Search

Your search keyword '"FAUST, OLIVER"' showing total 357 results
Start Over Author "FAUST, OLIVER"
357 results on '"FAUST, OLIVER"'

28. A self-reconfigurable system for software defined radio (SRS)

Author

Faust, Oliver

Subjects
621.3845
Abstract

This document introduces a Self-Reconfigurable system for Software defined radio (SR.S). The SRS is a modular processing platform which extends an ordinary PC system with Software Defined Radio capabilities. It consists of several daughter cards and one main board. The daughter cards connect the main board to external entities such as an analogue front end and the PC system. The main board hosts a single processor which executes signal processing algorithms and interfaces to external entities. The PC system controls which algorithms the processor executes and if necessary it initiates an algorithm change during runtime. The SRS system uses a reconfigurable architecture for the processor on the main board. This architecture provides a mechanism known as runtime reconfiguration to change the functionality or parts of the functionality of the processor. The PC system communicates with one part of the reconfigurable processor. This part reconfigures the signal processing part of the processor if the PC1 system initiates an algorithm change. I call this mechanism partial self-reconfiguration. Several components within the reconfigurable processor abstract the interfacing and signal processing tasks. The processor executes these components in parallel. I used a design methodology based on formal methods to design this component network. To be specific. I employed a model checker to validate a Communicating Sequential Processes (CSP) model of the component network. In a second step this CSP model was translated into hardware description code. The reconfigurable processor accelerates the baseband algorithms of digital communication standards. I studied the baseband algorithms which define the Orthogonal Frequency Division Multiplex (OFDM) modulation scheme. These algorithms have specific processing requirements. The specific-processor for the main board was selected based on the processing requirements of these algorithms. The SRS processing platform copes with the increasing algorithm complexity, higher demands for energy efficiency and flexibility. The modular system design provides a great deal of connection flexibility. This actually extends the idea of Software Defined Radio to wire or bus standards, i.e. software defines the bus standard. At the same time, formal methods ensure that the design, which defines the functionality of the reconfigurable processor, is reliable. Moreover, formal methods describe architecture and functionality of the system in a very compact manner. This provides the opportunity to document a hardware system completely. The OFDM algorithms provide a. deep insight into modern digital algorithms and how to accelerate these algorithms in a parallel architecture. This insight is necessary to draw up the specification for the system design.  The functionality of the reconfigurable processor was created with a design methodology based on formal methods. Compared with standard design methodologies the incorporation of formal methods speeds up the design process and the results are more reliable. The SRS system executes digital communication algorithms. Time and frequency synchronisation algorithms are amongst the most demanding, because they require realtime processing and they are very complex. The OFDM time and frequency synchronisation algorithms were improved with a technique called multi-rate signal processing. In a nutshell, multi-rate signal processing allows increased the time resolution and therefore it is possible to track a signal with finer steps.

Published
2005

29. Mental performance classification using fused multilevel feature generation with EEG signals.

Author

Aydemir, Emrah, Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta, Chakraborty, Subrata, Faust, Oliver, Arunkumar, N., Kaysi, Feyzi, and Acharya, U. Rajendra

Subjects
MENTAL arithmetic, PLURALITY voting, ELECTROENCEPHALOGRAPHY, K-nearest neighbor classification, BRAIN-computer interfaces
Abstract

Mental performance classification is a critical issue for brain-computer interfaces. Accurate and reliable classification of good or bad mental performance gives important clues for the preliminary diagnosis of some diseases and mental stress. In this work, we put forward an objective artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks. The proposed model consists of: (i) multilevel feature extraction based on statistical and texture analysis methods, (ii) feature ranking and selection with a Chi2 method, (iii) classification, and (iv) weightless majority voting classifier. The novelty of the presented model comes from multilevel fused feature generation. The presented model was developed using 20 channel electroencephalography data from 36 subjects. The signals were captured while the subjects were performing mental arithmetic tasks. The individual datasets were labeled as either good or bad, based on the task results. We have obtained an accuracy of 96.77% using O2 channel with a k-nearest neighbor classifier and reached 100.0% accuracy with the majority voting classifier. Our results indicate that it is possible to determine mental performance with artificial intelligence. That might be a steppingstone to establish objective measures for the clarity of thought during a wide range of mental tasks. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

30. Genetic Algorithms

Author

Klein, Robert, Faust, Oliver, Stadtler, Hartmut, editor, Kilger, Christoph, editor, and Meyr, Herbert, editor

Published
2015
Full Text
View/download PDF

48. Fusion of B‐mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma

Author

Pham, The‐Hanh, Faust, Oliver, Koh, Joel En Wei, Ciaccio, Edward J., Barua, Prabal D., Omar, Norlia, Ng, Wei Lin, Ab Mumin, Nazimah, Rahmat, Kartini, and Acharya, U. Rajendra

Subjects
Computational Theory and Mathematics, Artificial Intelligence, Control and Systems Engineering, Theoretical Computer Science
Abstract

In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p‐value, which was established with Student's t‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results