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APPLIED OPTICS 1997年第11期36卷 2401-2407页

作者： Fang, Q Zheng, SD GANSU IND UNIV DEPT MECH ENGNLANZHOU 730050GANZUPEOPLES R CHINA

A new optical profilometry-linearly coded profilometry (LCP)-is presented. It uses a sawtoothlike linear light structure to code the surface to be measured and a phase-shifting technique to decode the profile. Two kinds of coding-light structure, one with right-angle triangle teeth and the other with isosceles triangle teeth, are proposed. For coding with light with right-angle triangle teeth, a general decoding method is given. In addition, an optimum sampling manner and the measurement error are discussed with respect to a special case. For coding with light with isosceles triangle teeth, a decoding method with three samples is given. In our laboratory, an experimental system was established, and experiments that verified the reliability of the proposed methods were performed. Experimental results typical of those obtained are given. We find that LCP is similar to the widely used phase-measuring profilometry but has a faster measuring speed. (C) 1997 Optical Society of America.

关键词： optical profilometry linear coding

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A graph encoder-decoder network for unsupervised anomaly detection

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NEURAL COMPUTING & APPLICATIONS 2023年第32期35卷 23521-23535页

作者： Mesgaran, Mahsa Ben Hamza, A. Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada

A key component of many graph neural networks (GNNs) is the pooling operation, which seeks to reduce the size of a graph while preserving important structural information. However, most existing graph pooling strategies rely on an assignment matrix obtained by employing a GNN layer, which is characterized by trainable parameters, often leading to significant computational complexity and a lack of interpretability in the pooling process. In this paper, we propose an unsupervised graph encoder-decoder model to detect abnormal nodes from graphs by learning an anomaly scoring function to rank nodes based on their degree of abnormality. In the encoding stage, we design a novel pooling mechanism, named LCPool, which leverages locality-constrained linear coding for feature encoding to find a cluster assignment matrix by solving a least-square optimization problem with a locality regularization term. By enforcing locality constraints during the coding process, LCPool is designed to be free from learnable parameters, capable of efficiently handling large graphs, and can effectively generate a coarser graph representation while retaining the most significant structural characteristics of the graph. In the decoding stage, we propose an unpooling operation, called LCUnpool, to reconstruct both the structure and nodal features of the original graph. We conduct empirical evaluations of our method on six benchmark datasets using several evaluation metrics, and the results demonstrate its superiority over state-of-the-art anomaly detection approaches.

关键词： Anomaly detection Graph encoder-decoder Graph pooling linear coding

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Polynomial time algorithms for multicast network code construction

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IEEE TRANSACTIONS ON INFORMATION THEORY 2005年第6期51卷 1973-1982页

作者： Jaggi, S Sanders, P Chou, PA Effros, M Egner, S Jain, K Tolhuizen, LMGA CALTECH Dept Elect Engn Pasadena CA 91125 USA Univ Karlsruhe Fak Informat D-76128 Karlsruhe Germany Microsoft Res Redmond WA 98052 USA Philips Res Labs NL-5656 AA Eindhoven Netherlands

The famous max-flow min-cut theorem states that a source node s can send information through a network (V, E) to a sink node t at a rate determined by the min-cut separating s and t. Recently, it has been shown that this rate can also be achieved for multicasting to several sinks provided that the intermediate nodes are allowed to re-encode the information they receive. We demonstrate examples of networks where the achievable rates obtained by coding at intermediate nodes are arbitrarily larger than if coding is not allowed. We give deterministic polynomial time algorithms and even faster randomized algorithms for designing linear codes for directed acyclic graphs with edges of unit capacity. We extend these algorithms to integer capacities and to codes that are tolerant to edge failures.

关键词： communication networks efficient algorithms linear coding multicasting rate maximization

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Salient double reconstruction-based discriminative projective dictionary pair learning for crowd counting

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APPLIED INTELLIGENCE 2023年第2期53卷 1981-1996页

作者： Wang, Tao Luo, Hao Zhang, Kaibing Wang, Huake Li, Minqi Lu, Jian Chuzhou Univ Sch Comp & Informat Engn Chuzhou 239000 Peoples R China Xian Polytech Univ Sch Elect & Informat Xian 710048 Peoples R China

Crowd counting is one of the most fundamental tasks in the field of computer vision and dictionary learning has been successfully applied to the task. However, many traditional dictionary learning-based algorithms for crowd counting often show remarkably large prediction biases on real dynamic monitoring scenes where the feature distribution of the same count is of huge divergence. Meanwhile, these methods also clumsy at revealing salient feature changes between two video frames with the same crowd count. To overcome or alleviate these issues, in this paper we treat crowd counting as a particular classification problem and propose a novel dictionary learning algorithm called salient double-reconstruction based discriminative projective dictionary learning (SDR-DPL) for crowd counting. Specifically, the proposed SDR-DPL develops a novel reconstruction strategy which jointly considers reducing the feature distribution gap and incorporating salient feature mappings into the reconstruction term. This strategy benefits to make the learned dictionaries better adapt to large variations in monitoring crowd scenes and enables to achieve more accurate prediction on the number of pedestrians in both indoor and outdoor scenes. Moreover, we adopt an efficient linear coding technique to represent the crowd features regarding the learned synthesis dictionary, with which the optimization procedure breaks through the computational bottleneck that traditional sparse coding-based methods have to face with. Extensive evaluation experiments on five benchmark datasets validate the impressive performance of the proposed SDR-DPL compared with other state-of-the-art competitors. The source code has been available at https://***/Evelhz/SDR-DPL.

关键词： Crowd counting Dictionary learning Salient double reconstructions linear coding

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Feedback Capacity of Stationary Gaussian Channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第1期56卷 57-85页

作者： Kim, Young-Han Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

The feedback capacity of additive stationary Gaussian noise channels is characterized as the solution to a variational problem in the noise power spectral density. When specialized to the first-order autoregressive moving-average noise spectrum, this variational characterization yields a closed-form expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk-Kailath coding achieves the feedback capacity for the first-order autoregressive moving-average Gaussian channel, positively answering a long-standing open problem studied by Butman, Tiernan-Schalkwijk, Wolfowitz, Ozarow, Ordentlich, Yang-Kavcic-Tatikonda, and others. More generally, it is shown that a k-dimensional generalization of the Schalkwijk-Kailath coding achieves the feedback capacity for any autoregressive moving-average noise spectrum of order. Simply put, the optimal transmitter iteratively refines the receiver's knowledge of the intended message. This development reveals intriguing connections between estimation, control, and feedback communication.

关键词： Additive Gaussian noise channel autoregressive moving-average spectrum channel capacity feedback iterative refinement linear coding stationary Gaussian process

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Encoded dynamics for fault tolerance in linear finite-state machines

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2002年第1期47卷 189-192页

作者： Hadjicostis, CN Verghese, GC Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA MIT Electromagnet & Elect Syst Lab Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

This note discusses fault tolerance in linear finite-state machines (LFSMs). By replacing a given LFSM with a larger, redundant LFSM that preserves the state of the original one in some linearly encoded form, various alternatives to modular redundancy are considered. We characterize the class of appropriate redundant machines in terms of added dynamics and their coupling with the dynamics of the original machine, and illustrate how these can be chosen to minimize redundant hardware.

关键词： fault tolerance linear coding linear finite-state machines system embeddings

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MINIMAL DISTANCE LEXICOGRAPHIC CODES OVER AN INFINITE ALPHABET

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IEEE TRANSACTIONS ON INFORMATION THEORY 1991年第5期37卷 1366-1368页

作者： HERSCOVICI, DS Massachusetts Inst. of Technology Cambridge MA

It is shown that any lexicographic code S with minimal distance d has a unique basis where each basis vector is a one followed by a string of zeros, followed by d - 1 nonzero digits a(ij). Furthermore, the matrix A = ... 详细信息

关键词： COMBINATORIAL MATHEMATICS GAME THEORY HAMMING coding linear coding MATRICES

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Capacity Results for Multicasting Nested Message Sets Over Combination Networks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2016年第9期62卷 4968-4992页

作者： Bidokhti, Shirin Saeedi Prabhakaran, Vinod M. Diggavi, Suhas N. Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany Stanford Univ Dept Elect Engn Stanford CA 94305 USA Tata Inst Fundamental Res Sch Technol & Comp Sci Homi Bhabha Rd Bombay 400005 Maharashtra India Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

The problem of multicasting two nested messages is studied over a class of networks known as combination networks. A source multicasts two messages, a common and a private message, to several receivers. A subset of the receivers (called the public receivers) only demand the common message, and the rest of the receivers (called the private receivers) demand both the common and the private message. Three encoding schemes are discussed that employ linear superposition coding, and their optimality is proved in special cases. The standard linear superposition scheme is shown to be optimal for networks with two public receivers and any number of private receivers. When the number of public receivers increases, this scheme stops being optimal. Two improvements are discussed: one using pre-encoding at the source, and one using a block Markov encoding scheme. The rate-regions that are achieved by the two schemes are characterized in terms of feasibility problems. Both inner bounds are shown to be the capacity region for networks with three (or fewer) public and any number of private receivers. Although the inner bounds are not comparable in general, it is shown through an example that the region achieved by the block Markov encoding scheme may strictly include the region achieved by the pre-encoding/linear superposition scheme. Optimality results are founded on the general framework of Balister and Bollobas (2007) for sub-modularity of the entropy function. An equivalent graphical representation is introduced and a lemma is proved that might be of independent interest. Motivated by the connections between combination networks and broadcast channels, a new block Markov encoding scheme is proposed for broadcast channels with two nested messages. The rate-region that is obtained includes the previously known rate-regions. It remains open whether this inclusion is strict.

关键词： Network coding combination networks broadcast channels superposition coding linear coding block Markov encoding submodularity of entropy

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A Class of Non-linearly Solvable Networks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2017年第1期63卷 201-229页

作者： Connelly, Joseph Zeger, Kenneth Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

For each positive composite integer m, a network is constructed, which is solvable over an alphabet of size m but is not solvable over any smaller alphabet. These networks have no linear solutions over any module alphabets and are not asymptotically linearly solvable over any finite-field alphabets. The networks' capacities are all shown to equal one, and their linear capacities are all shown to be bounded away from one for all finite-field alphabets. In addition, if m is a non-power-of-prime composite number, then such a network is not solvable over any prime-power-size alphabet.

关键词： Capacity linear coding network solvability network coding

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Alternative target functions for protein structure prediction with neural networks

Alternative target functions for protein structure predictio...

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6th Conference on Data Mining and Knowledge Discovery

作者： Deng, H Harrison, R Pan, Y Tai, PC Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)0819453560

The prediction and modeling of protein structure is a central problem in bioinformatics. Neural networks have been used extensively to predict the secondary structure of proteins. While significant progress has been made by using multiple sequence data, the ability to predict secondary structure from a single sequence and a single prediction network has stagnated with an accuracy of about 75%. This implies that there is some limit to the accuracy of the prediction. In order to understand this behavior we asked the question of what happens as we change the target function for the prediction. Instead of predicting a derived quantity, such as whether a given chain is a helix, sheet or turn, we tested whether a more directly observed quantity such as the distance between a pair of alpha-carbon atoms could be predicted with reasonable accuracy. The alpha-carbon atom position is central to each residue in the protein and the distances between them in sequence define the backbone of protein. Knowledge of the distances between the alpha-carbon atoms is sufficient to determine the three dimensional structure of the protein. We have trained on distance data derived from the complete protein structure database (pdb) using a multi-layered perceptron (MLP) feedforward neural network with back propagation. It shows that the root of mean square error is 4.4 Angstrom while the mean of actual output is 11.5 Angstrom with orthogonal coding of protein primary sequence. Other coding schemes including BLOSUM62 coding and linear coding were tested with another two target functions of cutoff accuracy and correlation coefficient. The best correlation coefficient was achieved with BLOSUM62 coding scheme and the cutoff accuracy reached about 60%.

关键词： MLP alpha-carbon atoms RMSE orthogonal coding BLOSUM coding linear coding correlation coefficient cutoff accuracy

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