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Fast computation of residual complexity image similarity metric using low-complexity transforms

IET IMAGE PROCESSING 2015年第8期9卷 699-708页

作者： Pauchard, Yves Cintra, Renato J. Madanayake, Arjuna Bayer, Fabio M. Univ Western Ontario Robarts Res Inst Imaging Res Labs London ON Canada Univ Fed Pernambuco Signal Proc Grp Dept Estatst Recife PE Brazil Inst Natl Sci Appl LIRIS Lyon France Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA Univ Fed Santa Maria Dept Estatst BR-97119900 Santa Maria RS Brazil Univ Fed Santa Maria LACESM BR-97119900 Santa Maria RS Brazil

The authors apply two approaches to reduce the computation time of the residual complexity similarity metric employed in image registration applications aimed at hardware-based implementations with low-complexity transforms. First, the similarity metric is computed in image sub-blocks, which are subsequently combined into a global metric value. Second, the discrete cosine transform (DCT) needed in the computation of the similarity measure is replaced with multiplier-free low-complexity approximate transforms. The authors propose a new low-complexity transform requiring only 18 additions in an 8 x 8 block and compare it to: the round DCT, the signed DCT, the Hadamard transform and the Walsh-Hadamard transform. Detailed computational complexity analysis reveals that block-wise processing alone reduces computational cost by a factor of 8-9 for original DCT composed of multiplications and additions, and up to similar or equal to 4.90 when the proposed DCT is utilised;being the computation performed with additions only. Results obtained from computer simulated and realistic X-ray images demonstrate block-wise processing and approximate transforms result in successful image registration, making residual complexity similarity measure available to hardware-accelerated fast image registration applications.

关键词： image registration discrete cosine transforms Hadamard transforms computational complexity residual complexity image similarity metric computation time reduction global metric value hardware-based implementations image subblocks discrete cosine transform round DCT signed DCT multiplier-free low-complexity approximate transforms computational complexity analysis blockwise processing Walsh-Hadamard transform X-ray images image processing tasks image compression hardware-accelerated fast image registration

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A novel forward gene selection algorithm for microarray data

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NEUROCOMPUTING 2014年 133卷 446-458页

作者： Du, Dajun Li, Kang Li, Xue Fei, Minrui Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L-2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches. (C) 2014 Elsevier B.V. All rights reserved.

关键词： computational complexity analysis Data augmentation Fast regression algorithm Gene selection Small samples Variant correlation

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A novel division algorithm and architectures for parallel and sequential processing

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2005年第2期14卷 281-295页

作者： Tatas, K Soudris, DJ Siomos, D Thanailakis, A Democritus Univ Thrace Dept Elect & Comp Engn VLSI Design & Testing Ctr Xanthi 67100 Greece

A new algorithm for reducing the division operation to a series of smaller divisions is introduced. Partitioning the dividend into segments, we perform divisions, shifts, and accumulations taking into account the weight of dividend bits. Each partial division can be performed by any existing division algorithm. From an algorithmic point of view, computation analysis is performed in comparison with the existing algorithms. From an implementation point of view, since the division can be performed by any existing divider, the designer can choose the divider which best meets his specifications. Although the algorithm is presented for integer numbers, it can be easily generalized for fractions, since it is only a matter of representation. Two possible implementations of the algorithm, namely the sequential and parallel are derived, with several variations, allowing performance, cost, and cost/performance trade-offs. Exhaustive comparisons of the derived implementations with many existing implementations in terms of area cost, performance, and cost/performance are done. A plethora of alternative implementations can be derived due to a variable number of partitions.

关键词： computer arithmetic division computational complexity analysis algorithms parallel processing

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Hierarchical planning and reasoning about partially ordered plans-From theory to practice

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AI MAGAZINE 2022年第4期43卷 353-364页

作者： Bercher, Pascal Australian Natl Univ Coll Engn Comp & Cybernet Sch Comp Canberra Australia

This invited paper (part of the New Faculty Highlights Invited Speaker Program of AAAI'21*) surveys my work done until today. The reviewed work focuses on hierarchical task network (HTN) planning as well as on partial order causal link (POCL) planning. Lines of research include theoretical investigations (mostly computational complexity analyses), heuristic search, as well as the practical application of the technology for planning-based assistants, which support a human user in carrying out various tasks.

关键词： Planning Human-computer interaction computational complexity analysis

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Hybrid Hughes-Hartogs power allocation algorithms for OFDMA systems

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IET SIGNAL PROCESSING 2018年第9期12卷 1185-1192页

作者： Inacio de Souza, Joao Henrique Abrao, Taufik Univ Estadual Londrina Dept Elect Engn Rod Celso Garcia Cid PR445POB 10-011 BR-86057970 Londrina PR Brazil

This work analyses the discrete solution of Hughes-Hartogs (HH) for the transmission rate maximisation problem with power constraint in the orthogonal frequency division multiplexing access (OFDMA) systems and explores mechanisms to reduce the computational complexity of greedy algorithms. In addition to the solution characterisation, a computational complexity analysis is developed, considering the number of executed operations for running time purpose. Moreover, the authors have compared the system capacity via the throughput obtained with the HH solution, and its variants combined with three complexity reduction mechanisms. These tools consist of an initial allocation bit vector calculated by rounding the results of the water-filling (WF) solution, the multiple subchannels per iteration updating and the adoption of a subchannel grouping procedure. Their findings indicate that the update of multiple subchannels and the subcarriers grouping techniques reduce the number of iterations required for convergence of the original HH, with some throughput degradation. Also, the bit-allocation mechanism based on the WF is deployed as an alternative to overcome the HH solution, increasing the computational complexity.

关键词： frequency division multiple access iterative methods OFDM modulation computational complexity greedy algorithms optimisation telecommunication channels hybrid Hughes-Hartogs power allocation algorithms OFDMA systems discrete solution transmission rate maximisation problem power constraint greedy algorithms solution characterisation computational complexity analysis system capacity HH solution complexity reduction mechanisms initial allocation bit vector water-filling solution multiple subchannels iteration updating subchannel grouping procedure bit-allocation mechanism

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A hybrid forward algorithm for RBF neural network construction

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2006年第6期17卷 1439-1451页

作者： Peng, Jian-Xun Li, Kang Huang, De-Shuang Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness.

关键词： analytic framework computational complexity analysis parameter optimization radial basis function (RBF) neural network structure determination

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Automated higher-order complexity analysis

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THEORETICAL COMPUTER SCIENCE 2004年第1-2期318卷 79-103页

作者： Benzinger, R Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

This paper describes the automated complexitly analysis (ACA) system for automated higher-order complexity analysis of functional programs synthesized with the NUPRL proof development system. We introduce a general framework for defining models of computational complexity for functional programs based on an annotation of a given operational language semantics. Within this framework, we use type decomposition and polynomalization to express the complexity of higher-order terms. Symbolic interpretation of open terms automates complexity analysis, which involves generating and solving higher-order recurrence equations. Finally, the use of the ACA system is demonstrated by analyzing three different implementations of the pigeonhole principle. (C) 2003 Elsevier B.V. All rights reserved.

关键词： computational complexity analysis functional programs higher-order complexity program synthesis feasible mathematics

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Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第6期42卷 1941-1954页

作者： Al-Nahhal, Mohamed Al-Nahhal, Ibrahim Dobre, Octavia A. Soman, Sunish Kumar Orappanpara Mem Univ Fac Engn & Appl Sci St John NF A1C 5S7 Canada Ulster Univ Sch Engn Belfast BT15 1ED North Ireland

This paper proposes two novel neural network (NN) structures to estimate long-term steady linear and nonlinear signal-to-noise ratio (SNR) components in optical fiber communication systems. The first proposed structure is a parallel NN-based (ParNN) estimator, which estimates each SNR component using a different NN structure and input feature set. A combination of gated recurrent unit and dense layers is used to estimate the linear SNR component. On the other hand, the nonlinear SNR component is estimated using a combination of convolutional layer with dense layer. The proposed input features of the ParNN estimator are generated solely from the received signal without knowledge of the transmitted signal. These features are formed of the lower quartile, upper quartile, and entropy, which can accurately characterize the behavior of the SNR components by measuring the received signal spread and uncertainty. For further improvement of the ParNN estimator, an additional stage is added to form the proposed enhanced ParNN (EParNN) estimator. This additional stage consists of two feedforward NNs (FFNNs), each with a single dense layer, where the first FFNN is used to estimate the linear SNR component and the second one estimates the nonlinear SNR component. The input of this additional stage is a combination of the input features and output of the ParNN estimator. The computational complexity is derived for the proposed estimators. The training and testing dataset is built from 16-ary quadrature amplitude modulation of a dual-polarization on a wide range of standard single-mode fiber system configurations, e.g., number of wavelength division multiplexing channels, optical launch power, and number of spans. Numerical results demonstrate that the proposed ParNN estimator achieves better SNR estimation accuracy with comparable computational complexity compared to the most efficient work in the literature. The proposed ParNN estimator can independently estimate each SNR compone

关键词： computational complexity analysis convolutional layer dense layer gated recurrent unit optical fiber communications signal-to-noise ratio (SNR) estimation

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Optimised hybrid localisation with cooperation in wireless sensor networks

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IET SIGNAL PROCESSING 2017年第3期11卷 341-348页

作者： Khan, Muhammad W. Salman, Naveed Kemp, Andrew H. Univ Leeds Sch Elect & Elect Engn Leeds W Yorkshire England

In this study, the authors introduce a novel hybrid cooperative localisation scheme when both distance and angle measurements are available. Two linear least squares (LLS) hybrid cooperative schemes based on angle of arrival-time of arrival (AoA-ToA) and AoA-received signal strength (AoA-RSS) signals are proposed. The proposed algorithms are modified to accommodate cooperative localisation in resource constrained networks where only distance measurements are available between target sensors (TSs) while both distance and angle measurements are available between reference sensors and TSs. Furthermore, an optimised version of the LLS estimator is proposed to further enhance the localisation performance. Moreover, localisation of sensor nodes in networks with limited connectivity (partially connected networks) is also investigated. Finally, computational complexity analysis of the proposed algorithms is presented. Through simulation, the superior performance of the proposed algorithms over its non-cooperative counterpart and the hybrid signal based iterative non-linear least squares algorithms is demonstrated.

关键词： wireless sensor networks iterative methods least squares approximations wireless sensor networks novel hybrid cooperative localisation scheme linear least squares hybrid cooperative schemes target sensors LLS estimator computational complexity analysis hybrid signal based iterative nonlinear least squares algorithms

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Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2013年第1期3卷 75-85页

作者： Chen, Taihai Mazomenos, Evangelos B. Maharatna, Koushik Dasmahapatra, Srinandan Niranjan, Mahesan Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

In this paper, we first present a detailed study on the trade-off between the computational complexity (directly related to the power consumption) and classification accuracy for a number of classifiers for classifying normal and abnormal electrocardiograms (ECGs). In our analysis, we consider the spectral energy of the constituent waves of the ECG as the discriminative feature. Starting with the exhaustive exploration of single heart-beat- based classification to ascertain the complexity-accuracy trade-off in different classification algorithms, we then extend our study for multiple heartbeat-based classification. We use data available in Physionet as well as samples from Southampton General Hospital Cardiology Department for our investigation. Our primary conclusion is that a classifier based on linear discriminant analysis (LDA) achieves comparable level of accuracy to the best performing support vector machine classifiers with advantage of significantly reduced computational complexity. Subsequently, we propose an ultra low-power circuit implementation of the LDA classifier that could be integrated with the ECG sensor node enabling on-body normal and abnormal ECG classification. The simulated circuit is synthesized at 130 nm technology and occupies 0.70 mm(2) of silicon area (0.979 mm(2) after place and route) while it consumes 182.94 nW @ 1.08 V, estimated with Synopsys PrimeTime when operating at 1 KHz. These results clearly demonstrate the potential for low-power implementation of the proposed design.

关键词： computational complexity analysis discrete wavelet transform electrocardiogram (ECG) classification low energy remote healthcare applications

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