Corneliu Rusu

Sensors for Obstacle Detection - A Survey

The obstacle detection field is a very broad one and a lot of obstacle detection systems have been developed in the last years in this domain. We tried to identify the main character of an obstacle detection system from the ruttier scene. Thus, we classified the main types of sensors from this field in passive (visible and infrared spectrum camera) and active (radar, laser-scanner, sonar) sensors and we made a survey in this domain. After a short presentation of every type of sensor, we presented another current and fancy solution for an obstacle detection system: the fusion of different sensor together. Almost all obstacle detection systems use a combination of passive-active technology, and in general the best solution is obtained using a vision system combined with a distance sensor like radar or laser. Maybe the most low-priced system is one combining only vision systems, but the inconvenient in this case is the lack of distance information.

Classical geometrical approach to circle fitting--review and new developments

After a review of the circle fitting issue, we recall a relatively unknown method derived from a classical geometric result. We propose an improvement of this technique by reweighting the data, iterating the procedure, and choosing at every step as the new inversion point the one diametrically opposite to the previous inversion point.

Audio based solutions for detecting intruders in wild areas

This paper presents an overview of the work that has been done in the field of wildlife intruder detection using only acoustic sensors. The motivation of such an application is related to protection of large wildlife regions, such as forests, lakes, and other natural reservations. The sounds of interest are represented by humans, engines, birds and animals. In order to simulate various environmental situations, different types of noisy environments have been considered. Both low complexity and standard audio classification methods are presented. Standard audio classification methods prove to be more robust, but at an expense of significantly increased complexity. Since low complexity systems are more feasible for monitoring remote areas, the complexity issue is discussed and solutions are proposed.

Using a MEMS gyroscope to measure the Earth’s rotation for gyrocompassing applications

In this paper, a method and system for gyrocompassing based on a low-cost micro-electro-mechanical (MEMS) gyroscope are described. The proposed setup is based on the choice of a gyroscope with specified bias instability better than 2 deg h-1 and on careful error compensation. The gyroscope is aligned parallel to the local level, which helps to eliminate the g-sensitivity effect but also sacrifices a fraction of the Earth’s rotation rate that can be observed. The additive bias is compensated for by rotating the sensor mechanically and by extended Kalman filtering. In this paper, it is demonstrated that the proposed system is capable of observing the Earth’s rotation, and the north finding results show that a two-sigma accuracy of 4.03° was attained at latitude 61°N. With current MEMS gyroscopes, the system requires hours of time to achieve this accuracy, but the results demonstrate the theoretical accuracy potential of these small self-contained, low-cost sensors.

Adaptive data echo cancellation using cost function adaptation

Abstract For a digital echo canceller it is desirable to reduce the adaptation time, during which the transmission of useful data is not possible. LMS is a non-optimal algorithm in this case as the signals involved are statistically non-Gaussian. Walach and Widrow (IEEE Trans. Inform. Theory 30 (2) (March 1984) 275–283) investigated the use of a power of 4, while other research established algorithms with arbitrary integer (Pei and Tseng, IEEE J. Selected Areas Commun. 12 (9) (December 1994) 1540–1547) or non-quadratic power (Shah and Cowan, IEE.Proc.-Vis. Image Signal Process. 142 (3) (June 1995) 187–191). This paper suggests that continuous and automatic, adaptation of the error exponent gives a more satisfactory result. The family of cost function adaptation (CFA) stochastic gradient algorithm proposed allows an increase in convergence rate and, an improvement of residual error. As special case the staircase CFA algorithm is first presented, then the smooth CFA is developed. Details of implementations are also discussed. Results of simulation are provided to show the properties of the proposed family of algorithms.

Wildlife intruder detection using sounds captured by acoustic sensors

In this paper we classify the sounds originated from humans, birds and cars. The motivation of such a classification is to detect the intruders into protected wildlife regions such as protected forests, lakes, and other natural reservations. The proposed algorithm for sound encoding and classification is Time Encoded Signal Processing and Recognition (TESPAR) combined with the archetypes technique. We have tested our method on a database consisting of 300 recordings, 100 for each class, and several types of noise (white Gaussian noise, rain sound and wind sound) have been added to the recordings in order to simulate the different outdoor environments. Several pre-processing steps have been included and tested in order to verify the improvement of the classification accuracy. We performed a downsampling from 8 kHz to 6 kHz of the original recordings, followed by band pass filtering and the results shown an increased efficiency of TESPAR in the classification process.

A study of the effect of emotional state upon text-independent speaker identification

In this paper we evaluate the effect of the emotional state of a speaker when text-independent speaker identification is performed. The spectral features used for speaker recognition are the Mel-frequency cepstral coefficients, while for the training of the speaker models and testing the system the Gaussian Mixture Models are employed. The tests are performed on the Berlin emotional speech database which contains 10 different speakers recorded in different emotional situations: happy, angry, fear, bored, sad and neutral. The results show an important influence of the emotional state upon text-independent speaker identification. In the end we try to give a possible solution to this problem.

On-line signature recognition approach based on wavelets and Support Vector Machines

In this work, a new on-line signature verification system is proposed. Firstly, the pen-position parameters of the online signature are decomposed into multiscale signals by using the wavelet transform technique. A TESPAR DZ based method is employed to code the approximation and details coefficients. Thus, for each analyzed time function, a fixed dimension feature vector is obtained. Experimental results were reported using the SVC2004 database. The models were trained and tested with the Support Vector Machine classifier. A feature level fusion strategy was adapted.

Visible-infrared fusion in the frame of an obstacle recognition system

In this article we propose different fusion schemes using information provided by visible and infrared images for road obstacle SVM-based classification. Three approaches for the fusion of VIS and IR information are presented. The early fusion yields a feature vector integrating at the feature level both visual and infrared information. The obtained bimodal feature vector is used as input to an SVM-based classification scheme. The intermediate fusion, which is performed at the kernel level combines different simple kernels of the SVM classifier in order to obtain a multiple kernel (MK). The late fusion combines matching scores of individual obstacle recognition modules in order to improve the systems final decision. In this late fusion case two methods have been considered to calculate the optimum weighting parameter: an Adaptive Fusion of Scores (AFScores) and a non-Adaptive Fusion of Scores (nAFScores). Comparative results showed that fusion-based obstacle recognition systems outperform monomodal visual and infrared obstacle recognizers. An important advantage of these fusion-based systems is their possibility to adapt to the environmental illumination conditions due to the weighting parameter which can contribute to the adjustments of the systems final decision.

Supercapacitor modelling using experimental measurements

Supercapacitors are components for energy storage, dedicated for applications where both energy and power density are needed. Even if their energy density is ten times lower than the energy density of batteries, supercapacitors offer new alternatives for applications where energy storage is needed. We have done an experimental bench and we have measured the voltage across a supercapacitor in a constant current charge/discharge cycle. Using these measurements and a mathematical algorithm we have proposed an electrical model for the supercapacitor, and we have determined the parameters of the model also.