MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

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Design Of Analog Cmos Integrated Circuits

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

/pdf/surface-electromyography-signal-processing-and-138ym9ds1t.pdf

Surface Electromyography Signal Processing and Classification Techniques

Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control BFOA is inspired by the social foraging behavior of Escherichia coli BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains The underlying biology behind the foraging strategy of Ecoli is emulated in an extraordinary manner and used as a simple optimization algorithm This chapter starts with a lucid outline of the classical BFOA It then analyses the dynamics of the simulated chemotaxis step in BFOA with the help of a simple mathematical model Taking a cue from the analysis, it presents a new adaptive variant of BFOA, where the chemotactic step size is adjusted on the run according to the current fitness of a virtual bacterium Nest, an analysis of the dynamics of reproduction operator in BFOA is also discussed The chapter discusses the hybridization of BFOA with other optimization techniques and also provides an account of most of the significant applications of BFOA until date

/pdf/bacterial-foraging-optimization-algorithm-theoretical-378t1v85q1.pdf

Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications

In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he called the bacterial foraging optimization algorithm (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. This paper presents a mathematical analysis of the chemotactic step in BFOA from the viewpoint of the classical gradient descent search. The analysis points out that the chemotaxis employed by classical BFOA usually results in sustained oscillation, especially on flat fitness landscapes, when a bacterium cell is close to the optima. To accelerate the convergence speed of the group of bacteria near the global optima, two simple schemes for adapting the chemotactic step height have been proposed. Computer simulations over several numerical benchmarks indicate that BFOA with the adaptive chemotactic operators shows better convergence behavior, as compared to the classical BFOA. The paper finally investigates an interesting application of the proposed adaptive variants of BFOA to the frequency-modulated sound wave synthesis problem, appearing in the field of communication engineering.

/pdf/adaptive-computational-chemotaxis-in-bacterial-foraging-3d2gzdxnws.pdf

Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis

A wide band two stage CMOS voltage controlled ring oscillator (VCRO) based on fully differential delay cells proposed by Yan and Luong has been analyzed. The two stage and three stage VCO circuits are realized using GPDK 90 nm CMOS technology. The design parameters are varied widely and performance of the circuit is evaluated in this work. This scaled VCO has a very wide operating frequency range from 1.22 GHz to 3.22 GHz, which corresponds to a tuning range of 62.11%. Its power consumption is 9.61 mW and the phase noise is about - 90.01 dBc/Hz at 600 kHz offset from 1.74 GHz. The phase noise of the differential VCO is improved by cascading more number of stages at the cost of more power consumption. Three-stage differential delay VCO is implemented and it is observed to achieve - 95.47dBc/Hz of phase noise which is about 6 dB better than the two stage case with better tuning range of 64.23%. However the power consumption of the circuit increases to 14.44 mW.

Performance analysis and design of wideband CMOS voltage controlled ring oscillator

This paper presents a novel design methodology for design of optimal and robust current starved voltage controlled oscillator (CSVCO) circuit. A recently developed multiobjective optimization technique infeasibility driven evolutionary algorithm is used to minimize the power and the phase noise of the circuit at its schematic and physical level. The multiobjective optimization is carried out by taking into account the extracted parasitics that would be present in the physical integrated circuit and the random variations of parameters during fabrication in foundry. This method helps the designer in semiconductor industry by effectively reducing several time consuming design iterations to a single iteration ensuring the near optimal performance of the CSVCO. The performance of the circuit is validated by carrying out simulations for transient and noise analysis in Cadence tools using 90 nm 1P9M CMOS process.

A Multiobjective Optimization Based Fast and Robust Design Methodology for Low Power and Low Phase Noise Current Starved VCO

The present paper proposes a bacteria foraging optimization based independent component analysis (BFOICA) algorithm assuming a linear noise free model. It is observed that the proposed BFOICA algorithm overcomes the long standing permutation ambiguity and recovers the independent components(IC) in a fixed order which depends on the statistical characteristics of the signals to be estimated. The paper compares the performance of BFOICA algorithm with the constrained genetic algorithm based ICA (CGAICA) and most popular fast ICA algorithm. The proposed algorithm offers comparable or even better performance compared to fast ICA algorithm and faster convergence and better mean square error performance compared to CGAICA.

Bacteria Foraging Based Independent Component Analysis

Independent Component Analysis, a computationally efficient blind statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper attempts to treat the fundamental concepts involved in the independent component analysis (ICA) technique and reviews different ICA algorithms. A thorough discussion of the algorithms with their merits and weaknesses has been carried out. Applications of the ICA algorithms in different fields of science and technology have been reviewed. The limitations and ambiguities of the ICA techniques developed so far have also been outlined. Though several articles have reviewed the ICA techniques in literature, they suffer from the limitation of not being comprehensive to a first time reader or not incorporating the latest available algorithm and their applications. In this work, we present different ICA algorithms from their basics to their potential application...

/pdf/a-review-of-independent-component-analysis-techniques-and-2ozf4obi9p.pdf

Debiprasad Priyabrata Acharya

Papers

Performance analysis and design of wideband CMOS voltage controlled ring oscillator

A Multiobjective Optimization Based Fast and Robust Design Methodology for Low Power and Low Phase Noise Current Starved VCO

Bacteria Foraging Based Independent Component Analysis

A Review of Independent Component Analysis Techniques and their Applications

A high stable 8T-SRAM with bit interleaving capability for minimization of soft error rate