We present a method to monitor driver distraction based on a stereo camera to estimate the face pose and gaze of a driver in real-time. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area in the scene, a parameter which gives much information about the distraction pattern of the driver. The system does not require any subject-specific calibration, it is robust to fast and wide head rotations and works in low lighting conditions.The system provides some consistent statistics which help psychologists to assess the driver distraction patterns under influence of different In-Vehicle Information Systems (IVIS). These statistics are objective, as the drivers are not required to report their own distraction states. The proposed gaze fixation system has been tested on a set of challenging driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests.

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In this paper we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will be turned on low beams when other vehicles are detected. Our system uses a B&W micro-camera mounted in the windshield area and looking at forward of the vehicle. Digital image processing techniques are applied to analyze light sources and to detect vehicles in the images. The algorithm is efficient and able to run in real-time. Some experimental results and conclusions are presented.

A hardware solution is presented to obtain the eigenvalues and eigenvectors of a real and symmetrical matrix using field-programmable gate arrays (FPGAs). Currently, this system is used to compute the eigenvalues and eigenvectors in covariance matrices for applications in digital image processing that make use of the principal component analysis (PCA) technique. The proposed solution in this paper is based on the Jacobi method, but in comparison with other related works, it presents a different architecture that remarkably improves execution time, while reducing the number of consumed resources of the FPGA.Index Terms-CORDIC, eigenvalue, eigenvector, field-programmable gate array (FPGA).

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Matrix multiplication is a typical operation in different engineering areas, such as signal or image processing. This paper makes a brief description about some matrix multiplication proposals when working in FPGAs (Field Programmable Gate Array). Thanks to their low prices and low costs, currently these devices are used in many and different applications. There are some alternative methods that optimize execution time to carry out this operation under FPGAs. The internal structure of these devices allows parallel execution of matrix multiplication. However, a systolic structure needs many internal resources such as embedded multipliers and often it cannot be used because of the low number of embedded multipliers in the used device. This structure is commonly used in FPGAs for small size matrices. However our proposed alternatives allow an efficient multiplication of matrices of sizes as big as 512 x 512 elements. The study done in this work compares the delay and area consumed of different matrix multiplication algorithms.

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