A High Speed Networked Signal Processing Platform for Multi-element Radio Telescopes

A new architecture is presented for a Networked Signal Processing System (NSPS) suitable for handling the real-time signal processing of multi-element radio telescopes. In this system, a multi-element radio telescope is viewed as an application of a multi-sensor, data fusion problem which can be decomposed into a general set of computing and network components for which a practical and scalable architecture is enabled by current technology. The need for such a system arose in the context of an ongoing program for reconfiguring the Ooty Radio Telescope (ORT) as a programmable 264-element array, which will enable several new observing capabilities for large scale surveys on this mature telescope. For this application, it is necessary to manage, route and combine large volumes of data whose real-time collation requires large I/O bandwidths to be sustained. Since these are general requirements of many multi-sensor fusion applications, we first describe the basic architecture of the NSPS in terms of a Fusion Tree before elaborating on its application for the ORT. The paper addresses issues relating to high speed distributed data acquisition, Field Programmable Gate Array (FPGA) based peer-to-peer networks supporting significant on-the fly processing while routing, and providing a last mile interface to a typical commodity network like Gigabit Ethernet. The system is fundamentally a pair of two co-operative networks, among which one is part of a commodity high performance computer cluster and the other is based on Commercial-Off The-Shelf (COTS) technology with support from software/firmware components in the public domain.Comment: 19 pages, 4 eps figures, To be published in Experimental Astronomy (Springer

Signal acquisition challenges in mobile systems

In recent decades, the advent of mobile computing has changed human lives by providing information that was not available in the past. The mobile computing platform opens a new door to the connected world in which various forms of hand-held and wearable systems are ubiquitous. A single mobile device plays multiple roles and shapes human lives towards a better future. In these systems, sensor-based data acquisition plays an essential role in generating and providing useful information. The increased number of sensors is embedded in a single device in order to process various signal modalities. In practice, more than 30 data converters are required in designing a mobile system in which the data-converting blocks become among the most power-hungry components in battery-operated systems. Due to the increased variety of sensors, mobile systems are meant to face several obstacles. For example, the increased number of sensors increase system power consumption during the system operation. The increased power consumption directly affects operation time because mobile systems are powered by a limited energy source. Moreover, an increased amount of information also gives rise to bandwidth problems in communication due to the increased volume of data transmission. Also, this system design requires a larger area in a silicon die so that multiple signal paths can be placed without cross-channel interference. Therefore, the system design has presented a challenge in terms of trying to resolve the design constraints such as power consumption, bandwidth usage, storage space, and design complexity issues. To overcome these obstacles, in this dissertation, efficient data acquisition and processing methods are investigated. Specifically, this thesis considers the problems of energy-efficient sampling and binary event detection. This dissertation begins by presenting a new signal sampling scheme that enables higher precision signal conversion in compressed-sensing-based signal acquisition. The proposed scheme is based on the popular successive approximation register and employs a modified compressive sensing technique to increase the resolution of successive-approximation-register (SAR) analog-to-digital converter (ADC) architecture. Circuit-level architecture is discussed to implement the proposed scheme using the SAR ADC architecture. A non-uniform quantization scheme is proposed and it improves data quality after data acquisition. The proposed scheme is expected to be used for medium- or high- frequency data conversion. Secondly, the possibility of using fewer ADCs than channels is studied by leveraging sparse-signal representation and blind-source-separation (BSS) techniques. In particular, this dissertation examines the problem of using a single ADC or quantizer system for digitizing multi-channel inputs. Mixing and de-mixing strategies are extensively studied for sampling frequency-sparse signals and the proposed multi-channel architecture can be easily implemented using today's analog/mixed-signal circuits. The third part of this dissertation investigates a binary hypothesis testing problem. In mobile devices such as smartphones and tablet PCs, a major portion of energy is consumed in user interfaces (LCD display and touch input processing). For accurate detection and better user interface, energy-efficient sensing and detection schemes are necessary to manage multiple sensor inputs. A highly efficient detection scheme is presented that can detect binary events reliably with a fraction of the energy consumption required in the conventional energy detection.Electrical and Computer Engineerin

Spike Processing on an Embedded Multi-task Computer: Image Reconstruction

There is an emerging philosophy, called Neuro-informatics, contained in the Artificial Intelligence field, that aims to emulate how living beings do tasks such as taking a decision based on the interpretation of an image by emulating spiking neurons into VLSI designs and, therefore, trying to re-create the human brain at its highest level. Address-Event-Representation (AER) is a communication protocol that has embedded part of the processing. It is intended to transfer spikes between bioinspired chips. An AER based system may consist of a hierarchical structure with several chips that transmit spikes among them in real-time, while performing some processing. There are several AER tools to help to develop and test AER based systems. These tools require the use of a computer to allow the higher level processing of the event information, reaching very high bandwidth at the AER communication level. We propose the use of an embedded platform based on a multi-task operating system to allow both, the AER communication and processing without the requirement of either a laptop or a computer. In this paper, we present and study the performance of a new philosophy of a frame-grabber AER tool based on a multi-task environment. This embedded platform is based on the Intel XScale processor which is governed by an embedded GNU/Linux system. We have connected and programmed it for processing Address-Event information from a spiking generator.Ministerio de Educación y Ciencia TEC2006-11730-C03-0

Wavelet Signal Processing of Physiologic Waveforms

The prime objective of this piece of work is to devise novel techniques for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, ECG beat classification is achieved by using feature extracting techniques to make a neural network (NN) system more effective. The feature extraction technique used is Wavelet Signal Processing. Coefficients from the discrete wavelet transform were used to represent the ECG diagnostic information and features were extracted using the coefficients and were normalised. These feature sets were then used in the classifier i.e. a simple feed forward back propagation neural network (FFBNN). This paper presents a detail study of the classification accuracy of ECG signal by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-Layered perceptron (MLP) with back propagation training algorithm. The ECG signals have been taken from MIT-BIH ECG database, and are used in training to classify 3 different Arrhythmias out of ten arrhythmias. These are normal sinus rhythm, paced beat, left bundle branch block. Before testing, the proposed structures are trained by back propagation algorithm. The results show that the wavelet decomposition method is very effective and efficient for fast computation of ECG signal analysis in conjunction with the classifier

Embedding Multi-Task Address-Event- Representation Computation

Address-Event-Representation, AER, is a communication protocol that is intended to transfer neuronal spikes between bioinspired chips. There are several AER tools to help to develop and test AER based systems, which may consist of a hierarchical structure with several chips that transmit spikes among them in real-time, while performing some processing. Although these tools reach very high bandwidth at the AER communication level, they require the use of a personal computer to allow the higher level processing of the event information. We propose the use of an embedded platform based on a multi-task operating system to allow both, the AER communication and processing without the requirement of either a laptop or a computer. In this paper, we present and study the performance of an embedded multi-task AER tool, connecting and programming it for processing Address-Event information from a spiking generator.Ministerio de Ciencia e Innovación TEC2006-11730-C03-0

Flexible data input layer architecture (FDILA) for quick-response decision making tools in volatile manufacturing systems

This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments’ Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant’s Key Performance Factors

Multiuser Detection and Channel Estimation for Multibeam Satellite Communications

In this paper, iterative multi-user detection techniques for multi-beam communications are presented. The solutions are based on a successive interference cancellation architecture and a channel decoding to treat the co-channel interference. Beams forming and channels coefficients are estimated and updated iteratively. A developed technique of signals combining allows power improvement of the useful received signal; and then reduction of the bit error rates with low signal to noise ratios. The approach is applied to a synchronous multi-beam satellite link under an additive white Gaussian channel. Evaluation of the techniques is done with computer simulations, where a noised and multi-access environment is considered. The simulations results show the good performance of the proposed solutions.Comment: 12 page