| 21. |
- Malki, Suleyman, et al.
(författare)
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Hand Veins Feature Extraction using DTCNNs
- 2006
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Ingår i: Proceedings SSoCC.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper we study the potential of Cellular Neural Networks implemented on a Field-Programmable Gate Array to handle the person identification based on hand veins in real time. With a minimal distance measure of 2 pixels for False Feature Elimination, it has a True Acceptance Rate of 65% and a False Rejection Rate of 5%. The performance rises drastically with increasing pixel distance and will therefore be camera sensitive.
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| 22. |
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| 23. |
- Malki, Suleyman, et al.
(författare)
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ISO/OSI compliant network-on-chip implementation for CNN applications
- 2005
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Ingår i: Proceedings of the SPIE - The International Society for Optical Engineering. - : SPIE. - 0277-786X .- 1996-756X. ; 5839:1, s. 341-352
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Konferensbidrag (refereegranskat)abstract
- The paper investigates the potential for a packet switching network for real-time image processing by a Cellular Neural Network (CNN) implemented on a Field-Programmable Gate-Array (FPGA). The implementation of a CNN requires several parameter restrictions with respect to the universal concept. For instance, the number representation and the cloning template are often confined to respectively 8 bits and a neighborhood of 1. It has been shown that optimal (i.e. minimal level) CNN architectures as derived from a morphological specification of the desired operation lead to arbitrarily large templates. A subsequent transformation step can turn this into a sequence of smaller templates for a specified hardware platform. The existence of a generic platform that can already handle the universal CNN architecture for prototyping and verification eliminates this need for technology-driven performance degradation. The proposed packet switcher consists of a physical layer where the CNN nodal function is performed, a data-link layer where the nodal data are maintained, a network layer with the packet receiver and sender and the actual switch as element of the transport layer. This ISO/OSI compliant level-wise structure monitors the network parameters and autonomously adjusts for the size of the neighborhood. It separates the broadcast of the network variables from the actual computation, allowing each to be executed at its own speed. The concept is tested on a re-design of the ILVA architecture and has been shown to handle arbitrary neighborhoods and precision at a comparable size and speed (1 node per BlockRAM / multiplier module @220 MHz clock)
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| 24. |
- Malki, Suleyman, et al.
(författare)
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It takes a winner to take his share
- 2003
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Ingår i: Proceedings ProRisc?03. - 9073461391 ; , s. 517-522
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Konferensbidrag (refereegranskat)abstract
- Novelty detection is based on the creation of a space with similarity metric. It is discussed that the design of a neural detector is a compromise between promptness, universatility, robustness and sensitivity. The feed-forward topology is chosen from three alternatives for its ability to design that compromise by applying structural redundancy. A generic FPGA implementation supports the use in adaptive intelligent systems.
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| 25. |
- Malki, Suleyman, et al.
(författare)
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Neural vision sensors for surface defect detection
- 2004
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Ingår i: 2004 IEEE International Joint Conference on Neural Networks. - 0780383591 ; , s. 3155-3160
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Konferensbidrag (refereegranskat)abstract
- Vision sensors are built from a camera and intelligent hardware and/or software. Steadily decreasing microelectronic costs have spawned a large number of vision sensory applications, such as surface defect detection. A constructive method for defect detection entails a mixture of mathematical and intelligent modules. Such a heterogeneous modular system can be realized in many ways. In this paper we discuss a packet-switched implementation on a macro-enriched field-programmable gate-array
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| 26. |
- Malki, Suleyman, et al.
(författare)
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On the packet-switched implementation of a discrete-time CNN
- 2004
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Ingår i: Proceedings of the Euromicro Symposium on Digital System Design. - 0769522033 ; , s. 234-241
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Konferensbidrag (refereegranskat)abstract
- Cellular neural networks are widely used with real-time image processing's applications. Such systems can be efficiently realized using macro enriched field-programmable gate-arrays. This paper explores the benefits of packet switching and discusses its advantages over a current design based on circuit switching. The implementation is performed using Xilinx Virtex-II Pro P30 and handles around 500 Mpixels per second using 128 parallel processing nodes
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| 27. |
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| 28. |
- Malki, Suleyman, et al.
(författare)
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Vein feature extraction using DT-CNNs
- 2006
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Ingår i: 10th International Workshop on Cellular Neural Networks and Their Applications, 2006. CNNA '06.. - 1424406404 ; , s. 307-312
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Konferensbidrag (refereegranskat)abstract
- Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Security systems based on fingerprints and retina patterns have been widely developed, but can be easily falsified. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper an existing feature extraction algorithm, that has been developed for fingerprint recognition, is adapted for vein recognition. The algorithm has been implemented as cellular neural network and realized on a field-programmable gate-array. The detection quality is comparable to the 99.45% reached earlier by direct image comparison, but suffers from the image resolution sensitivity of the false feature elimination
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| 29. |
- Malki, Suleyman, et al.
(författare)
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Veins feature extraction using DT-CNNS - art. no. 65900N
- 2007
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Ingår i: VLSI Circuits and Systems III. - : SPIE. - 1996-756X .- 0277-786X. ; 6590, s. 5900-5900
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Konferensbidrag (refereegranskat)abstract
- As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very vulnerable. One's fingerprints are accessible as soon as the person touches a surface, while a high resolution camera easily captures the retina pattern. Thus, both patterns can easily be "stolen" and forged. Beside, technical considerations decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling. On the other hand, vein patterns of either a palm of the hand or a single finger offer stable, unique and repeatable biometrics features. A fingerprint-based identification system using Cellular Neural Networks has already been proposed by Gao. His system covers all stages of a typical fingerprint verification procedure from Image Preprocessing to Feature Matching. This paper performs a critical review of the individual algorithmic steps. Notably, the operation of False Feature Elimination is applied only once instead of 3 times. Furthermore, the number of iterations is limited to I for all used templates. Hence, the computational need of the feedback contribution is removed. Consequently the computational effort is drastically reduced without a notable chance in quality. This allows a full integration of the detection mechanism. The system is prototyped on a Xilinx Virtex II Pro P30 FPGA.
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| 30. |
- Malki, Suleyman, et al.
(författare)
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Velocity measurement by a vision sensor
- 2006
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Ingår i: 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE Cat. No. 06EX1332). - 142440245X ; , s. 135-140
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Konferensbidrag (refereegranskat)abstract
- Vision sensors are of increasing interest for non-intrusive, remote measurements in industrial as well as consumer products. Their operation is based on intelligent techniques for image understanding, followed by robust quantification of the detected phenomenon. A well-known application area is movement detection and isolation. This paper discusses how movement can be detected and quantified by means of a single cellular neural network, implemented as a network-on-chip and realized on a field-programmable gate-array. The final prototype can isolate a moving object and measure its velocity at a speed of 250 frames per second
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