ADVANCES IN CIRCUITS AND SYSTEMS 6, November 2005

A quarterly news service of the IEEE Circuits and Systems Society

Editor: Martin Hasler VP Technical Activities

 

CONTENTS

 

1. New Advances in Multi-Directional Multi-Scroll Chaos Generation

2. Brain Computer Interface 1

3. Brain Computer Interface 2

4. Topology Control in  Wireless Ad Hoc and Sensor Networks

5. Modeling Human Motion (2004 Best paper award of the IEEE Transactions on Circuits and Systems for Video Technology)

 

 

 

1. New Advances in Multi-Directional Multi-Scroll Chaos Generation

 

Description by Wallace K. S. Tang: Recently, generating complex multi-scroll chaotic attractors via simple electronic circuits has seen rapid development. Ref. [1] introduced an approach based on hysteresis circuit series for creating one-directional n-scroll, two-directional planar grid scroll, and three-directional spatial grid scroll attractors with rigorous mathematical verifications. Ref. [2] initiated another approach based on saturated circuit series for the same tasks, namely, generating one-directional n-scroll, two-directional planar grid scroll, and three-directional spatial grid scroll attractors,also with rigorously mathematical proofs.

 

References:

[1] J. Lu, F. L., Han, X. H. Yu and G. Chen,  "Generating 3-D multi-scroll chaotic attractors: A hysteresis series switching method," Automatica, vol. 40, no. 10, pp. 1677-1687, Oct. 2004.

[2] J. Lu, G. Chen, X. H. Yu and H. Leung,  "Design and analysis of multi-scroll chaotic attractors from saturated function series," IEEE Transactions on Circuits and Systems I, vol. 51, no. 12, pp. 2476-2490, Dec. 2004.

 

Communicated by the Technical Committee on Nonlinear Circuits and Systems

 

 

 

2. Brain Computer Interface 1

 

Description by Chin-Teng Lin: Neural spike trains in rats' motor cortices were recorded for real-time control tasks. The rat was placed in a conditioning chamber to decide which one of two paddles should be activated to shift the cue light to the center. Using one to six principal components with a Bayes classifier achieved classification accuracy comparable to a more sophisticated support vector classifier.

 

Reference: Jing Hu  Si, J. Olson, B.P. Jiping He, “Feature detection in motor cortical spikes by principal component analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 256 – 262, Sep. 2005.

 

Communicated by the Technical Committee on Neural Systems and Applications

 

 

 

3. Brain Computer Interface 2

 

Descriptions by Chin-Teng Lin: Brain-machine interface models are used for interpreting the neural activity generated in motor tasks. A nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. From the model roles are attributed to the sampled motor, premotor, and parietal cortices in generating hand movements and a temporal sensitivity measure is derived.

 

Reference: J.C. Sanchez, D. Erdogmus, M.A.L. Nicolelis, J. Wessberg, J. C. Principe, “Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 213 – 219, June 2005.

 

Communicated by the Technical Committee on Neural Systems and Applications

 

 

 

4. Topology Control in  Wireless Ad Hoc and Sensor Networks

 

Description by Krishnaiya Thulasiraman: Reducing energy consumption (aimed at extending the network lifetime) and radio interference (aimed at increasing the network traffic carrying capacity) are important issues in wireless ad hoc and sensor networks. Topology control aims to control the graphs underlying the networks so that some global graph property such as connectivity is maintained while reducing energy consumption and radio interference. Overview article.

 

Reference:  Paolo Santi, " Topology Control in Wireless Ad Hoc and Sensor Networks", ACM Computing Surveys, Vol. 37, June 2005, pp.164-194.

 

Communicated by the Technical Committee on Graph Theory and Computing

 

 

 

5. Modeling Human Motion (2004 Best paper award of the IEEE Transactions on Circuits and Systems for Video Technology)

 

Description by Ling Guan: This paper introduces a unique paradigm, the alphabet of dynemes – the smallest contrastive dynamic units of human movement and presents a novel notation system for computational modeling and recognition of general human movement from monocular video sequences. The powerful anthropometric characteristics are utilized as the primary features for the study of biometrics and recognition of human skills in video images.

 

Reference: R.D. Green, Ling Guan, Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, Feb. 2004, pp. 179-190.

 

Communicated by the Technical Committee on Visual Signal Processing and Communications