Signal Processing Research Papers - Academia.edu (original) (raw)

The current-mode instrumentation amplifier based on second-generation current conveyors (CCII) offers many benefits over conventional instrumentation amplifier architectures. It does not need any matched components to achieve high CMRR and its bandwidth is not gain-bandwidth product limited. Therefore, the wideband CMOS current conveyor has been used in this structure. HSPICE simulations have shown that its performance is quite satisfactory for wideband current-mode signal processing. Furthermore, different CMRR enhancement techniques of current-mode instrumentation amplifiers are discussed. The new simulated instrumentation amplifier provides approximately 40 dB of improvement on the high-frequency CMRR up to the 3-dB corner frequency and even higher. In addition, composite conveyor techniques improving the instrumentation amplifier performance are presented

- by
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- Signal Processing, High Frequency, Low Power Electronics, Instrumentation Amplifier

Java-DSP is a freely accessible web-based software, primarily used in signal processing education and research. In this paper, we present Java-DSP modules that have been developed for the study and analysis of the MPEG-1 Layer III algorithm. We have embedded JLayer1.0, an open source MP3 library, to Java-DSP and developed an intuitive interface to expose undergraduate and graduate students to the several modules in the encoding/decoding process. The Java-DSP MP3 decoder block is an interactive function which can be used to examine the characteristics and visualize outputs of different modules in the algorithm. Some of the important functions incorporated in the proposed interface include the analysis of the hybrid filter bank, polyphase filters and the window switching based on perceptual criteria. The MP3 algorithm represents a compelling framework for teaching certain aspects in DSP. We are using this module to introduce filter banks and windowing to undergraduate students.

- by Andreas Spanias
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- Signal Processing, Open Source, Graduate Student, Undergraduate Student

Key issues related to the modeling of ultra-high speed transmission are discussed in this paper. These topics include components modeling, link modeling and BER estimation. Different solutions for the transport of 100 Gb/s over a single wavelength including technologies such as coherent detection, polarization multiplexing, optical OFDM, and digital signal processing are reviewed and compared with means of numerical simulations.

- by Igor Koltchanov
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- Signal Processing, Modeling, Digital Signal Processing, Numerical Simulation

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring.

- by Jeronimo Arenas-Garcia
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- Mechanical Engineering, Image Processing, Remote Sensing, Machine Learning

Agarwood oil has been widely used especially in fragrance, incense, prayers and traditional medicinal. In the Middle East, the market demand for Agarwood oil is very high. Agarwood oil is traded based on high grade and low grade, corresponding to expensive price and cheap price, respectively. Currently, the grading of Agarwood oil, specifically Aquilaria Malaccensis, depends on its physical appearance such as color and odour. This paper presents the analysis of Aquilaria Malaccensis based on GC-MS data. The work involves of statistical technique such as boxplot and PCA. The analysis part was done on 64 chemical compounds on 7 samples of agarwood oil obtained by Forest Research Institute Malaysia (FRIM). It was done via MATLAB ver. R2010a. The result shows that the distribution of chemical compounds in Agarwood oil is not normal and five componets is identified from 64variables Agarwood oil samples, gathered by boxplot and PCA, individually.

- by Mohd Taib
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- Signal Processing, Traditional Medicine, Chromatography, Principal Component Analysis

This paper presents an overview of analysis agarwood oil and its quality grading. The review suggested agarwood oil can be graded according to their chemical properties and so that there is a common standard recognized worldwide on grading the agarwood oil. Analysis based on chemical profiles is required to ensure that agarwood oil can be classified based on their respective classes or grades where the accurate results can be measured. Conventionally, the grading of agarwood oil is performed by trained human graders (sensory panels) depends on its physical appearance such as color, odor, high fixative and consumer perception. However, this method is limited due to human nose cannot accept many samples in one time and easily get fatigues especially when dealing with continuous production. The human sensory panel also limited in terms of subjectivity, poor reproducibility, time consumption and large labour expense. These are constraining factors in increasing agarwood oil trade and market penetration.

- by Mohd Taib
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- Signal Processing, Grading

In this paper, a synergy of advanced signal processing and soft computing strategies is applied in order to identify different types of human brain tumors, as a help to confirm the histological diagnosis of experts and consequently to facilitate the decision about the correct treatment or the necessity of an operation. A computational tool has been developed that merges, on the one hand, wavelet transform to reduce the size of the biomedical spectra and to extract the main features, and on the other hand, Support Vector Machine and Neural Networks to classify them. The influence of some of the configuration parameters of each of those soft computing techniques on the clustering is analyzed. These two methods and another one based on medical knowledge are compared. The classification results obtained by these computational tools are promising specially taking into account that medical knowledge has not been considered and that the number of samples of each class is very low in some cases.

- by Mladen Kezunovic
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- Signal Processing, Digital Signal Processing, Algorithm, Power Transmission

... donning. This system is designed to be embedded in Bio-Suit, a revolutionary space suit concept developed for many years by Prof. Dava ... exploration. I.Bio-SUIT SYSTEM The Bio-Suit System is a project developed by Prof. Dava ...

- by G. Trotti and +1
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- Design, Signal Processing, Fuzzy Logic, Neural Network

We introduce length-sensing and control schemes for the dual-recycled cavity-enhanced Michelson interferometer configuration proposed for the Advanced Laser Interferometer Gravitational Wave Observatory ͑LIGO͒. We discuss the principles of this scheme and show methods that allow sensing and control signals to be derived. Experimental verification was carried out in three benchtop experiments that are introduced. We present the implications of the results from these experiments for Advanced LIGO and other future interferometric gravitational-wave detectors.

- by Kenneth Strain
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- Mechanical Engineering, Signal Processing, Sensitivity Analysis, Interferometry

Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor-actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change.

A model of the multibeam echosounding process was developed. This model has now been used as the basis for the application of a model inversion technique, with the aim of analyzing midwater multibeam echosounder data, for fisheries... more

- by Arthur Sale
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- Data Mining, Signal Processing, Data Analysis, Underwater Acoustics

Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance ͑SR͒. In a previous study ͓Phys. Lett. A 243, 281 ͑1998͔͒, we showed that when colored noise with 1/f ␤ spectrum is added to the FitzHugh-Nagumo ͑FHN͒ neuronal model, the optimal noise variance for SR could be minimized with ␤Ϸ1. In this study, we investigate analytically how the noise color (␤) affects the SR profile in a linearized version of the FHN model. We demonstrate that the aforementioned effect of 1/f noise is related to the dynamical characteristics of the model neuron, i.e., the refractory period, the low-pass filtering effect of the membrane capacitance, and the high-pass filtering effect of the recovery variable.

- by Daichi Nozaki
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- Biophysics, Signal Processing, Stochastic processes, Colored Noise

Identification of people in surveillance videos is an important problem and MPEG-7 visual descriptors are utilized for such recognition in a regional manner, which result from independently moving subjects in front of stationary cameras. While background modeling is achieved by using a hierarchical non-parametric Parzen-window approach, the resulting regional descriptors are classified by combining experts via different combination rules. Simulation results enjoy a promising recognition performance for the tested data set.

- by B. Orten
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- Signal Processing, Principal Component Analysis, Gaussian processes, Background modeling

The paper presents the digital implementation of signal processing algorithms that simulate natural concert hall reverberation. It deals with the complete artificial reverberators proposed by Schroeder, Moorer and Gardner. For each reverberation algorithm the implementation was done in two steps: 1. the algorithm is checked using a model in Matlab Simulink; 2. the Verilog code is written and tested. The results prove that the Verilog design is feasible and can be further developed for acoustic improvements of rooms.

- by Botond Sándor Kirei
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- Signal Processing
- by Arzad Kherani
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- Computer Science, Signal Processing, Congestion Control, Queueing theory

For many audiovisual applications, the integration and synchronization of audio and video signals is essential. The objective of this paper is to develop a system that displays the active objects in the captured video signal, integrated with their respective audio signals in the form of text. The video and audio signals are captured and processed separately. The signals are buffered and integrated and synchronized using a time-stamping technique. Time-stamps provide the timing information for each of the audio and video processes, the speech recognition and the object detection, respectively. This information is necessary to correlate the audio packets to the video frames. Hence, integration is achieved without the use of video information, such as lip movements. The results obtained are based on a specific implementation of the speech recognition module, which is determined to be the bottleneck process in the proposed system.

- by Mohamed Elhelaly
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- Computer Science, Signal Processing, Audio Signal Processing, Signal Integrity

The paper analyses the possibility to recover different biomedical signals if limited number of samples is available. Having in mind that monitoring of health condition is done by measuring and observing key parameters such as heart activity through electrocardiogram or anatomy and body processes through magnetic resonance imaging, it is important to keep the quality of the reconstructed signal as better as possible. To recover the signal from limited set of available coefficients, the Compressive Sensing approach and optimization algorithms are used. The theory is verified by the experimental results.

- by Vesna Mandic
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- Signal Processing, Multimedia, Digital Signal Processing, Compressive Sensing

Implementation of carrier-sensing-based medium access control (MAC) protocols on inexpensive reconfigurable radio platforms has proven challenging due to long and unpredictable delays associated with both signal processing on a general purpose processor (GPP) and the interface between the RF front-end and the GPP. In this paper, we develop a split-functionality implementation of a contention based carriersensing MAC, in which some of the functions reside on an FPGA and others reside in the GPP. We provide an FPGAbased implementation of a carrier sensing block and develop two versions of a CSMA MAC protocol based upon this block. We experimentally test the performance of the resulting protocols in a multihop environment in terms of end-to-end throughput and required frame retransmissions. We cross-validate these results with a network simulator with modules modified to reflect the mean and variance of delays measured in components of the real software-defined radio system.

- by Paolo Di Francesco
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- Signal Processing, FPGA, Field-Programmable Gate Arrays, Sensors

The reliable condition monitoring of machines and the early detection of faults play an important role in condition-based maintenance. Information on the condition of machines is needed in different forms but finally it has to be in as simple form as possible so that it can be utilised in the decision-making by the maintenance and management personnel. In the field of machine condition monitoring, there is a wide range of signal processing methods, which are used in feature extraction and fault diagnosis. Differentiation and integration are very common and important operations in vibration signal processing. The order of derivative is typically an integer number but it can also be any fractional number. Especially in the challenging fault and process cases, signals whose order of derivative is a real or complex number could be clearly more sensitive in fault detection than the commonly used signals: displacement, velocity and acceleration. In this paper, the application of complex o...

In the area of broad-band antenna array signal processing, the global minimum of a quadratic equality constrained quadratic cost minimization problem is often required. The problem posed is usually characterized by a large optimization space (around 50-90 tuples), a large number of linear equality constraints, and a few quadratic equality constraints each having very low rank quadratic constraint matrices. Two main difficulties arise in this class of problem. Firstly, the feasibility region is nonconvex and multiple local minima abound. This makes conventional numerical search techniques unattractive as they are unable to locate the global optimum consistently (unless a finite search area is specified). Secondly, the large optimization space makes the use of decision-method algorithms for the theory of the reals unattractive. This is because these algorithms involve the solution of the roots of univariate polynomials of order to the square of the optimization space. In this paper we present a new algorithm which exploits the structure of the constraints to reduce the optimization space to a more manageable size. The new algorithm relies on linear-algebra concepts, basic optimization theory, and a multivariate polynomial root-solving tool often used by decision-method algorithms.

- by Antonio Cantoni
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- Mathematics, Applied Mathematics, Signal Processing, Linear Algebra

A new robust computationally efficient variable step-size LMS algorithm is proposed and it is applied for secondary path (SP) identification of feedforward and feedback active noise control (ANC) systems. The proposed variable step-size Griffiths' LMS (VGLMS) algorithm not only uses a step-size, but also the gradient itself, based on the cross-correlation between input and the desired signal. This makes the algorithm robust to both stationary and non-stationary observation noise and the additional computational load involved for this is marginal. Further, in terms of convergence speed and error, it is better than those by the Normalized LMS (NLMS) and the Zhang's method (Zhang in EURASIP J.

- by Veena Shantharam
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- Signal Processing, Cross Correlation, Active noise control, Convergence Rate
- by Albert Reuther
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- Mechanical Engineering, Signal Processing, Data Engineering, Prototypes

A novel regularization technique which can combine signals from all Global Positioning System (GPS) satellites for a given instant and a given receiver is developed to estimate the vertical total electron content (VTEC) values for the 24-hour period without missing any important features in the temporal domain. The algorithm is based on the minimization of a cost function which also includes a high pass penalty filter. Optional weighting function and sliding window median filter are added to enrich the processing and smoothing of the data. The developed regularized estimation algorithm is applied to GPS data for various locations for the solar maximum week of 23-28 April 2001. The parameter set that is required by the estimation algorithm is chosen optimally using appropriate error functions. This robust and optimum parameter set can be used for all latitudes and for both quiet and disturbed days. It is observed that the estimated TEC values are in general accordance with the TEC estimates from other global ionospheric maps, especially for quiet days and midlatitudes. Owing to its 30 s time resolution, the regularized VTEC estimates from the developed algorithm are very successful in representation and tracking of sudden temporal variations of the ionosphere, especially for high latitudes and during ionospheric disturbances.

- by Cemil Erol
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- Signal Processing, Multidisciplinary, GPS, Satellite Communication

Bu makalede imge sıkıştırma uygulamalarında kullanılmak uzere yeni bir renk dönüşümü metoduönerilmektedir. Bahsi geçen renk dönüşümünün katsayıları, imgenin renk histogramı kullanılarak hesaplanmakta ve bu sayede imgeyë ozel bir renk dönüşümü elde edilmektedir.Önerilen yeni dönüşümün sıkıştırma başarısı JPEG algoritması kullanılarak gösterilmiştir. Literatürde yapılan benzer testlerde sıkça kullanılan 15 resimüzerinde yapılan deneylerde algoritmanın farklı sıkıştırma oranlarına karşılık gelen PSNR degerlerine bakıldıgında, JPEG algoritmasına oranla daha iyi sonuçlar verdigi gözlenmiştir.

- by Alexander Suhre
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- Signal Processing, Data Compression, Image coding, Image compression

We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.

- by Mark Butala
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- Signal Processing, Algorithm, Multidisciplinary, Parameter estimation

Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. In this work, we describe a system for the recognition of musical instruments from isolated notes. We are introducing the use of a Time Encoded Signal Processing method to produce simple matrices from complex sound waveforms, for instrument note encoding and recognition. These matrices are presented to a Fast Artificial Neural Network (FANN) to perform instrument recognition with promising results in organ classification and reduced computational cost. The evaluation material consists of 470 tones from 19 musical instruments synthesized with 5 wide used synthesizers (Microsoft Synth, Creative SB Live! Synth, Yamaha VL-70m Tone Generator, Edirol Soft-Synth, Kontakt Player) and 84 isolated notes from 20 western orchestral instruments (Iowa University Database).

- by Giorgos Mazarakis
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- Artificial Intelligence, Signal Processing, Musical acoustics, Neural Network

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation towards solving vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or stateds based on wavelet mutli-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented.

- by Jacob Crossman
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- Engineering, Artificial Intelligence, Technology, Machine Learning

Inference of online social network users' attributes and interests has been an active research topic. Accurate identification of users' attributes and interests is crucial for improving the performance of personalization and recommender systems. Most of the existing works have focused on textual content generated by the users and have successfully used it for predicting users' interests and other identifying attributes. However, little attention has been paid to user generated visual content (images) that is becoming increasingly popular and pervasive in recent times. We posit that images posted by users on online social networks are a reflection of topics they are interested in and propose an approach to infer user attributes from images posted by them. We analyze the content of individual images and then aggregate the image-level knowledge to infer user-level interest distribution. We employ image-level similarity to propagate the label information between images, as well as utilize the image category information derived from the user created organization structure to further propagate the category-level knowledge for all images. A real life social network dataset created from Pinterest is used for evaluation and the experimental results demonstrate the effectiveness of our proposed approach.

- by Quanzeng You
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- Signal Processing

Westland Helicopters carried out a series of tests on the CH46 gearbox recording the vibration levels picked up by eight accelerometers while operating the transmission with faulted and fault-free components. This paper concentrates on characterizing the effect of a damaged spiral bevel pinion (fault number 4: Spiral Bevel Input Pinion Tooth Spalling). The small spiral bevel pinion (26T) is located on the same shaft as the large collector gear (74T). We demonstrate that the vibrations induced by the meshing of the collector gear are dominant even when damaged spiral bevel pinions are in place. In fact the damaged pinion modulates the torsional vibrations induced by the collector gear. The effect is analyzed using time-domain averaging and subsequent filtering. Considering the nature of the vibrations induced by the defected spiral bevel pinion we note that if the sample mean is negligible, time domain averaging techniques may fail.

- by M. Zacksenhouse
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- Signal Processing

Abstract This work introduces an Ambient Assisted Route Planner (A2RP) aimed for providing route planning in unknown indoor environments. System was first designed as an assistive mobility aid to be used by intelligent powered wheelchairs, although it can be suitable also for other autonomous mobile robotics systems, non-automated mobility device users or pedestrians. A2RP system is based on a set of XML description files that can be retrieved from the internet. These files contain all the information needed to access public or private buildings: floor maps, accessibility information, available routes and calibration landmarks. XML description files must be created, located and maintained in an internet server specially dedicated for this purpose. This paper presents description files structure and the associated software applications: a visual editor to build and maintain XML accessibility information files and a navigation setup program to be run on board of the intelligent wheelchair processor.

- by juan garcia
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- Signal Processing, Route Planning, Mobile Device, Indoor Environment

This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm.

- by Luc Vandendorpe
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- Signal Processing, Iterative Methods, Convergence, Parameter estimation

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimisation and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multitarget filters sequentially estimate the number of targets and their position in the state-space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks, and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.

- by Ba-Ngu Vo and +1
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- Signal Processing, Multidisciplinary, Target Tracking, Performance Evaluation

Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixedsize matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA's SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.

- by buthina Qatatsheh
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- Signal Processing, Wireless Sensor Networks, Neural Networks, Computer Hardware

An investigation into the feature extraction and selection of infant cry with asphyxia is presented in this paper. The feature of the cry signal was extracted using mel frequency cepstrum coefficient (MFCC) analysis and the significant coefficients were selected using orthogonal least square (OLS) algorithm. The effect of varying the number of MFCC filter banks on the feature selection was examined. It was found that the best set of coefficients could be achieved when 40 filter banks were used.

- by Mohamed Yassin
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- Pediatrics, Signal Processing, Feature Selection, Paediatrics
- by Erchin Serpedin
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- Engineering, Computer Science, Signal Processing, Wireless Communications

The proposed Biomedical Signal Processing Laboratory incorporates several components that enhance its usefulness for inquiry-based learning. The Laboratory orients around the physical construction and testing of a variety of simple signal processing circuit modules, introduced as lessons. can be easily determined through measurement with a BIOPAC data acqu tem software permits simple comparisons between real-world and simulated s tics. The modules can be combined in a step-by-step fashion to create a varie gnal processing systems. Signal processing systems established by the Labo from signals sourced from the student’s own body. Through the application of ition system and associated software, students can build and test signal proce nce against mathematical simulation using graphical comparisons, combine nals sourced from their own bodies and then analyze the results. In the proc their own bodies, students ’ curiosity is stimulated and they gain more control le to test and retest to...

Due to the growing computation rates of intensive signal processing applications, using Multiprocessor System on Chip (MPSoC) becomes an incontrovertible solution to meet the functional requirements. Today, Electronic System Level (ESL) design is considered a vital premise to overcome the design complexity intrinsic in the heterogeneity of these devices. However, the development of tools at the system level is in the face of extremely challenging requirements such as the rapid system prototyping, the accurate performance estimation, and the reliable design space exploration (DSE). Focusing on the issue of ESL development tools, this paper describes an MPSoC environment design which targets the Multidimensional Intensive Signal Processing (MISP) application domain. Within this environment, we have defined first a generic execution model that supports any type of MPSoC. It can adapt to any parallel application and handle efficiently the scheduling and synchronizations at all the levels of granularity. Second, a new Virtual Processor (VP) based simulation technique is proposed for implementing the execution model. This proposal leverages the high-level specification of the system to provide a heterogeneous MPSoCs simulation without using an Instruction Set Simulator (ISS). VP-based simulation is implemented in SystemC at a timed transactional level allowing a good trade-off between high simulation speed and performance estimation accuracy. The usefulness and the effectiveness of our MPSoC environment is illustrated through two MISP applications executed on a typical MPSoC. Results show that our approach enables fast MPSoC virtual prototyping, data transfers and timing analysis, and reliable DSE for architectural optimizations.

- by Rabie Ben Atitallah
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- Computer Science, Signal Processing, Timing Analysis, Computer Hardware

The substitution of digital representations for analog images provides access to methods for digital storage and transmission and enables the use of a variety of digital image processing techniques, including enhancement and computer assisted screening and diagnosis. Lossy compression can further improve the efficiency of transmission and storage and can facilitate subsequent image processing. Both digitization (or digital acquisition) and lossy compression alter an image from its traditional form, and hence it becomes important that any such alteration be shown to improve or at least not damage the utility of the image in a screening or diagnostic application. One approach to demonstrating in a quantifiable manner that a specific image mode is at least equal to another is by clinical experiment simulating ordinary practice and suitable statistical analysis. In this paper we describe a general protocol for performing such a verification and present preliminary results of a specific experiment designed to show that 12 bpp digital mammograms compressed in a lossy fashion to 0.015 bpp using an embedded wavelet coding scheme result in no significant differences from the analog or digital originals. 0 1997 Elsevier Science B.V. R&sum& La substitution d'images analogiques par des reprCsentations numCriques donne accts g des mCthodes de stockage et de transmission numkriques, et permet l'utilisation d'une grande variCtC de techniques de traitement d'images, incluant le rehaussement, les tests de dCpistage assist6 ordinateur et le diagnostic. La compression avec pertes peut encore amCliorer l'efficacitk de la transmission et du stockage, et peut faciliter le traitement ultCrieur des images. La numirisation et la compression avec pertes alttrant toutes deux une image par rapport $ sa forme traditionnelle, il devient important de montrer qu'une telle alttration amkliore, ou du moins ne rtduit pas, I'utilitC de l'image dans un screening ou une application de diagnostic. Une approche pour dt?montrer d'une man&e quantifiable qu'un mode d'image specifique est au moins Cgal 5 un autre est l'exptrimentation clinique simulant la pratique ordinaire jointe $ une analyse statistique adapt&e. Dans cet article, nous dttcrivons un protocole g&n&al pour effectuer une telle vCrification et prtsentons les rCsultats prCliminaires d'une expkrience faite pour montrer que des mamogrammes numCrisis g 12 bpp et comprimts avec pertes $ 0.15 bpp d l'aide d'une technique de codage par ondelettes incluses ne presentent pas de diffkrences significatives par rapport aux versions originales analogique ou numCrique. 0 1997 Elsevier Science B.V.

- by A. Aiyer
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- Engineering, Technology, Image Processing, Signal Processing
- by Jerome Antoni
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- Signal Processing, Structural Acoustics

Recent trends in clinical and telemedicine applications highly demand automation in (electrocardiogram) ECG signal processing and heart beat classification. A real-time patientadaptive cardiac profiling scheme using repetition detection is proposed in this paper. We introduce a novel local ECG beat classifier to profile each patient's normal cardiac behavior. As ECG morphologies vary from person to person, and even for each person, it can vary depending on the person's physical condition, having such profile is essential for various diagnosis (e.g. arrhythmia) purposes, and can successfully raise an early warning flag for the abnormal cardiac behavior of any individual. Experimental results show that our technique follows the MIT/BIH arrhythmia database annotations with high accuracy.

- by Miad Faezipour
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- Signal Processing, Support Vector Machines, Morphology, Databases

Cet article présente un nouveau procédé de correction auditive utilisant un modèle paramétrique du signal de parole. Ce procédé permet, en plus des opérations classiques de compression et d'amplification, d'effectuer des transformations élaborées (modification du rythme temporel, modification de l'enveloppe spectrale, etc.) tout en conservant la structure naturelle du signal de parole. Les premiers résultats d'une série de tests cliniques montrent que ce procédé est bien accueilli par les personnes atteintes de surdités intermédiaires (moyennes ou sévères) et pour lesquelles il n'existe pas véritablement de traitement prothétique adapté.

- by Alain Goye
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- Signal Processing, Hearing Impaired, Boolean Satisfiability, Hearing Aid

Oral administration is the most convenient route for drug therapy. The knowledge of the gastrointestinal transit and specific site for drug delivery is a prerequisite for development of dosage forms. The aim of this work was to demonstrate that is possible to monitor the disintegration process of film-coated magnetic tablets by multi-sensor alternate current Biosusceptometry (ACB) in vivo and in vitro. This method is based on the recording of signals produced by the magnetic tablet using a seven sensors array and signal-processing techniques. The disintegration was confirmed by signals analysis in healthy human volunteers' measurements and in vitro experiments. Results showed that ACB is efficient to characterize the disintegration of dosage forms in the stomach, being a research tool for the development of new pharmaceutical dosage forms. q

- by Jose Miranda
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- Signal Processing, Drug delivery, Signal Analysis, Drug Delivery Systems

The a priori signal-to-noise ratio (SNR) plays an important role in many speech enhancement algorithms. In this paper we present a data-driven approach to a priori SNR estimation. It may be used with a wide range of speech enhancement techniques, such as, e.g., the minimum mean square error (MMSE) (log) spectral amplitude estimator, the super Gaussian joint maximum a posteriori (JMAP) estimator, or the Wiener filter. The proposed SNR estimator employs two trained artificial neural networks, one for speech presence, one for speech absence. The classical decision-directed a priori SNR estimator by Ephraim and Malah is broken down into its two additive components, which now represent the two input signals to the neural networks. Both output nodes are combined to represent the new a priori SNR estimate. As an alternative to the neural networks, also simple lookup tables are investigated. Employment of these data-driven nonlinear a priori SNR estimators reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.

- by Suhadi Suhadi
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- Engineering, Signal Processing, Neural Network, Algorithm

Pour la plupart des systèmes mécaniques, les vibrations peuvent être considérées comme nuisibles. Cet article présente une méthode de compensation active des vibrations d'un alternateur synchrone. Son principe est le suivant : la machine est dotée au stator d'enroulements auxiliaires dits de compensation. Alimentés par un courant, ils engendrent une vibration supplémentaire, imposée destructrice vis-à-vis des vibrations naturelles de la machine. Les statistiques d'ordre supérieur (bicohérence) permettent de valider le modèle non linéaire complet du transfert vibration/commande, bien que le spectre suffise pour valider son approximation. Une méthode de compensation feedforward est ensuite élaborée puis simulée. Celle-ci utilise l'information à priori dont nous disposons sur le transfert, c'est à dire la forme de la non linéarité. Des taux de réduction de 80% de la puissance des vibrations ont été atteints en simulation.

- by Christine Serviere
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- Signal Processing, Modeling, A Priori Knowledge, Vibration Control

In this paper, we present two new articulated motion analysis and object tracking approaches: the decentralized articulated object tracking method and the hierarchical articulated object tracking method. The first approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking. Instead, we introduce a decentralized scheme and model the interpart interaction within an innovative Bayesian framework. Specifically, we estimate the interaction density by an efficient decomposed interpart interaction model. To handle severe self-occlusions, we further extend the first approach by modeling high-level interunit interaction and develop the second algorithm within a consistent hierarchical framework. Preliminary experimental results have demonstrated the superior performance of the proposed approaches on real-world videos in both robustness and speed compared with other articulated object tracking methods.

- by Dan Schonfeld
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- Cognitive Science, Algorithms, Artificial Intelligence, Signal Processing

A three-sensor element multipoint optical fibre sensor system capable of detecting varying ethanol concentrations in water for use in industrial process water systems is reported. The sensor system utilizes a U-bend configuration for each sensor element in order to maximize the sensitivity of each of the sensing regions along the optical fibre cable. The sensor system is interrogated using a technique known as optical time domain reflectometry, as this method is capable of detecting attenuation over distance. Analysis of the data arising from the sensor system is performed using artificial neural network pattern recognition techniques, coupled with Fourier-transform-based signal processing. The signal processing techniques are applied to the obtained sensor system data, prior to the artificial neural network analysis, with the aim of reducing the computational resources required by the implemented artificial neural network.

In this paper, we present an edge detection and characterization method based on wavelet transform. This method relies on a modelization of contours as smoothed singularities of three particular types (transition, peak and line). Using the wavelet transform modulus maxima lines of the edge models, position and descriptive parameters of each edge point can be inferred. Indeed, on the one hand, the proposed algorithm allows to detect and locate edges at a locally adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure both its amplitude and smoothness degree. The latter parameters represent, respectively, the contrast and the blur level of the edge point. Evaluation of the method is performed on both synthetic and real images. Synthetic data are used to investigate the influence of different factors and the sensitivity to noise, whereas real images allow to highlight the performance and interests of the method. In particular, we point out that the measured smoothness degree provides a cue to recover depth from defocused images or a cue to diffusion measurements in images of cloud structures. Moreover, from an indoor scene, we demonstrate the relevance of type identification for segmentation purposes. r

- by Cécile Barat
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- Engineering, Technology, Signal Processing, Edge Detection