Ching-Kun Chen

Personalized information encryption using ECG signals with chaotic functions

The development of efficient data encryption to ensure high security of information transmission has long been a popular research subject. Because electrocardiogram (ECG) signals vary from person to person, and can be used as a new tool for biometric recognition. This study introduces an individual feature of ECG with chaotic Henon and logistic maps for personalized cryptography. This study also develops an encryption algorithm based on the chaos theory to generate initial keys for chaotic logistic and Henon maps. The proposed personalized encryption system uses a convenient handheld device to collect ECG signals from the user. High quality randomness in ECG signals results in a widely expanded key space, making it an ideal key generator for personalized data encryption. The experiments reported in this study demonstrate the use of this approach in encrypting texts and images, and applied of the proposed approach to secure communications.

A Chaotic Theorectical Approach to ECG-Based Identity Recognition [Application Notes]

Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control. Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc. Still, there are novel identification technologies based on the individuals biometric features under development [1-4].

Adaptive IPM-based lane filtering for night forward vehicle detection

This paper presents a new vision-based vehicle detection method for Forward Collision Warning System (FCWS) at nighttime. Also, lane detection is performed for assistance. To effectively extract the bright objects of interest, an essential image preprocessing including the tone mapping, contrast enhancement and adaptive binaryzation is applied in the nighttime road scenes. The characteristics of taillights in gray-level image are extracted by night vehicle detection method, and the resulted taillight candidates are verified by their corresponding red-component which results from R and B color channels. The taillight candidates to be performed with pairing algorithm are filtered by our proposed adaptive lane boundaries on the basis of Inverse Perspective Mapping (IPM). In addition, we proposed a new detecting scheme which performs the detecting algorithm on two Region of Interest (ROI) defined by different size each time. The computing burden is then reduced because vehicle detection does not have to be performed on the entire image. Finally, relative distance and Time To Collision (TTC) are estimated to warn the inappropriate driving behavior of the driver. The proposed night vehicle detection which integrates lane detection has successfully implemented in ADI-BF561 600MHz dual-core DSP.

Individual identification based on chaotic electrocardiogram signals during muscular exercise

An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each persons cardiac signal possesses unique characteristics. Thus, researchers have attempted to use ECG signals for personal identification. However, most studies verify results using ECG signals taken from databases which are obtained from subjects under the condition of rest. Therefore, the extraction and analysis of a subjects ECG typically occurs in the resting state. This study presents experiments that involve recording ECG information after the heart rate of the subjects was increased through exercise. This study adopts the root mean square value, nonlinear Lyapunov exponent, and correlation dimension to analyse ECG data, and uses a support vector machine (SVM) to classify and identify the best combination and the most appropriate kernel function of a SVM. Results show that the successful recognition rate exceeds 80% when using the nonlinear SVM with a polynomial kernel function. This study confirms the existence of unique ECG features in each person. Even in the condition of exercise, chaotic theory can be used to extract specific biological characteristics, confirming the feasibility of using ECG signals for biometric verification.

On GDM allocation method for partial range queries

Abstract Almost all the efforts devoted to the multi-disk file allocation problem have been concentrated on partial match retrieval so far, while the study of that for range retrieval is less progressive. This paper is concerned with the multi-disk file allocation problem for partial range retrieval. We particularly concentrate on the performance analysis of GDM (Generalized Disk Modulo) allocation method for partial range retrieval. A performance formula for evaluating the average response time of this allocation method over all possible partial range queries is derived first. Then, based on this formula, we investigate the optimality property of the GDM allocation method for partial range queries. It is shown that the GDM allocation method still guarantees strictly optimal performance for partial range queries under many conditions occurring commonly in practice.

Data Encryption Using ECG Signals with Chaotic Henon Map

Electrocardiogram (ECG) signals varying from person to person, it could possibly be applied as a tool for biometric recognition. This paper attempts to introduce an individual feature of ECG with a chaotic Henon map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signals from the encrypted person and applies an intelligent algorithm based on Chaos theory to generate initial keys for chaotic Henon map. High quality randomness of ECG signals results in a widely expanded key space which would be an ideal key generator for data encryption.

A note on allocating k -ary multiple key hashing files among multiple disks

Abstract In this paper, we shall introduce the concept of k -ary multiple key hashing (MKH) files and the disk modulo (DM) allocation method. It is pointed out that k -ary MKH files exhibit the property of facilitating partial match retrieval. We then derive a close-form performance formula of the DM allocation method for k -ary MKH files on multidisk systems. Further more, we show that the DM allocation method is optimal for allocating k -ary MKH files on a k -disk system.

Data Encryption and Transmission Based on Personal ECG Signals

ECG signal vary from person to person, making it difficult to be imitated and duplicated. Biometric identification based on ECG is therefore a useful application based on this feature. Synchronization of chaotic systems provides a rich mechanism which is noise-like and virtually impossible to guess or predict. This study intends to combine our previously proposed information encryption/decryption system with chaotic synchronization circuits to create private key masking. To implement the proposed secure communication system, a pair of Lorenz-based synchronized circuits is developed by using operational amplifiers, resistors, capacitors and multipliers. The verification presented involves numerical simulation and hardware implementation to demonstrate feasibility of the proposed method. High quality randomness in ECG signals results in a widely expanded key space, making it an ideal key generator for personalized data encryption. The experiments demonstrate the use of this approach in encrypting texts and images via secure communications.

Health care platform based on acquisition of ECG for HRV analysis

In the measurement of physiological signals, the electro-cardiogram (ECG) is essential to most medical diagnoses related to the heart behavior, which is closely associated with humans health. In this study, we have developed a health care platform (HCP) which is able to solve the baseline drift problem, detect the heart-rate status, and record the ECG signals. The HCP provides a convenient handheld device to collect ECG signals in moving state from only two conductive electrodes. The device transmits the data to computers through wireless transmission and shows real-time users status including spectrum analysis and Fourier transform. In this paper, the HRV analysis is divided into two parts, one is time-domain analysis in HCP and frequency-domain analysis in PC. The experimental results analyze the life-behavior patterns of tested subjects that may affect HRV parameters (HF, LF and HF/LF) so that one may achieve the task of disease prevention and health management.

Point stabilization for autonomous lawnmower using backstepping adaptive control

This paper studied the point stabilization problem for a constrained autonomous lawnmower. A constrained kinetic model is established first. The authors proposed a backstepping adaptive controller to solve the stabilizing control design problem. The proposed approach has been numerically verified.