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Object detection is important in car sharing services. Accuracy, efficiency, and low memory consumption are desirable for object detection in car sharing services. This paper presents a network system that satisfies all these requirements. Our approach first divides the object detection task into multiple simpler local regression tasks. Then, we propose the generalized Haar filter-based convolutional neural network to reduce the consumption of memory and computing resource. To achieve real-time performance, we introduce a sparse window generation strategy to reduce the number of input image patches without sacrificing accuracy. We perform experiments on both vehicle and pedestrian data sets. Experimental results demonstrate that our approach can accurately detect objects under challenging conditions. Note to Practitioners—Object detection is an important part of intelligent vehicle technologies, which play an important role in car sharing services. Object detection provides metadata for collision avoidance, self-driving systems, and driver-assistance systems, which can result in better safety and consumer experiences in car sharing services. Although deep learning has achieved an excellent performance in object detection, they consume a large amount of storage and computing resource, which makes them difficult to be deployed for car sharing services. This paper suggests a novel approach which is based on the generalized Haar filter and the local regression strategy. Our approach is accurate, efficient, and light. The experimental results verify the effectiveness of the proposed approach in car sharing services. [ABSTRACT FROM AUTHOR]

Edge is an important feature in video classification which finds applications in video representation and coding. In H.264/AVC, intra-prediction mode decision (a computationally intensive process) is based on the orientation of the edges in the macroblock. In this paper, we first investigate the difference properties derived from three coefficients in the non-normalized Haar transform (NHT) domain and present a fast and efficient method to classify block edge using these properties. The proposed method significantly reduces the number of computational operations in the edge models determination with no multiplications and less addition operations. The use of edge classification for fast intra-prediction mode decision in H.264/AVC video coding is then presented. The experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The efficient and compact representation of images is a fundamental problem in computer vision. In this paper, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We propose two closely related novel subspace methods to model images: The nonorthogonal binary subspace (NBS) method and the binary principal component analysis (B-PCA) algorithm. NBS is spanned directly by binary box functions and can be used for image representation, fast template matching, and many other vision applications. B-PCA is a structure subspace that inherits the merits of both NBS (fast computation) and PCA (modeling data structure information). B-PCA base vectors are obtained by a novel PCA-guided NBS method. We also show that B-PCA base vectors are nearly orthogonal to each other. As a result, in the nonorthogonal vector decomposition process, the computationally intensive pseudoinverse projection operator can be approximated by the direct dot product without causing significant distance distortion. Experiments on real image data sets show a promising performance in image matching, reconstruction, and recognition tasks with significant speed improvement. [ABSTRACT FROM AUTHOR]

A technique for displaying gray shades in root-mean-square responding displays is proposed. It is based on wavelets derived from a slant matrix of order 3. We have shown that the hardware complexity of display drivers and controller can be reduced to the same extent as that of the technique using the simple integer Haar wavelets. The number of time intervals to complete a cycle is 25% less than that of Haar wavelets, and we have also shown that it is not necessary to compute the orthogonal transform repeatedly. [ABSTRACT FROM AUTHOR]

Due to the rapid development of computer and sensing technology, many measurements of process variables are readily available in manufacturing processes. These measurements carry a large amount of information about process conditions. It is highly desirable to develop a process monitoring and diagnosis methodology that can utilize this information. In this paper, a statistical process control monitoring system is developed for a class of commonly available process measurements—cycle-based waveform signals. This system integrates the statistical process control technology and the Haar wavelet transform. With it, one can not only detect a process change, but also identify the location and estimate the magnitude of the process mean shift within the signal. A case study involving a stamping process demonstrates the effectiveness of the proposed methodology on the monitoring of the profile-type data. Note to Practitioners—Cycle-based signal refers to an analog or digital signal that is obtained through automatic sensing during each operation cycle of a manufacturing process. The cycle-based signal is very common in various manufacturing processes (e.g., forming force in stamping processes, the holding force, and the current signals in spot welding processes, the insertion force in the engine assembly process). In general, cycle-based signals contain rich process information. In this paper, cycle-based signal monitoring will be accomplished by monitoring the wavelet transformation of the signal, instead of monitoring the raw observations themselves. Further, a decision-making technique is developed using the SPC monitoring system to locate where the mean shift occurred and to estimate magnitudes of mean shifts. Thus, this paper presents a generic framework for the enhanced statistical process control technique of cycle-based signals. [ABSTRACT FROM AUTHOR]

Describes the proposed hybrid finite-difference time-domain/Haar multiresolution time-domain technique for the time domain analysis. Characterization of microwave structures; Implementation of dynamic mesh refinement; Calculation of the arbitrary number of wavelet levels.

Discusses a parallel between Haar multiresolution time-domain and the classic Yee finite-difference time-domain algorithm. Implementation of the incident fields as a plane wave and extend the formulation to inhomogeneous dielectric materials; Results of simulations on some canonical targets; Conclusion.

Deals with a study which examined the state analysis and parameter estimation of bilinear systems in electronics via Haar wavelets. Properties of Haar wavelets; State analysis; Parameter estimation; Numerical examples; Conclusion.

Describes the derivation of an finite-difference time-domain method using a complete set of Haar wavelets. Three dimensional functions and time iterative difference equations; Relation between the Haar basis coefficients and the actual field values; Boundary conditions for perfect electric conductor.

Describes an inverse Haar transform processor (1-D-IFHT) of programmable length between eight and 1024 points. On-line computing of the 1-D-IFHT; Information on programmable length 1-D-IFHT processor; Implementation of 1-D-IFHT processor in a chip; Conclusion.

In this paper, the wavelet transform-based approach is studied to analyze the transient oscillatory response of an electrodynamics suspension (EDS) maglev system. The magnetic field intensity is mainly a function of the levitation height between the cradle and the track. The wavelet transform-based approach is used to analyze the harmonics of the magnetization function. Haar wavelet is selected as the mother wavelet to decompose the coefficients of the wavelet transform. Field analyses based on the Fourier series and the finite-element method are provided in order to illustrate the advantage of the proposed scheme in the analysis of dynamic characteristics of the beginning stage of levitation. [ABSTRACT FROM AUTHOR]

Presents a study which introduced a derivation of the matrices of orthogonal functions including the Haar wavelet. Application of the Laplace transform approach in the derivation; Results; Conclusion.

Introduces a transformation to localize the equations defining the successive levels of the Mallat pyramid for two-dimensional Haar wavelets. Proposed methodology for implementing the wavelet transforms in parallel architectures; Conclusion.