Domain Signal Processing Research Articles

Particles Mass Flow Rate and Concentration Measurement using Electrostatic Sensor

Abstract In many industries where flow parameters measurement is essential to control manufacturing process, the use of a reliable, cost effective and high accuracy instrument is an important issue. Appropriate measurement method and design leads to improvement of pneumatic conveyors operation and process efficiency. This paper present an instrumentation design based on passive charge detection using a single electrostatic sensor. Two different sensor electrodes are applied to show the flexibility of electrostatic sensor application. A time domain signal processing algorithm is developed to measurement of mass flow rate and concentration profile from acquired electrical charge signal. The findings is led to a low cost and high accuracy design, the experimental test results of the design shows less than ±5% error between measured parameters and reference reading acquired from the manual weighing.

Analysis of SystemC actor networks for efficient synthesis

Applications in the signal processing domain are often modeled by dataflow graphs. Due to heterogeneous complexity requirements, these graphs contain both dynamic and static dataflow actors. In previous work, we presented a generalized clustering approach for these heterogeneous dataflow graphs in the presence of unbounded buffers. This clustering approach allows the application of static scheduling methodologies for static parts of an application during embedded software generation for multiprocessor systems. It systematically exploits the predictability and efficiency of the static dataflow model to obtain latency and throughput improvements. In this article, we present a generalization of this clustering technique to dataflow graphs with bounded buffers, therefore enabling synthesis for embedded systems without dynamic memory allocation. Furthermore, a case study is given to demonstrate the performance benefits of the approach.

Non intrusive diagnosis of polymer electrolyte fuel cells by wavelet packet transform

Fuel cell is a promising technology for both automotive and stationary applications. However, its reliability and its lifetime remain major hurdles to its wide access to these markets. It is therefore necessary to develop reliable diagnosis tools adapted to these two applications’ requirements. More particularly, online and real time tools for diagnosis will permit an early faults diagnosis and therefore an increase of the system reliability and performance. Most of the existing fault diagnosis methodologies in fuel cells require the knowledge of numerous parameters that may lead to a special inner parameter monitoring setup, which is difficult, even impossible to obtain, considering constraints like fuel cell stacks’ geometry. Moreover, considering the final fuel cell stack end-uses, for instance in transportation applications in which the “on-board” instrumentation has to be minimized, a model using a minimal number of parameter is highly desirable. In this paper, a simple and low-cost flooding diagnosis method applied to a PEFC (Polymer Electrolyte Fuel Cell) is described. This method only uses the stack voltage and can be adapted to a large set of fuel cell configurations and applications. Coming from the signal-processing domain, the diagnosis consists in a signal feature extraction by multiscale decomposition using discrete wavelet transform, followed by fault identification and classification. Results obtained in this work showed that the wavelet analysis method allows the identification of the flooding based on the patterns obtained from the wavelet packet coefficients. The application of wavelet theory to fuel cell diagnosis is innovative and very promising and the experimental results obtained in this study proved its feasibility and reliability to classify correctly PEFC experimental states into flooded and non-flooded state of health.

Exploration and implementation of a highly efficient processor element for multimedia and signal processing domains

The exploration and design process of highly efficient processor element for multimedia and signal processing domains is presented in this study. With the introduction of synchronous data-transfer architecture for high-performance embedded applications, the effectively exploring the exponential-size architectural design spaces by detailed simulation is intractable. The authors attack this via an automated approach. At first, its cost model is built to achieve fast and accurate estimation with the characteristic of scalability. Then, the hierarchical design space exploration methodology involving heuristic-based local process and analytical global optimisation step is proposed to achieve the approximate optimum with short time-to-market. For target domains, our proposed method arrives at better optimised results within only 25 h when compared with other methods. A System on Chip (SoC) involving the optimised processor element has been implemented in 0.13 µm complementary metal oxide semiconductor (CMOS) process and the experimental results show that our processor element outperforms TMS320C64 series and does the obvious acceleration to multimedia applications in SoC system.

Adaptive feature extraction using sparse coding for machinery fault diagnosis

In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.

Compensating I–Q Imperfections in Hybrid RF/Digital Predistortion With an Adapted Lookup Table Implemented in an FPGA

The performance of hybrid RF/digital predistortion (RF-DPD) is limited, due to in-phase (I) and quadrature-phase (Q) imperfection in its key component, the RF vector multiplier, and the associated circuitry. These imperfections cause errors, in terms of implemented gain and phase of the predistortion function. This brief presents the methodology of implementing hybrid RF-DPD with a lookup table (LUT) adapted to compensate for hardware related I-Q imperfections of the RF vector multiplier within the digital signal processing domain. This modified LUT will accurately compensate for I-Q imperfection, without needing a precise tuning of the control voltages at the pins of the RF vector multiplier. This brief also presents the test setup for characterizing the RF-DPD system to obtain the I-Q imperfections within it and utilizes this information to modify the LUT to compensate for these imperfections. To verify the capability of the modified LUT in compensating for the I-Q imperfections, an experimental validation is carried out by linearizing a class-AB base station power amplifier using the hybrid RF-DPD system developed with an Altera Stratix field-programmable gate array (FPGA) evaluation board. In addition to the 12-dB adjacent-channel leakage ratio obtained using static RF-DPD, an improvement of 2.5 dB is achieved using the proposed I-Q compensation technique.

Eulerian laser Doppler vibrometry: Online blade damage identification on a multi-blade test rotor

Laser Doppler vibrometry enables the telemetry-free measurement of online turbomachinery blade vibration. Specifically, the Eulerian or fixed reference frame implementation of laser vibrometry provides a practical solution to the condition monitoring of rotating blades. The short data samples that are characteristic of this measurement approach do however negate the use of traditional frequency domain signal processing techniques. It is therefore necessary to employ techniques such as time domain analysis and non-harmonic Fourier analysis to obtain useful information from the blade vibration signatures. The latter analysis technique allows the calculation of phase angle trends which can be used as indicators of blade health deterioration, as has been shown in previous work for a single-blade rotor. This article presents the results from tests conducted on a five-blade axial-flow test rotor at different rotor speeds and measurement positions. With the aid of artificial neural networks, it is demonstrated that the parameters obtained from non-harmonic Fourier analysis and time domain signal processing on Eulerian laser Doppler vibrometry signals can successfully be used to identify and quantify blade damage from among healthy blades. It is also shown that the natural frequencies of individual blades can be approximated from the Eulerian signatures recorded during rotor run-up and run-down.

Signal processing on platforms with multiple cores: Part 1 - Overview and methodologies [From the Guest Editors

Multicore processors are now prevalent in all major domains of signal processing. Many laptop and desktop computers today are shipped with dualcore and even quad-core processors. The number of cores is even higher for the Sony PlayStation 3, which is equipped with an 8-core IBM CELL Broadband Engine processor, Nvidia GeForce 9800 GX2, which has 256 stream processors, and SUN UltraSPARC T1/T2 processor, which has 8 cores. Technology predictions indicate that this trend will continue and that the number of cores per processor can easily double around every two or three years.

EFFICIENT STATISTICAL MODELING OF WAVELET COEFFICIENTS FOR IMAGE DENOISING

Statistical modeling of wavelet coefficients is a critical issue in wavelet domain signal processing. By analyzing the defects of other existing methods, and exploiting the local dependency of wavelet coefficients, an efficient statistical model is proposed. Improved variance estimation of the local wavelet coefficients can be obtained using the new model. Then we apply an approximate minimum mean squared error (MMSE) estimation procedure to restore the wavelet image coefficients. The modeling process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).

A feature analysis for dimension reduction based on a data generation model with class factors and environment factors

Currently, high-dimensional data such as image data is widely used in the domain of pattern classification and signal processing. When using high-dimensional data, feature analysis methods such as PCA (principal component analysis) and LDA (linear discriminant analysis) are usually required in order to reduce memory usage or computational complexity as well as to increase classification performance. We propose a feature analysis method for dimension reduction based on a data generation model that is composed of two types of factors: class factors and environment factors. The class factors, which are prototypes of the classes, contain important information required for discriminating between various classes. The environment factors, which represent distortions of the class prototypes, need to be diminished for obtaining high class separability. Using the data generation model, we aimed to exclude environment factors and extract low-dimensional class factors from the original data. By performing computational experiments on artificial data sets and real facial data sets, we confirmed that the proposed method can efficiently extract low-dimensional features required for classification and has a better performance than the conventional methods.

Array-OL with delays, a domain specific specification language for multidimensional intensive signal processing

Intensive signal processing applications appear in many application domains such as video processing or detection systems. These applications handle multidimensional data structures (mainly arrays) to deal with the various dimensions of the data (space, time, frequency). A specification language allowing the direct manipulation of these different dimensions with a high level of abstraction is a key to handling the complexity of these applications and to benefit from their massive potential parallelism. The Array-OL specification language is designed to do just that. We introduce here an extension of Array-OL to deal with states or delays by the way of uniform inter-repetition dependences. We show that this specification language is able to express the main patterns of computation of the intensive signal processing domain.

A Review of Slow- and Fast-Light Based on Stimulated Brillouin Scattering in Future Optical Communication Networks

Slow- and Fast-Light has been investigated during the last years as a powerful tool to reduce and increase the group velocity of light pulses by light. It has enjoyed much recent interest because of the practical applications for telecommunication and information systems. Among these are optical signal processing, radio frequency-photonics, nonlinear optics and time domain spectroscopy. Furthermore, it can be seen as a key technology for optical delay lines, buffers, equalizers and synchronizers in packet switched networks. For a realization, there are different methods and material systems possible. Among them the nonlinear effect of stimulated Brillouin scattering (SBS) is of special interest. This article gives an overview about the fundamentals and some experimental results of the Slow- and Fast-Light effect based on the SBS in optical fibers.

Design Space Exploration for an ASIP/Co-Processor Architecture used in GNSS Receivers

The development of algorithms from the communication and the signal processing domain often requires floating point arithmetic as well as support for trigonometric functions. While state-of-the-art digital signal processors offer hardware support for this kind of functions, embedded processors for mobile applications emulate floating point arithmetic and trigonometric functions in software. This emulation increases the number of required processing cycles compared to a dedicated hardware implementation. During development and implementation of ASIPs another problem with the emulation of these instructions arises. As the emulation has to be very flexible (e.g. variable operand bit width) emulation is carried out generally in a high level programming language leading to increased flexibility at the cost of reduced performance. This paper presents the design space exploration for an ASIP with an attached floating point co-processor in terms of performance and area and energy costs. To integrate the co-processor instructions, the ASIP's architecture description is modified and software development tools (e.g. assembler, linker and compiler) are adapted. Additionally, the ASIP's software libraries allow for a seamless integration of the co-processor instructions and enable ease of use for the application development. Power consumption and required silicon area can be decreased by choosing an application specific subset of supported co-processor functions and reduction of the co-processor's datapath bit width. The presented ASIP/co-processor architecture is functionally verified and area and power figures are derived for various configurations. A detailed analysis of the presented architecture used as a platform for GNSS receivers reveals promising configurations.

Orchestrating the execution of stream programs on multicore platforms

While multicore hardware has become ubiquitous, explicitly parallel programming models and compiler techniques for exploiting parallelism on these systems have noticeably lagged behind. Stream programming is one model that has wide applicability in the multimedia, graphics, and signal processing domains. Streaming models execute as a set of independent actors that explicitly communicate data through channels. This paper presents a compiler technique for planning and orchestrating the execution of streaming applications on multicore platforms. An integrated unfolding and partitioning step based on integer linear programming is presented that unfolds data parallel actors as needed and maximally packs actors onto cores. Next, the actors are assigned to pipeline stages in such a way that all communication is maximally overlapped with computation on the cores. To facilitate experimentation, a generalized code generation template for mapping the software pipeline onto the Cell architecture is presented. For a range of streaming applications, a geometric mean speedup of 14.7x is achieved on a 16-core Cell platform compared to a single core.

Graph spectral image smoothing using the heat kernel

A new method for smoothing both gray-scale and color images is presented that relies on the heat diffusion equation on a graph. We represent the image pixel lattice using a weighted undirected graph. The edge weights of the graph are determined by the Gaussian weighted distances between local neighboring windows. We then compute the associated Laplacian matrix (the degree matrix minus the adjacency matrix). Anisotropic diffusion across this weighted graph-structure with time is captured by the heat equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigensystem with time. Image smoothing is accomplished by convolving the heat kernel with the image, and its numerical implementation is realized by using the Krylov subspace technique. The method has the effect of smoothing within regions, but does not blur region boundaries. We also demonstrate the relationship between our method, standard diffusion-based PDEs, Fourier domain signal processing and spectral clustering. Experiments and comparisons on standard images illustrate the effectiveness of the method.

Automated Identification of Vehicles using Acoustic Signal Processing

Automated identification of unauthorised intruding vehicles approaching protected infrastructure is becoming increasingly important for security purposes. This three‐year project, which is now in its second year, aims to develop a real‐time acoustic vehicle type recognition system that will be predominantly composed of three main parts; acoustic signal pre‐processing, feature extraction, and decision making (or classification). The main study area covers various signal processing techniques in time, time‐frequency and potentially frequency domains with signal classification implemented using a range of artificial intelligence techniques such as artificial neural networks. So far the focus has been on time domain signal processing and neural network classification. Whilst the work is at an early stage, the time domain methods such as Time Domain Signal Coding (TDSC) and Co‐Occurrence Matrix combined with neural networks have already shown some promise. The presentation will introduce the project, describe methodologies involved as well as the results to date for the novel acoustic identification of categories of vehicles.

Parallel Memory Architecture for Application-Specific Instruction-Set Processors

Many of the current applications used in battery powered devices are from digital signal processing, telecommunication, and multimedia domains. These applications typically set high requirements for computational performance and often parallelism is the key solution to meet the performance requirements. In order to exploit the parallel processing units, memory should be able to feed the data path with data. This calls for a memory organization supporting parallel memory accesses. In this paper, a conflict resolving parallel data memory system for application-specific instruction-set processors is described. The memory structure is generic and reusable to support various application-specific designs. The proposed memory system does not employ any predefined access format signals for memory addressing. The proposed parallel memory system is attached to an application-specific instruction-set processor core and comparison on area, power, and critical path are shown. The experiments show that significant power savings can be obtained by exploiting the parallel memory system instead of multi-port memory.

Time–frequency feature representation using energy concentration: An overview of recent advances

Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time–frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time–frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time–frequency representations, and feature classifiers. The results of the literature review indicate that time–frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future.

Frequency domain processing techniques for pulse shape modulated ultra wideband systems

In this paper, two frequency domain signal processing techniques for pulse shape modulation (PSM) ultra wideband (UWB) systems are presented. Firstly, orthogonal detection of UWB PSM Hermite pulses in frequency domain is addressed. It is important because time domain detection by correlation-based receivers is severely degraded by many sources of distortion. Pulse- shape, the information conveying signal characteristic, is deformed by AWGN and shape-destructive addition of multiple paths from the propagation channel. Additionally, because of the short nature of UWB pulses, timing mismatches and synchronism degrade the performance of PSM UWB communication systems. In this paper, frequency domain orthogonality of the Hermite pulses is exploited to propose an alternative detection method, which makes possible efficient detection of PSM in dense multipath channel environments. Secondly, a ranging method employing the Cepstrum algorithm is proposed. This method is partly processed in the frequency domain and can be implemented without additional hard- ware complexity in the terminal.

The Design of an Allo-Optical Packet Switching Network

Recent advances in the all-optical signal processing domain report high-speed and nontrivial functionality directly implemented in the optical layer. These developments mean that the all- optical processing of packet headers has a future. In this article we address various important control plane issues that must be resolved when designing networks based on all-optical packet-switched nodes.