely triggered by generation trips, load shedding and system oscillations.

Stopping sets and stopping set distribution of a linear code are used to determine the performance of this code under iterative decoding over a binary erasure channel (BEC). Let C be a binary [n,k] linear code with parity-check matrix H, where the rows of H may be dependent. A stopping set S of C with parity-check matrix H is a subset of column indices of H such that the restriction of H to S does not contain a row of weight one. The stopping set distribution {Ti(H)}i=0n enumerates the number of stopping sets with size i of C with parity-check matrix H. Note that stopping sets and stopping set distribution are related to the parity-check matrix H of C. Let H* be the parity-check matrix of C which is formed by all the nonzero codewords of its dual code C⊥. A parity-check matrix H is called BEC-optimal if Ti(H)=Ti(H*), i=0,1,..., n and H has the smallest number of rows. In this paper, we study stopping sets, stopping set distributions and BEC-optimal parity-check matrices of binary linear codes. Using finite geometry in combinatorics, we obtain BEC-optimal parity-check matrices and then determine the stopping set distributions for the Simplex codes, the Hamming codes, the first order Reed-Muller codes, and the extended Hamming codes, which are some Reed-Muller codes or their shortening or puncturing versions.

Recent developments in smart grid technology have spawned interest in the use of phasor measurement units to help create a reliable power system transmission and distribution infrastructure. Wide-area monitoring systems (WAMSs) utilizing synchrophasor measurements can help with understanding, forecasting, or even controlling the status of power grid stability in real-time. A power system frequency monitoring network (FNET) was first proposed in 2001 and was established in 2004. As a pioneering WAMS, it serves the entire North American power grid through advanced situational awareness techniques, such as real-time event alerts, accurate event location estimation, animated event visualization, and post event analysis. Several papers published in the past several years discussed the FNET structure and its functionality. This paper presents some of the latest implementations of FNET's applications, which add significant capacities to this system for observing power system problems.

Computation of phase angles using the discrete Fourier transform (DFT) algorithm has been proven to be sufficiently accurate in a variety of power system applications when the input signal is three-phase voltage and the voltage frequency is near the nominal value. However, the performance of the DFT algorithm in time of the singe-phase voltage input with a large frequency excursion so far has not been well explored, much less documented. This paper briefly discusses the importance of the synchronized phase angle measurement in power systems, and brings up the necessity of research on single-phase measurement systems. The performance of the DFT algorithm in power system phase angle measurements is thoroughly analyzed. Corresponding rectifying methods, including quasi-positive-sequence DFT and offset compensation, are proposed and examined by relevant numerical simulations and necessary laboratory tests. The results of the simulations and tests show a perfect match between the derived equations and the phase angle data, and thus verify the effectiveness of the proposed algorithm, which is believed to be able to greatly improve the performance of DFT in the aspect of phase angle calculation when severe power system upsets with remarkable frequency excursions are present.

In this correspondence, we study the minimum pseudoweight and minimum pseudocodewords of low-density parity-check (LDPC) codes under linear programming (LP) decoding. First, we show that the lower bound of Kelley, Sridhara, Xu, and Rosenthal on the pseudoweight of a nonzero pseudocodeword of an LDPC code whose Tanner graph has girth greater than is tight if and only if this pseudocodeword is a real multiple of a codeword. Then, the lower bound of Kashyap and Vardy on the stopping distance of an LDPC code is proved to be also a lower bound on the pseudoweight of a nonzero pseudocodeword of an LDPC code whose Tanner graph has girth , and this lower bound is tight if and only if this pseudocodeword is a real multiple of a codeword. Using these results we further obtain that for some LDPC codes, there are no other minimum pseudocodewords except the real multiples of minimum weight codewords. This means that the LP decoding for these LDPC codes is asymptotically optimal in the sense that the ratio of the probabilities of decoding errors of LP decoding and maximum-likelihood decoding approaches as the signal-to-noise ratio (SNR) tends to infinity. Finally, some LDPC codes are listed to illustrate these results.

We compare, both objectively and subjectively, the performance of various advanced motion compensation methods, including overlapped block motion compensation (OBMC) and control grid interpolation (CGI), in a 3-D wavelet-based coding system. The motion vectors of the methods are obtained by using a sequence of 1-D dynamic programming algorithms that minimizes the cost function. Our experiment results indicate that an OBMC sequence usually has a higher peak signal-to-noise ratio (PSNR) than other methods, while a CGI sequence usually contains the fewest blocking artifacts. We provide a simple framework that combines the OBMC and CGI sequences. The proposed hybrid method removes more than 50% of the blocking artifacts of an OBMC sequence, while simultaneously maintaining a high PSNR performance

In this correspondence, we study the probability of undetected error for binary constant weight codes. First, we derive a new lower bound on the probability of undetected error for binary constant weight codes. Next, we show that this bound is tight if and only if the binary constant weight codes are generated from certain t-designs in combinatorial design theory. This means that these binary constant weight codes generated from certain t-designs are uniformly optimal for error detection. Along the way, we determine the distance distributions of such binary constant weight codes. In particular, it is shown that binary constant weight codes generated from Steiner systems are uniformly optimal for error detection. Thus, we prove a conjecture of Xia, Fu, Jiang, and Ling. Furthermore, the distance distribution of a binary constant weight code generated from a Steiner system is determined. Finally, we study the exponent of the probability of undetected error for binary constant weight codes. We derive some bounds on the exponent of the probability of undetected error for binary constant weight codes. These bounds enable us to extend the region in which the exponent of the probability of undetected error is exactly determined

Scanning a document page from a thick bound volume often results in two kinds of distortions in the scanned image, i.e., shade along the "spine" of the book and warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. From a technical point of view, this shape from shading (SFS) problem in real-world environments is characterized by 1) a proximal and moving light source, 2) Lambertian reflection, 3) nonuniform albedo distribution, and 4) document skew. Taking all these factors into account, we first build practical models (consisting of a 3D geometric model and a 3D optical model) for the practical scanning conditions to reconstruct the 3D shape of the book surface. We next restore the scanned document image using this shape based on deshading and dewarping models. Finally, we evaluate the restoration results by comparing our estimated surface shape with the real shape as well as the OCR performance on original and restored document images. The results show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.

In this correspondence, we study the probability of undetected error for binary constant-weight codes. First, we derive a new formula on the probability of undetected error for binary constant-weight codes. Second, using this new formula and linear programming, we give two new lower bounds on the probability of undetected error for binary constant-weight codes. These two new lower bounds improve on previously known lower bounds in certain cases. Furthermore, we show that these two lower bounds are tight if and only if the binary constant-weight codes are generated from certain t-designs in combinatorial design theory. This means that these binary constant-weight codes generated from certain t-designs are uniformly optimal for error detection. Along the way, we determine the distance distributions of such binary constant-weight codes. Finally, several examples are given to illustrate the results obtained in this correspondence.

Recently, binary 0-1 measurement matrices, especially those from coding theory, were introduced to compressed sensing. Dimakis et al. found that the linear programming (LP) decoding of LDPC codes is very similar to the LP reconstruction of compressed sensing, and they further showed that the sparse binary parity-check matrices of good LDPC codes can be used as provably good measurement matrices for compressed sensing under basis pursuit (BP). Moreover, Khajehnejad et al. made use of girth to certify the good performances of sparse binary measurement matrices. In this paper, we examine the performance of binary measurement matrices with uniform column weight and arbitrary girth under BP. For a fixed measurement matrix, we first introduce a performance indicator $w^{BP}_{min}$ called minimum BP weight , and show that any $k$ -sparse signals could be exactly recovered by BP if and only if $k\le (w^{BP}_{min}-1)/2$ . Then, lower bounds of $w^{BP}_{min}$ are studied. Borrowing ideas from the tree bound for the LDPC codes, we obtain several explicit lower bounds of $w_{BP}^{min}$ , which improve on the previous results in some cases. These lower bounds also imply explicit $\ell _{1}/\ell _{1}$ , $\ell _{2}/\ell _{1}$ and $\ell _\infty /\ell _{1}$ sparse approximation guarantees, and further confirm that large girth has positive impacts on the performance of binary measurement matrices under BP.

The design of optimal multifilter banks and optimum time-frequency resolution multiwavelets with different objective functions is discussed. The symmetric extension transform related to multifilter banks with symmetric properties is presented. It is shown that such a symmetric extension transform is nonexpensive. More optimal multifilter banks for image compression are constructed, and some of them are used in image compression. Experiments show that optimal multifilter banks have better performances in image compression than Daubechies' orthogonal wavelet filters and Daubechies' least asymmetric wavelet filters, and for some images, they even have better performances than the scalar (9,7)-tap biorthogonal wavelet filters. Experiments also show that the symmetric extension transform provided in this paper improves the rate-distortion performance compared with the periodic extension transform.

The undetected error probability of binary constant weight codes on a binary symmetric channel is studied in this correspondence. First, we present a necessary condition for the binary constant weight codes to be proper for error detection. Then, by using the necessary condition, we study the error detection capability of binary optimum constant weight codes.

In this letter, the stopping sets and stopping distance of finite geometry LDPC (FG-LDPC) codes are studied. It is known that FG-LDPC codes are majority-logic decodable and a lower bound on the minimum distance can be thus obtained. It is shown in this letter that this lower bound on the minimum distance of FG-LDPC codes is also a lower bound on the stopping distance of FG-LDPC codes, which implies that FG-LDPC codes have considerably large stopping distance. This may explain in one respect why some FG-LDPC codes perform well with iterative decoding in spite of having many cycles of length 4 in their Tanner graphs.

In this letter, we study the minimum pseudo-codewords of low-density parity-check (LDPC) codes under linear programming (LP) decoding. We show that a lower bound of Chaichanavong and Siegel on the pseudo-weight of a pseudo-codeword is tight if and only if this pseudo-codeword is a real multiple of a codeword. Using this result we further show that for some LDPC codes, e.g., Euclidean plane and projective plane LDPC codes, there are no other minimum pseudo-codewords except the real multiples of minimum codewords.

The well-known m-out-of-n code /spl Omega//sub n//sup m/ consists of all binary vectors of length n and weight m. It is known that it is good for error detection (in the technical sense, that is, the probability of undetected error P/sub ud/(/spl Omega//sub n//sup m/,p)/spl les/P/sub ud/(/spl Omega//sub n//sup m/,1/2) for all p, 0/spl les/p/spl les/1/2) only for a few small values of m and n. It is therefore of interest to determine (bounds for) the threshold in general, that is, find the range of bit-error probabilities p for which P/sub ud/ (/spl Omega//sub n//sup m/,p)/spl les/P/sub ud/ (/spl Omega//sub n//sup m/,1/2). In this article such bounds are given.