The correct identification of individuals through different biometric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative measures have been proposed during the last two decades: electrocardiogram (ECG) and electroencephalogram (EEG) signals, iris or facial recognition, conductual traits, etc. Several works have shown that ECG-based recognition is a feasible alternative, either for stand-alone or multi-biometric recognition systems. In this paper, we propose a novel framework for ECG-based biometric identification, consisting of a simple and robust feature extraction approach and a clustering-based feature reduction method, that enables for an efficient and scalable biometric identification. The proposed feature reduction approach is a two phase method: it uses a clustering algorithm to group features according to their similarities first, and then clusters are represented in terms of a prototype vector and associated to the available subjects. On its side, the proposed time-domain feature extraction method is a semi-fiducial procedure, where the well-known Pan–Tompkins algorithm is first used to detect the R wave peaks of the QRS complexes, and then fixed-width time segments are selected for further dimensionality reduction and feature extraction. The resulting combined methods are efficient, robust, scalable and attain excellent results (with up-to 98.6% sensitivity) on all the subjects of the Physikalisch-Technische Bundesanstalt (PTB) database, regardless of their pathological or healthy status. Additionally, we also show how the existing Auto Correlation/Discrete Cosine Transform (AC/DCT)-based non-fiducial feature extraction method can be integrated within our framework, allowing us to attain up to 90.6% sensitivity on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Since this database is much noisier and has a much lower sampling rate (360 Hz instead of 1000 Hz), we claim that this is a very good result.

The electrocardiogram (ECG) is the main biomedical signal used to diagnose and monitor cardiac pathologies. A typical ECG is composed of quasi-periodic activations (the QRS complexes, and the P and T waves) and periods of inactivity, plus noise and interferences. The sparse nature of the ECG has lead to the development of many compressed sensing (CS) and sparsity-aware ECG signal processing algorithms. In order to attain a good performance, these methods require appropriate dictionaries, and several on-line dictionary construction approaches have been devised. However, all of them require a substantial computational cost and the derived dictionaries are composed of atoms which may not be representative of real-world signals. In this work, we describe an efficient method for off-line construction of an overcomplete and multi-scale dictionary using a clustering-based approach. The resulting dictionary, whose atoms are the most representative waveforms from the training set, is then used to obtain a sparse representation of the ECG signal. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.

Article Clocking strategies and scannable latches for low power appliacations Share on Authors: V. Zyuban IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NY IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NYView Profile , D. Meltzer Epson Research and Development, NY and IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NY Epson Research and Development, NY and IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NYView Profile Authors Info & Claims ISLPED '01: Proceedings of the 2001 international symposium on Low power electronics and designAugust 2001 Pages 346–351https://doi.org/10.1145/383082.383181Online:06 August 2001Publication History 5citation318DownloadsMetricsTotal Citations5Total Downloads318Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper presents a design methodology emphasizing early and quick timing closure for high frequency microprocessor designs. This methodology was used to design a Gigahertz class PowerPC microprocessor with 19 million transistors. Characteristics of “Timing Closure by Design are 1) logic partitioned on timing boundaries, 2) predictable control structures (PLAs), 3) static interfaces for dynamic circuits, 4) low skew clock distribution, 5) deterministic method of macro placement, 6) simplified timing analysis, and 7) refinement method of chip integration with early timing analysis.

This paper describes the design methodology used to build an experimental 1.0 GigaHertz PowerPC integer microprocessor at IBM's Austin Research Laboratory. The high frequency requirements dictated the chip composition to be almost entirely custom macros using dynamic circuit techniques. The methodology presented will cover design and verification tools as well as circuit constraints and microarchitecture philosophy. The microarchitecture, circuits and tools were defined by the high frequency requirements of the processor as well as the aggressive design schedule and size of the design team.

A simple method is presented which defines Walsh functions in terms of products of Rademacher functions, but which preserves the ordering of the Walsh functions necessary to retain the notion of increasing number of zero crossings, or sequency.