Action categorization with modified hidden conditional random field

In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.     

Segmentation and classification of hyperspectral images using watershed transformation

Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures.     

An improved new error estimation algorithm for optimal filter lengths for stereophonic acoustic echo cancellation

In this paper, we propose an new error estimate algorithm (NEEA) for stereophonic acoustic echo cancellation (SAEC) that is based on the error estimation algorithm (EEA) in [Nguyen-Ky T, Leis J, Xiang W. An improved error estimate algorithm for stereophonic acoustic echo cancellation system. In: International conference on signal processing and communication systems, ICSPCS’2007, Australia; December 2007]. In the EEA and NEEA, with the minimum error signal fixed, we compute the filter lengths so that the error signal may approximate the minimum error signal. When the echo paths change, the adaptive filter automatically adjusts the filter lengths to the optimum values. We also investigate the difference between the adaptive filter lengths. In contrast with the conclusions in [Khong AWH, Naylor PA. Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms. IEEE Trans Audio, Speech, Lang Process 2006;14(3):785-96, Gansler T, Benesty J. Stereophonic acoustic echo cancellation and two channel adaptive filtering: an overview. Int J Adapt Control Signal Process 2000;4:565-86, Benesty J, Gansler T. A multichannel acoustic echo canceler double-talk detector based on a normalized cross-correlation matrix. Acoust Echo Noise Control 2002;13(2):95-101, Gansler T, Benesty J. A frequency-domain double-talk detector based on a normalized cross-correlation vector. Signal Process 2001;81:1783-7, Eneroth P, Gay SL, Gansler T, Benesty J. A real-time implementation of a stereophonic acoustic echo canceler. IEEE Trans. Speech Audio Process 2001;9(5):513-23, Gansler T, Benesty J. New insights into the stereophonic acoustic echo cancellation problem and an adaptive nonlinearity solution. IEEE Trans. Speech Audio Process 2002; 10(5):257-67, Benesty J, Gansler T, Morgan DR, Sondhi MM, Gay SL. Advances in network and acoustic echo cancellation. Berlin: Springer-Verlag; 2001], our simulation results have shown that the filter lengths can be different. Our simulation results also confirm that the NEEA is better than EEA and SM-NLMS algorithm in terms of echo return loss enhancement.     

A modeling approach on the TelosB WSN platform power consumption

Substantial characteristics of wireless sensor networks, such as autonomy and miniature size, are achieved at the expense of restricted energy resources. Optimal resource management is thus among the most important challenges in WSNs development and its success requires accurate and practical models based on detailed insight concerning the factors contributing to the overall power consumption of a WSN mote. To achieve such awareness, that will enable models development, appropriate measuring test-beds and methodologies are needed, facilitating reliable and accurate power consumption measurements of critical functionalities.To cover the need for energy models that precisely define the power consumption behavior of WSN hardware platforms, this paper contributes with a measuring methodology including three steps: the design and implementation of a measuring system for a wide range of power consumption thresholds, the identification, isolation and measurement of elementary functionalities of a WSN platform with respect to their contribution to the overall mote power consumption, and the extraction of valuable conclusions based on the respective measurements resulting in the composition of a practical, yet accurate power consumption model.     

Modular analysis and modelling of risk scenarios with dependencies

The risk analysis of critical infrastructures such as the electric power supply or telecommunications is complicated by the fact that such infrastructures are mutually dependent. We propose a modular approach to the modelling and analysis of risk scenarios with dependencies. Our approach may be used to deduce the risk level of an overall system from previous risk analyses of its constituent systems. A custom made assumption-guarantee style is put forward as a means to describe risk scenarios with external dependencies. We also define a set of deduction rules facilitating various kinds of reasoning, including the analysis of mutual dependencies between risk scenarios expressed in the assumption-guarantee style.     

C-AMTE: A location mechanism for flexible cache management in chip multiprocessors

This paper describes Constrained Associative-Mapping-of-Tracking-Entries (C-AMTE), a scalable mechanism to facilitate flexible and efficient distributed cache management in large-scale chip multiprocessors (CMPs). C-AMTE enables fast locating of cache blocks in CMP cache schemes that employ one-to-one or one-to-many associative mappings. C-AMTE stores in per-core data structures tracking entries to avoid on-chip interconnect traffic outburst or long distance directory lookups. Simulation results using a full system simulator demonstrate that C-AMTE achieves improvement in cache access latency by up to 34.4%, close to that of a perfect location strategy.     

On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters

A model for the computational cost of the finite-difference time-domain (FDTD) method irrespective of implementation details or the application domain is given. The model is used to formalize the problem of optimal distribution of computational load to an arbitrary set of resources across a heterogeneous cluster. We show that the problem can be formulated as a minimax optimization problem and derive analytic lower bounds for the computational cost. The work provides insight into optimal design of FDTD parallel software. Our formulation of the load distribution problem takes simultaneously into account the computational and communication costs. We demonstrate that significant performance gains, as much as 75%, can be achieved by proper load distribution.     

Interest-Intended Piece Selection in BitTorrent-like peer-to-peer file sharing systems

BitTorrent is a popular peer-to-peer file sharing system and a target file shared through BitTorrent is partitioned into pieces and downloaded from multiple peers in parallel in order to shorten the download process. However, due to peer dynamics in P2P networks, rare pieces may be lost and thus lead to the so-called last piece problem. BitTorrent employs rarest-first piece selection algorithm to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. In this paper, we propose an Interest-Intended Piece Selection (IIPS) algorithm aiming at better alleviating the last piece problem while maintaining stable cooperation between peers. IIPS is named interest intended in that every IIPS peer favors pieces that, if downloaded, would increase the probability of being interesting to its cooperating peers. Simulation results show that IIPS achieves less occurrences of piece loss under tough conditions and slightly outperforms the BitTorrent’s rarest-first algorithm in terms of higher piece diversity.     

A module-based LSB substitution method with lossless secret data compression

All the various data hiding methods can be simply divided into two types: (1) the extracted important data are lossy, (2) the extracted important data are lossless. The proposed method belongs to the second type. In this paper, a module-based substitution method with lossless secret data compression function is used for concealing smoother area of secret image by modifying fewer pixels in the generated stego-image. Compared with the previous data hiding methods that extract lossless data, the generated stego-image by the proposed method is always with better quality, unless the hidden image is with very strong randomness.     

MEMS AC voltage reference for miniaturized instrumentation and metrology

This work explores MEMS (Micro-Electro-Mechanical Systems) potentialities to fabricate AC voltage references through mechanical-electrical transduction that could be used for high precision electrical metrology and for applications in miniaturized instrumentation. AC voltage reference ranging from 5 V to 90 V have been designed and fabricated using the same Epitaxial Silicon On Insulator (SOI) surface micromachining process allowing an accurate control of both dimensions and material properties. The measured MEMS AC voltage reference values have been found in a good agreement with CoventorWare calculations. These tests structures have also been used to develop the read-out electronics to drive the MEMS.