Efficient Binary Codes Research Articles

Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

AbstractSimilarity search, that is, finding similar items in massive data, is a fundamental computing problem in many fields such as data mining and information retrieval. However, for large‐scale and high‐dimension data, it suffers from high computational complexity, requiring tremendous computation resources. Here, based on the low‐power self‐selective memristors, for the first time, we propose an in‐memory search (IMS) system with two innovative designs. First, by exploiting the natural distribution law of the devices resistance, a hardware locality sensitive hashing encoder has been designed to transform the real‐valued vectors into more efficient binary codes. Second, a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel. Our IMS system demonstrated a 168× energy efficiency improvement over all‐transistors counterparts in clustering and classification tasks, while achieving a software‐comparable accuracy, thus providing a low‐complexity and low‐power solution for in‐memory data mining applications.image

Optimized feature space learning for generating efficient binary codes for image retrieval

In this paper, a novel approach for learning a low-dimensional optimized feature space for image retrieval with minimum intra-class variance and maximum inter-class variance is proposed. The classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low-dimensional feature space for single-labeled images. Since image retrieval involves images with multiple objects, LDA cannot be directly used for dimensionality reduction and feature space optimization. This problem is addressed by utilizing the relationship between LDA and Canonical Correlation Analysis (CCA) eigenvalues to generate an optimized feature space for both single-labeled and multi-labeled images. A CCA-based network architecture which correlates the low-dimensional feature vectors with the image label vectors is proposed. We design a novel loss function such that the correlation coefficients of CCA are maximized. Our experiments prove that we could train the neural network to reach the theoretical lower bound of loss corresponding to the negative sum of the correlation coefficients. Once the optimized feature space is generated, feature vectors are binarized with the Iterative Quantization (ITQ) approach. Finally, we propose an ensemble network to generate binary codes of desired bit length for retrieval. The measurement of mean average precision shows that the proposed approach outperforms the retrieval results of other single-labeled and multi-labeled image retrieval benchmarks at same bit numbers in a considerable number of cases.

Robust multimodal discrete hashing for cross-modal similarity search

Hashing technology improves the search efficiency and reduces the storage space of data. However, building an effective modal with unsupervised cross modal retrieval and generating efficient binary code is still a challenging task, considering of some issues needed to be further discussed and researched for unsupervised multimodal hashing. Most of the existing methods ignore the discrete restriction, and manually or experientially determine the weights of each modality. These limitations may significantly reduce the retrieval accuracy of unsupervised cross-modal hashing methods. To solve these problems, we propose a robust hash modal that can efficiently learn binary code by employing a flexible and noise-resistant ℓ2,1-loss with nonlinear kernel embedding. In addition, we introduce an intermediate state mapping that facilitate later modal optimization to measure the loss between the hash codes and the intermediate states. Experiments on several public multimedia retrieval datasets validate the superiority of the proposed method from various aspects.

Binary Joint Network-Channel Coding for Reliable Multi-Hop Wireless Networks

The high and variable packet loss in wireless networks due to the unreliable nature of wireless channels, interference, link or router failure, fading, and shadowing wireless link are the main problems and challenges in implementing a reliable wireless network. Packet loss probability is the most important performance measure for real-time applications. Packet loss is due to errors, congestion in the queue because of high traffic, collision, hidden nodes, and link failures. In this paper, we focus on the links failure problem. To address this problem, improve the performance of wireless networks, and enhance and make the network survivability and availability more reliable, a new efficient binary joint network and channel coding (B-JNCC) technique is proposed. B-JNCC combines low-complexity parity-check (LCPC) channel coding and the wireless network erasure correcting with router code to detect lost packets to reduce the bit error rate and increase the packet delivery ratio over a wireless multi-access channel. B-JNCC is applied at the receiver node. Simulation results show that the proposed B-JNCC outperforms the binary symbol-wise JNCC, non-binary JNCC (NB-JNCC), and NB separate network-channel coding.

Supervised discrete cross-modal hashing based on kernel discriminant analysis

Cross-modal hashing methods have drawn considerable attention due to the rapid growth of multi-modal data. To obtain efficient binary codes in a low-dimensional Hamming space, most existing approaches relaxed the discrete constraint, which could cause quantization loss and even result in performance degradation. In order to avoid this bottleneck, some scholars employed iterative discrete cyclic coordinate descent (DCC) to learn hash codes bit by bit, but this was very time-consuming. To counter this problem, a simple yet novel supervised discrete cross-modal hashing framework is represented to directly learn the unified discrete binary codes with a close-form, rather than bit by bit. Furthermore, to preserve label separability, the kernel discriminant analysis is fused into the proposed framework to enrich the discrimination ability of the learned binary codes. The goal of the proposed method is to obtain the common discrete binary codes of different modalities in a shared latent Hamming space so that the different modalities of a sample can be effectively connected. Experimental study shows the encouraging results of the proposed algorithm in comparisons to the state-of-the-art baseline approaches on four real-world datasets. Especially on the LabelMe dataset, the superiority of the proposed method is obvious, with an average improvement of 9% over the best available results.

Learning Efficient Binary Codes From High-Level Feature Representations for Multilabel Image Retrieval

Due to the efficiency and effectiveness of hashing technologies, they have become increasingly popular in large-scale image semantic retrieval. However, existing hash methods suppose that the data distributions satisfy the manifold assumption that semantic similar samples tend to lie on a low-dimensional manifold, which will be weakened due to the large intraclass variation. Moreover, these methods learn hash functions by relaxing the discrete constraints on binary codes to real value, which will introduce large quantization loss. To tackle the above problems, this paper proposes a novel unsupervised hashing algorithm to learn efficient binary codes from high-level feature representations. More specifically, we explore nonnegative matrix factorization for learning high-level visual features. Ultimately, binary codes are generated by performing binary quantization in the high-level feature representations space, which will map images with similar (visually or semantically) high-level feature representations to similar binary codes. To solve the corresponding optimization problem involving nonnegative and discrete variables, we develop an efficient optimization algorithm to reduce quantization loss with guaranteed convergence in theory. Extensive experiments show that our proposed method outperforms the state-of-the-art hashing methods on several multilabel real-world image datasets.

Software for Brain Network Simulations: A Comparative Study

Numerical simulations of brain networks are a critical part of our efforts in understanding brain functions under pathological and normal conditions. For several decades, the community has developed many software packages and simulators to accelerate research in computational neuroscience. In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks. Next, our investigations on the characteristics of computational architecture and efficiency indicate that all simulators compile the most computationally intensive procedures into binary code, with the aim of maximizing their computational performance. However, not all simulators provide the simplest method for module development and/or guarantee efficient binary code. Third, a study of their amenability for high-performance computing reveals that NEST can almost transparently map an existing model on a cluster or multicore computer, while NEURON requires code modification if the model developed for a single computer has to be mapped on a computational cluster. Interestingly, parallelization is the weakest characteristic of BRIAN, which provides no support for cluster computations and limited support for multicore computers. Fourth, we identify the level of user support and frequency of usage for all simulators. Finally, we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package. The results indicate that BRIAN provides the most concise language for both cases considered. Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models. Overall, the case studies reinforce our general observation that simulators have a bias in the computational performance toward specific types of the brain network models.

Learning unified binary codes for cross-modal retrieval via latent semantic hashing

Nowadays the amount of multimedia data such as images and text is growing exponentially on social websites, arousing the demand of effective and efficient cross-modal retrieval. The cross-modal hashing based methods have attracted considerable attention recently as they can learn efficient binary codes for heterogeneous data, which enables large-scale similarity search. Generally, to effectively construct the cross-correlation between different modalities, these methods try to find a joint abstraction space where the heterogeneous data can be projected. Then a quantization rule is applied to convert the abstraction representation to binary codes. However, these methods may not effectively bridge the semantic gap through the latent abstraction space because they fail to capture latent information between heterogeneous data. In addition, most of these methods apply the simplest quantization scheme (i.e. sign function) which may cause information loss of the abstraction representation and result in inferior binary codes. To address these challenges, in this paper, we present a novel cross-modal hashing based method that generates unified binary codes combining different modalities. Specifically, we first extract semantic features from the modalities of images and text to capture latent information. Then these semantic features are projected to a joint abstraction space. Finally, the abstraction space is rotated to produce better unified binary codes with much less quantization loss, while preserving the locality structure of projected data. We integrate the binary code learning procedures above to develop an iterative algorithm for optimal solutions. Moreover, we further exploit the useful class label information to reduce the semantic gap between different modalities to benefit the binary code learning. Extensive experiments on four multimedia datasets show that the proposed binary coding schemes outperform several other state-of-the-art methods under cross-modal scenarios.

Transmission parameters optimization and receiver architectures for DVB‐S2X systems

SummaryThe second‐generation specification of the digital video broadcasting for satellite (DVB‐S2) was developed in 2003 with the aim of improving the existent broadcasting standard DVB‐S. The main new features introduced by DVB‐S2 included increased baud rates, higher cardinality constellations (up to 32 points), and more efficient binary codes. The extension to DVB‐S2, approved in 2014 with the name DVB‐S2X, together with continuous technological evolution, moves further steps in this direction, with the use of constellations with cardinality up to 256 points, improved granularity of modulation and coding schemes, and the possibility to increase the baud rate. In this scenario, it is important to be able to ascertain what is the best transceiver structure, starting from the choice of the shaping pulse and the baud rate of the transmitted signals and ending with the most promising receiver architectures, with the aim of maximizing the spectral efficiency. In this paper, we will discuss some of the aspects of this investigation, namely, the optimization of transmission parameters and the description of an efficient receiver. We will then assess the performance of the proposed scheme in comparison with a classical DVB‐S2 architecture. Some synchronization aspects will also be discussed, to account for the impairments introduced by the channel. Copyright © 2015 John Wiley & Sons, Ltd.

Efficient binary code indexing with pivot based locality sensitive clustering

High-dimensional indexing is fundamental in multimedia research field. Compact binary code indexing has achieved significant success in recent years for its effective approximation of high-dimensio...

Re-examining the performance bottleneck in a NIDS with detailed profiling

Designing a high-speed network intrusion detection system (NIDS) has attracted much attention in recent years due to ever-increasing amount of network traffic and ever-complicated attacks. Numerous studies have been focusing on accelerating pattern matching for a high-speed design because some early studies observed that pattern matching is a performance bottleneck. However, the effectiveness of such acceleration has been challenged recently. This work therefore re-examines the performance bottleneck by profiling two popular NIDSs, Snort and Bro, with various types of network traffic in detail. In the profiling, we find pattern matching can be dominant in the Snort execution if the entire packet payloads in the connections are scanned, while executing the policy scripts is an obvious bottleneck in the Bro execution. This work suggests three promising directions towards a high-speed NIDS design for future research: a method to precisely specify the possible locations of the signatures in long connections, a compiler to transform the policy scripts to efficient binary codes for execution, and an efficient design of connection tracking and packet reassembly.

Transmission scheme for a K-way relay multiple-input multiple-output channel

In the multiple-input multiple-output Gaussian K-users relay interference channel, each user intends to convey a multicast message to all the other users while receiving K−1 independent messages from the other users via an intermediate relay. An encoding and decoding strategy is proposed which is inspired from cryptography theory. The proposed encoding and decoding strategy involves a signal space alignment for an encryption message for multiple access phases and zero forcing with successive network code decoding for the broadcast phase. Then, the investigation of the antenna configuration conditions is an initial step for addressing the degrees of freedom of the above channel. It presents a new modified high-order pulse amplitude modulation that enables us to use the computationally efficient binary channel coding under a physical-layer network coding. It is shown that the proposed signalling scheme significantly outperforms the conventional non-binary code and is slightly worse than the lattice code.

On the linear description of the Huffman trees polytope

The Huffman tree is a well known concept in data compression discovered by David Huffman in 1952 [7]. A Huffman tree is a binary tree and represents the most efficient binary code for a given alphabet with respect to a distribution of frequency of its characters. In this paper, we associate any Huffman tree of n leaves with the point in Qn having as coordinates the length of the paths from the root to every leaf from the left to right. We then study the convex hull, that we call Huffmanhedron, of those points associated with all the possible Huffman trees of n leaves. First, we present some basic properties of Huffmanhedron, especially, about its dimensions and its extreme vertices. Next we give a partial linear description of Huffmanhedron which includes in particular a complete characterization of the facet defining inequalities with nonnegative coefficients that are tight at a vertex corresponding to some maximum height Huffman tree (i.e. a Huffman tree of depth n−1). The latter contains a family of facet defining inequalities in which coefficients follow in some way the law of the Fibonacci sequence. This result shows that the number of facets of Huffmanhedron is at least a factorial of n and consequently shows that the facial structure of Huffmanhedron is rather complex. This is quite in contrast with the fact that using the algorithm of Huffman described in [7], one can minimize any linear objective function over the Huffmanhedron in O(nlogn) time. We also give two procedures for lifting and facet composition allowing us to derive facet-defining inequalities from the ones in lower dimensions.

A Nonconventional Approach to Generating Efficient Binary Gray Code Sequences

The gray codes are named after Frank Gray, who patented (U.S. Patent 2 632 058) the use of them in shaft encoders in 1953. Because of the reflection property of the code, sometimes the term Gray code is used to refer to reflected binary codes. The Gray codes are cycling in nature and maintain the unit hamming distance between one code word to the next code word. To visualize the attributes of reflection property, cyclic nature and unit hamming distance of Gray codes, Fig. 1 can be referred to where the binary Gray code patterns G(n) for size n51 to 4 b are depicted.

Modified High-Order PAMs for Binary Coded Physical-Layer Network Coding

The non-binary code (NBC) or the lattice code (LC) has been the only possible channel code with the pulse amplitude modulation (PAM) signaling of modulation size greater than two for the physical layer network coding (PNC) over a decode-and-forward (DF) relaying channel. A major drawback with the NBC or LC however is its high computational complexity. In this letter, we present new modified high-order PAMs that enable us to use the computationally efficient binary channel coding under the PNC over a DF relaying channel.

The effects of compiler optimisations on embedded system power consumption

Power consumption is a subject of serious consideration in embedded systems design because embedded systems are constrained to stringent power and energy requirements. The mission of lowering power consumption and energy usage of such systems is an important task to prolong their usage in real time situations. In this paper, we study the effects of compiler optimisations on embedded systems energy usage and power consumption in real time situations and the importance of running efficient binary codes in realising a more power efficient and better performing embedded system. Compiler optimisations at various levels involving different architectural features have been experimented and it is shown that architecture driven compiler optimisations have a better impact on reducing power consumption and energy usage in embedded system than blind code optimisations.

Period distribution of generalized B-J codes and constructions of cyclically permutable (CP) codes

NCUYEN et a1. ['I give a method for constructing binary CP constant-weight (CPC) codes, then they construct protocol sequences with good performance by using the codes. To make the minimum distance of the CP codes large enough, Nguyen et a1 . select two classes of maximum distance separable ( MDS) codes which are q-ary Reed-Solomon ( R-S) codes and qary generalized BerlekampJustesen (BJ ) codes. The generalized B-J codes are more interesting because of their larger code length and minimum distance. It can be seen from ref. [ 11 that the construction from R-S codes is asymptotically optimum, but the construction from generalized B-J codes is asymptotically bad since its efficiency is only about l /q which turns out to be 0 when q turns out to be infinity. Hence, how to improve the efficiency of the construction for generalized B-J codes is a key problem. In this note the period distribution of generalized BJ codes is proposed. Then, we give a method of constructing asymptotically optimum CP codes and generalized CP codes. Thus, a class of more efficient binary CPC codes and protocol sequences for collision channel without feedback can be obtained. Let C be a [ n , k ] cyclic code over F,, where ( n , q ) = l . Definition lL4? 'I . The cyclic shifting of codeword c in C is S ( c ) = ( cl , c2, "-, C, 1, co) . Let 1 = ( I , 1, . . a , 1 ) . If there exist positive integer r and u which is an element in F, such that Sr ( c ) = c + u -1, we call r a generalized period of c . Let t ( c ) denote the minimum generalized period of c . Specially, if u = 0, r is defined as a period of c . The minimum period of c is denoted by t ( c ) . We call a codeword c full order (or generalized full order) if the minimum period (or generalized period) is n . Let A, (or x) be the number of codewords in C

Binary multilevel coset codes based on Reed-Muller codes

We study the construction and decoding of binary multilevel coset codes. This construction, originally introduced by Blokh and Zyablov in 1974 and by Zinov'ev in 1976, shows remarkable analogies with most recent schemes of coded modulations. Basic elements of the construction are an inner code, head of a partition chain having suitable distance properties, and a set of outer codes, generally nonbinary. For each partition level there is an outer code whose alphabet has the same order of the partition: in this way it is possible to associate every partition subset to a code symbol. It is well known that these codes can be efficiently decoded by the so called multistage decoding. We show that good codes (in terms of performance/complexity) can be constructed using Reed-Muller (RM) codes as inner codes. To this aim RM codes are revisited in the framework of the above construction and decoding techniques. In particular we describe a family of decoders for RM codes which include Forney's (1988) and Hemmati's (1989) decoders as special cases. Finally, we present some examples of efficient binary codes based on RM codes, and assess their performance via computer simulation. >

A Simple and Efficient Binary Coding Algorithm and "C" Computer Program that Facilitate Analysis of Categorical Data: Part II

An efficient method for coding, decoding, and analyzing categorical data is presented. The binary coding algorithm and computer program allow the researcher to simultaneously code category frequencies and frequencies of responses within categories. The computer program reads the binary code and produces: (a) a printed output consisting of a complete written record of all of the subjects' responses; (b) a data file consisting of category frequency counts; and (c) a second data file consisting of frequency counts of the different responses within each category. Both data files can be directly analyzed without modification by most major statistical software packages such as SPSSX and SAS. The coding algorithm and computer program significantly reduce the time and errors associated with data coding and entry, and facilitate statistical analyses of categorical data.

A Simple and Efficient Binary Coding Algorithm and "C" Computer Program that Facilitate Analysis of Categorical Data

An efficient method for coding and decoding categorical data is presented. The coding algorithm and computer program allows the researcher to simultaneously code category frequencies and specific category responses. The computer program reads the binary code and produces a data file consisting of category frequency counts and a printed output file consisting of a complete written record of all of the subjects' responses. The coding algorithm and computer program can significantly reduce the time, expense and errors associated with categorical data coding, retrieval, and analysis.