Bing-Fei Wu

Simple error detection methods for hardware implementation of Advanced Encryption Standard

In order to prevent the Advanced Encryption Standard (AES) from suffering from differential fault attacks, the technique of error detection can be adopted to detect the errors during encryption or decryption and then to provide the information for taking further action, such as interrupting the AES process or redoing the process. Because errors occur within a function, it is not easy to predict the output. Therefore, general error control codes are not suited for AES operations. In this work, several error-detection schemes have been proposed. These schemes are based on the (n+1, n) cyclic redundancy check (CRC) over GF(28), where nisin{4,8,16}. Because of the good algebraic properties of AES, specifically the MixColumns operation, these error detection schemes are suitable for AES and efficient for the hardware implementation; they may be designed using round-level, operation-level, or algorithm-level detection. The proposed schemes have high fault coverage. In addition, the schemes proposed are scalable and symmetrical. The scalability makes these schemes suitable for an AES circuit implemented in 8-bit, 32-bit, or 128-bit architecture. Symmetry also benefits the implementation of the proposed schemes to achieve that the encryption process and the decryption process can share the same error detection hardware. These schemes are also suitable for encryption-only or decryption-only cases. Error detection for the key schedule in AES is also proposed and is based on the derived results in the data procedure of AES

A high-performance and memory-efficient pipeline architecture for the 5/3 and 9/7 discrete wavelet transform of JPEG2000 codec

In this paper, we propose a high-performance and memory-efficient pipeline architecture which performs the one-level two-dimensional (2-D) discrete wavelet transform (DWT) in the 5/3 and 9/7 filters. In general, the internal memory size of 2-D architecture highly depends on the pipeline registers of one-dimensional (1-D) DWT. Based on the lifting-based DWT algorithm, the primitive data path is modified and an efficient pipeline architecture is derived to shorten the data path. Accordingly, under the same arithmetic resources, the 1-D DWT pipeline architecture can operate at a higher processing speed (up to 200 MHz in 0.25-/spl mu/m technology) than other pipelined architectures with direct implementation. The proposed 2-D DWT architecture is composed of two 1-D processors (column and row processors). Based on the modified algorithm, the row processor can partially execute each row-wise transform with only two column-processed data. Thus, the pipeline registers of 1-D architecture do not fully turn into the internal memory of 2-D DWT. For an N/spl times/M image, only 3.5N internal memory is required for the 5/3 filter, and 5.5N is required for the 9/7 filter to perform the one-level 2-D DWT decomposition with the critical path of one multiplier delay (i.e., N and M indicate the height and width of an image). The pipeline data path is regular and practicable. Finally, the proposed architecture implements the 5/3 and 9/7 filters by cascading the three key components.

A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance

This paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic scenes. The proposed method identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis techniques. First, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied to effectively extract bright objects of interest. This automatic multilevel thresholding approach provides a robust and adaptable detection system that operates well under various nighttime illumination conditions. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The proposed real-time vision system has also been implemented and evaluated on a TI DM642 DSP-based embedded platform. The system is set up on elevated platforms to perform traffic surveillance on real highways and urban roads. Experimental results demonstrate that the proposed traffic surveillance approach is feasible and effective for vehicle detection and identification in various nighttime environments.

Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments

In speech processing, endpoint detection in noisy environments is difficult, especially in the presence of nonstationary noise. Robust endpoint detection is one of the most important areas of speech processing. Generally, the feature parameters used for endpoint detection are highly sensitive to the environment. Endpoint detection is severely degraded at low signal-to-noise ratios (SNRs) since those feature parameters cannot adequately describe the characteristics of a speech signal. As a result, this study seeks the banded structure on speech spectrogram to distinguish a speech from a nonspeech, especially in adverse environments. First, this study proposes a feature parameter, called band-partitioning spectral entropy (BSE), which exploits the use of the banded structure on speech spectrogram. A refined adaptive band selection (RABS) method is extended from the adaptive band selection method proposed by Wu et al., which adaptively selects useful bands not corrupted by noise. The successful RABS method is strongly depended on an on-line detection with minimal processing delay. In this paper, the RABS method is combined with the BSE parameter. Finally, a novel robust feature parameter, adaptive band-partitioning spectral entropy (ABSE), is presented to successfully detect endpoints in adverse environments. Experimental results indicate that the ABSE parameter is very effective under various noise conditions with several SNRs. Furthermore, the proposed algorithm outperforms other approaches and is reliable in a real car.

The Heterogeneous Systems Integration Design and Implementation for Lane Keeping on a Vehicle

In this paper, an intelligent automated lane-keeping system is proposed and implemented on our vehicle platform, i.e., TAIWAN i TS-1. This system challenges the online integrating heterogeneous systems such as a real-time vision system, a lateral controller, in-vehicle sensors, and a steering wheel actuating motor. The implemented vision system detects the lane markings ahead of the vehicle, regardless of the varieties in road appearance, and determines the desired trajectory based on the relative positions of the vehicle with respect to the center of the road. To achieve more humanlike driving behavior such as smooth turning, particularly at high levels of speed, a fuzzy gain scheduling (FGS) strategy is introduced to compensate for the feedback controller for appropriately adapting to the SW command. Instead of manual tuning by trial and error, the methodology of FGS is designed to ensure that the closed-loop system can satisfy the crossover model principle. The proposed integrated system is examined on the standard testing road at the Automotive Research and Testing Center (ARTC)1 and extra-urban highways.

A simplified approach to Bode's theorem for continuous-time and discrete-time systems

A simplified approach to W.H. Bodes (1945) theorem for both continuous-time and discrete-time systems, along with some generalization, are presented. For continuous-time systems, the constraints of open-loop stability and roll-off at s= varies as are removed. A counterexample shows that when the excess poles/zeros vanishes, the Bode integral drops from infinite to finite value when the open-loop gain crosses a critical value. A revised result is also developed. The salient feature of this approach is that at no stage are either Cauchys theorem or the Poisson integral invoked; the simplified proof relies only on elementary analysis. This approach carries over to the discrete-time cases in a straightforward manner. >

Nighttime Vehicle Detection for Driver Assistance and Autonomous Vehicles

This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding is applied on the grabbed road-scene images. Then the extracted bright objects are processed by a rule-based procedure, to identify the vehicles by locating and analyzing their vehicle light patterns, and estimate their distances to the camera-assisted car. Experimental results demonstrate the effectiveness of the proposed method on detecting vehicles at night

Dynamic Calibration and Occlusion Handling Algorithms for Lane Tracking

An approach of rapidly computing the projective width of lanes is presented to predict the projective positions and widths of lanes. The Lane Marking Extraction Finite State Machine is designed to extract points with features of lane markings in the image, and a cubic B-spline is adopted to conduct curve fitting to reconstruct road geometry. A statistical search algorithm is also proposed to correctly and adaptively determine thresholds under various kinds of illumination conditions. Furthermore, the parameters of the camera in a moving car may change with the vibration, so a dynamic calibration algorithm is applied to calibrate camera parameters and lane widths with the information of lane projection. Moreover, a fuzzy logic is applied to determine the situation of occlusion. Finally, a region-of-interest determination strategy is developed to reduce the search region and to make the detection more robust with respect to the occlusion on the lane markings or complicated changes of curves and road boundaries.

Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance

This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and then identifying and classifying the moving cars and motorbikes in the traffic scenes. Experimental results demonstrate that the proposed approach is feasible and effective for vehicle detection and identification in various nighttime environments for traffic surveillance.

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.