High performance FPGA implementation of the mersenne twister

Efficient generation of random and pseudorandom sequences is of great importance to a number of applications [4]. In this paper, an efficient implementation of the Mersenne Twister is presented. The proposed architecture has the smallest footprint of all published architectures to date and occupies only 330 FPGA slices. Partial pipelining and sub-expression simplification has been used to improve throughput per clock cycle. The proposed architecture is implemented on an RC1000 FPGA Development platform equipped with a Xilinx XCV2000E FPGA, and can generate 20 million 32 bit random numbers per second at a clock rate of 24.234 MHz. A through performance analysis has been performed, and it is observed that the proposed architecture clearly outperforms other existing implementations in key comparable performance metrics

3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

DWT/PCA face recognition using automatic coefficient selection

In PCA-based face recognition, there is often a trade-off between selecting the most relevant parts of a face image for recognition and not discarding information which may be useful. The work presented in this paper proposes a method to automatically determine the most discriminative coefficients in a DWT/PCA-based face recognition system, based on their inter-class and intra-class standard deviations. In addition, the eigenfaces used for recognition are generally chosen based on the value of their associated eigenvalues. However, the variance indicated by the eigenvalues may be due to factors such as variation in illumination levels between training set faces, rather than differences that are useful for identification. The work presented proposes a method to automatically determine the most discriminative eigenfaces, based on the inter-class and intra-class standard deviations of the training set eigenface weight vectors. The results obtained using the AT&T database show an improvement over existing DWT/PCA coefficient selection techniques

Novel sparse OBC based distributed arithmetic architecture for matrix transforms

Inner product (IP) forms the basis of a number of signal processing algorithms and applications such as transforms, filters, communication systems etc. Distributed arithmetic (DA) provides an effective methodology to implement IP of vectors and matrices using a simple combination of memory elements, adders and shifters instead of lumped multipliers. This bit level rearrangement results in much higher computational efficiencies and yields compact designs highly suited for high performance resource constrained applications. Offset binary coding (OBC) is an effective technique to further optimize the DA, and allows us to reduce the memory requirements by a factor of two, with minimum additional computational complexity. This makes OBC-DA attractive for applications that are both resource and memory constrained. In addition, sparse matrix factorization techniques can be exploited to further reduce the size of the DA-ROMs. In this paper, the design and implementation of a novel OBC based DA is demonstrated using a generic architecture for implementing discrete orthogonal transforms (DOTs). Implementation is performed on the Xilinx Virtex-II Pro field programmable gate array (FPGA), and a detailed comparison between conventional and OBC based DA is presented to highlight the trade offs in various design metrics including performance, area and power

A general framework for efficient FPGA implementation of matrix product

Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance systems are required by the developers for fast processing of computationally intensive applications. Reconfigurable hardware devices in the form of Filed-Programmable Gate Arrays (FPGAs) have been proposed as viable system building blocks in the construction of high performance systems at an economical price. Given the importance and the use of matrix algorithms in scientific computing applications, they seem ideal candidates to harness and exploit the advantages offered by FPGAs. In this paper, a system for matrix algorithm cores generation is described. The system provides a catalog of efficient user-customizable cores, designed for FPGA implementation, ranging in three different matrix algorithm categories: (i) matrix operations, (ii) matrix transforms and (iii) matrix decomposition. The generated core can be either a general purpose or a specific application core. The methodology used in the design and implementation of two specific image processing application cores is presented. The first core is a fully pipelined matrix multiplier for colour space conversion based on distributed arithmetic principles while the second one is a parallel floating-point matrix multiplier designed for 3D affine transformations.Peer reviewe

Floating-Point Matrix Product on FPGA

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Machine learning paradigms for modeling spatial and temporal information in multimedia data mining

Multimedia data mining and knowledge discovery is a fast emerging interdisciplinary applied research area. There is tremendous potential for effective use of multimedia data mining (MDM) through intelligent analysis. Diverse application areas are increasingly relying on multimedia under-standing systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, machine learning, pattern recognition, multimedia databases, and smart sensors. The main mission of this special issue is to identify state-of-the-art machine learning paradigms that are particularly powerful and effective for modeling and combining temporal and spatial media cues such as audio, visual, and face information and for accomplishing tasks of multimedia data mining and knowledge discovery. These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics. A number of papers have been submitted to the special issue in the areas of imaging, artificial intelligence; and pattern recognition and five contributions have been selected covering state-of-the-art algorithms and advanced related topics. The first contribution by D. Xiang et al. “Evaluation of data quality and drought monitoring capability of FY-3A MERSI data” describes some basic parameters and major technical indicators of the FY-3A, and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. The second contribution by A. Belatreche et al. “Computing with biologically inspired neural oscillators: application to color image segmentation” investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to gray scale and color image segmentation, an important task in image understanding and object recognition. The major contribution of this paper is the ability to use neural oscillators as a learning scheme for solving real world engineering problems. The third paper by A. Dargazany et al. entitled “Multibandwidth Kernel-based object tracking” explores new methods for object tracking using the mean shift (MS). A bandwidth-handling MS technique is deployed in which the tracker reach the global mode of the density function not requiring a specific staring point. It has been proven via experiments that the Gradual Multibandwidth Mean Shift tracking algorithm can converge faster than the conventional kernel-based object tracking (known as the mean shift). The fourth contribution by S. Alzu’bi et al. entitled “3D medical volume segmentation using hybrid multi-resolution statistical approaches” studies new 3D volume segmentation using multiresolution statistical approaches based on discrete wavelet transform and hidden Markov models. This system commonly reduced the percentage error achieved using the traditional 2D segmentation techniques by several percent. Furthermore, a contribution by G. Cabanes et al. entitled “Unsupervised topographic learning for spatiotemporal data mining” proposes a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency Identification (RFID) data. The new unsupervised algorithm depicted in this article is an efficient data mining tool for behavioral studies based on RFID technology. It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning. Finally, we would like to thank all those who helped to make this special issue possible, especially the authors and the reviewers of the articles. Our thanks go to the Hindawi staff and personnel, the journal Manager in bringing about the issue and giving us the opportunity to edit this special issue

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

CFD analysis of tensile conical membrane structures as microclimate modifiers in hot arid regions

Extensive research has been undertaken to understand form finding and the structural behaviour of tensile membrane structures (TMS). In comparison, little is known about the environmental behaviour of this class of structure and a lack of predictive tools for informing their design serves as a barrier to their wider acceptance by the building industry. This paper presents part of a research study carried out to investigate the effect of various forms, configurations and orientations of conical TMS on the airflow behaviour under, around and above these structures for application in hot arid climates. The airflow behaviour and airspeed in an enclosed or semi-enclosed space significantly influence thermal comfort in hot-arid climates. Part of this research involved a parametric study of the airflow behaviour of conical TMS scaled models with different geometrical configurations and orientations in an environmental wind tunnel. The results of this experimental investigation were compared with results from investigations carried out in computational fluid dynamics (CFD) software “FLUENT 5.5” on 1:20 and 1:1 models. The comparison showed that the CFD modelling was able to replicate much of the behaviour observed in the wind tunnel and the approach was extended to explore arrays of conical TMS. This paper presents the CFD modelling undertaken to explore airflows over varying conical tensile fabric structures. The modelling is carried over single conical structures either inverted or straight. These investigations explore how the form, geometry and orientation of a conical TMS affect the airflow rates. The paper concludes with a discussion of how the form and geometrical configuration affect the airflow behaviour and hence the thermal comfort level of the occupants of the semi-enclosed space