Real Time Machine Learning Based Car Detection in Images With Fast Training

Our primary interest is to build fast and reliable object recognizers in images based on small training sets. This is important in cases where the training set needs to be built mostly manually, as in the case that we studied, the recognition of the Honda Accord 2004 from rear views. We describe a novel variant of the AdaBoost based learning algorithm, which builds a strong classifier by incremental addition of weak classifiers (WCs) that minimize the combined error of the already selected WCs. Each WC is trained only once, and examples do not change their weights. We describe a set of appropriate feature types for the considered recognition problem, including a redness measure and dominant edge orientations. The existing edge orientation bin division was improved by shifting so that all horizontal (vertical, respectively) edges belong to the same bin. We propose to pre-eliminate features whose best threshold value is near the trivial position at the minimum or maximum of all threshold values. This is a novel method that has reduced the training set WC quantity to less than 10% of its original number, greatly speeding up training time, and showing no negative impact on the quality of the final classifier. We also modified the AdaBoost based learning machine. Our experiments indicate that the set of features used by Viola and Jones and others for face recognition was inefficient for our problem, recognizing cars accurately and in real time with fast training. Our training method has resulted in finding a very accurate classifier containing merely 30 WCs after about 1 h of training. Compared to existing literature, we have overall achieved the design of a real time object detection machine with the least number of examples, the least number of WCs, the fastest training time, and with competitive detection and false positive rates.     

Low-Power VLSI Implementation of the Inner Receiver for OFDM-Based WLAN Systems

In this paper, we propose low-power designs for the synchronizer and channel estimator units of the Inner Receiver in wireless local area network systems. The objective of the work is the optimization, with respect to power, area, and latency, of both the signal processing algorithms themselves and their implementation. Novel circuit design strategies have been employed to realize optimal hardware and power efficient architectures for the fast Fourier transform, arc tangent computation unit, numerically controlled oscillator, and the decimation filters. The use of multiple clock domains and clock gating reduces the power consumption further. These blocks have been integrated into an experimental digital baseband processor for the IEEE 802.11a standard implemented in the 0.25mum- 5-metal layer BiCMOS technology from Institute for High Performance Microelectronics.     

On the role of hydrophilicity and hydrophobicity in aqueous heterophase polymerization

This paper reports on experimental results of aqueous heterophase polymerizations with monomers of quite different solubility in water ranging from the water-soluble 2-hydroxyethyl methacrylate to lauryl methacrylate with solubility in water of only about 10⁻⁴ mM. A calorimetric study revealed the strong influence of both the hydrophilicity of the monomer and the stirrer speed on the rate of polymerization in the absence of surfactants. In order to obtain maximum latex yield and high efficiency (which is a measure considering colloidal properties, polymerization recipe, and polymerization parameters) the initiator-surfactant combination must be properly chosen in dependence on the hydrophilicity of the monomer. Results are presented for sodium alkyl sulfates or disodium-N-stearoyl-l-glutamate as surfactant and potassium peroxodisulfate, or poly(ethylene glycol)-azo- compounds, or 2,2′-azobis(N-2′-methylpropanoyl-2-amino-alkyl-1)-sulfonates as initiators.     

Adaptive output-feedback stabilization of non-local hyperbolic PDEs

We address the problem of adaptive output-feedback stabilization of general first-order hyperbolic partial integro-differential equations (PIDE). Such systems are also referred to as PDEs with non-local (in space) terms. We apply control at one boundary, take measurements on the other boundary, and allow the system’s functional coefficients to be unknown. To deal with the absence of both full-state measurement and parameter knowledge, we introduce a pre-transformation (which happens to be based on backstepping) of the system into an observer canonical form. In that form, the problem of adaptive observer design becomes tractable. Both the parameter estimator and the control law employ only the input and output signals (and their histories over one unit of time). Prior to presenting the adaptive design, we present the non-adaptive/baseline controller, which is novel in its own right and facilitates the understanding of the more complex, adaptive system. The parameter estimator is of the gradient type, based on a parametric model in the form of an integral equation relating delayed values of the input and output. For the closed-loop system we establish boundedness of all signals, pointwise in space and time, and convergence of the PDE state to zero pointwise in space. We illustrate our result with a simulation.     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

Automatic player position detection in basketball games

This paper presents us with a framework for the automatic player position detection (APPD) in the game of basketball. Court players are detected in the images broadcasted via television stations. In them, at any point of time, the view is from only one camera. This makes the detection process much more difficult. The player detection is based on the mixture of non-oriented pictorial structures. The detection of body parts is performed by the Support Vector Machine (SVM) algorithm. The results of these detections are combined together with constraints on their locations, which specify the position of one body part with respect to the parent body part. In order to train the whole model, we used a latent form of SVM called the latent SVM (LSVM). Such approach generated the statistical accuracy of about 82 %, which represents one of the best results in basketball player detection framework. Beside players, the algorithm detected a certain number of false positive objects. These are mostly people from the audience and the referees as well. This paper contains a simple and robust solution to remove them all, based on the play court boundaries and the histogram comparison. Separating players in different teams is done by k-means clustering. The inputs to this algorithm are saturation histograms calculated on the jerseys. A spatial transformation is determined by the detected play court boundaries and the actual court measures. Using this transformation, points representing the location of detected players in TV images are mapped to the actual location of players on the court, which was the main goal of our research. The proposed solution is sound and efficient. In addition, it is backed up by the experimental results obtained using the model of the actual footage of basketball games.     

Stochastic source seeking for nonholonomic unicycle

We apply the recently introduced method of stochastic extremum seeking to navigate a nonholonomic unicycle towards the maximum of an unknown, spatially distributed signal field, using only the measurement of the signal at the vehicle’s location but without the measurement of the vehicle’s position. Keeping the forward velocity constant and controlling only the angular velocity, we design a stochastic source seeking control law which employs excitation based on filtered white noise, rather than sinusoidal perturbations used in the existing work. We study stability with the help of stochastic averaging theorems that we recently developed for general nonlinear continuous-time systems with stochastic perturbations. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source. We characterize the convergence speed explicitly and provide design guidelines for maximizing it, as well as for minimizing the residual set near the source. We present a detailed simulation study, including a study of the effect of saturation on the steering input.     

Functionally testable path delay faults on a microprocessor

The impact of delay defects on these functionally untestable paths on overall circuit performance involves identification of such paths determining the achievable path delay fault coverage and reducing the subsequent test generation effort. The experimental results for two microprocessors (Parwan and DLX) indicate that a significant percentage of structurally testable paths are functionally untestable     

3D distance fields: a survey of techniques and applications

A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?     

Influence of elastic and thermal mismatch on the local crack-driving force in brittle composites

An expression is derived for the change of localK ₁ value of a crackfront near circular and spherical inclusions with elastic moduli and thermal expansion coefficient different from those of the matrix. The derivation is based on the concept of an image stress which is imposed on the crack, to illustrate the interaction between elastic and thermal stress concentrations developed around inclusions in a composite material and the crack-tip stress field.