Cascade Algorithm Research Articles

Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

It is necessary for autonomous robotics in agriculture to provide real time feedback, but due to a diverse array of objects and lack of landscape uniformity this objective is inherently complex. The current study presents two implementations of the multiple-expert colour feature extreme learning machine (MEC-ELM). The MEC-ELM is a cascading algorithm that has been implemented along side a summed area table (SAT) for fast feature extraction and object classification, for a fully functioning object detection algorithm. The MEC-ELM is an implementation of the colour feature extreme learning machine (CF-ELM), which is an extreme learning machine (ELM) with a partially connected hidden layer; taking three colour bands as inputs. The colour implementation used with the SAT enable the MEC-ELM to find and classify objects quickly, with 84% precision and 91% recall in weed detection in the Y’UV colour space and in 0.5 s per frame. The colour implementation is however limited to low resolution images and for this reason a colour level co-occurrence matrix (CLCM) variant of the MEC-ELM is proposed. This variant uses the SAT to produce a CLCM and texture analyses, with texture values processed as an input to the MEC-ELM. This enabled the MEC-ELM to achieve 78–85% precision and 81–93% recall in cattle, weed and quad bike detection and in times between 1 and 2 s per frame. Both implementations were benchmarked on a standard i7 mobile processor. Thus the results presented in this paper demonstrated that the MEC-ELM with SAT grid and CLCM makes an ideal candidate for fast object detection in complex and/or agricultural landscapes.

METHOD OF COMPENSATION OF IONOSPHERE ERRORS OF SPACE OBJECTS COORDINATES DEFINITION BY MEANS OF TWO POSITION RADAR OBSERVATION

The algorithm of estimation and compensation of ionosphere influence on the measurement of parameters of the motion of space objects in two-position radar system with account of radio physical effects depending on elevation angles and the operating frequency is developed. It is assumed that the observed space object is traсked object, the orbital parameters which are well known, including the dependence of the velocity of the point on the orbit, and the uncertainty of the current coordinates of the object is caused mainly by forecast error of its position of in orbit (longitudinal error). To estimate the true position of space object in the orbit and the parameter, determining the influence of the ionosphere, a joint optimal processing of measurement of ranges to the object, obtained by two separated radars, taking into account the relevant ionospheric propagation delays and available a priori data on observable object trajectory. Estimation of unknown parameters are obtained on the basis of the criterion of maximum a posteriori probability density for these parameters, taking into account the measured and a priori data. The task of searching for maximum a posteriori probability density is reduced to task of searching of minimum weighted sum of squares, for the solution of which the cascade algorithm of iteration through is implemented in the work. Estimation accuracy of the position of space objects in orbit after compensation of ionosphere influence have been studied by Monte-Carlo method. Dependencies of mean square error of the position estimation of space objects upon elevation angles, operation frequency and solar activity have been obtained. It is shown that the effectiveness of the algorithm increases with the spatial base of measurements (for a fixed orbit of the object).

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

A cascadic multilevel optimization algorithm for the design of composite structures with curvilinear fiber based on Shepard interpolation

A hierarchy of parameterizations of fiber angle arrangement are constructed by using the Shepard interpolation. From the top level to the bottom level of the hierarchy, the number and density of design points for the Shepard interpolation increase, thus the design freedom and the resolution of parameterization increase as well. Also, a hierarchy of optimization problems are formulated, and they are solved successively from the coarsest level to the finest level. After the optimization problem at a coarse level is solved, one goes to its neighboring finer level, using the solution of the former one to compute an initial design for the latter one. Again, the Shepard interpolation is used for the computation of initial design. In the cascadic multilevel optimization, it is not necessary to devote too much effort to find an accurate solution for the optimization at a coarse level, and the optimization is allowed to stop when it comes near a local optimal solution. Only at the finest level, it is meaningful to seek an accurate solution which is usually also a local optima. Therefore, a guideline for the selection of parameters that affect efficiency of the optimization at all the levels is proposed.

Haar wavelets and subdivision algorithms on the plane

This paper presents a classification of all two-digits Haar systems and tiles on the two-dimension plane up to affine similarity. We obtain three cases, only two of them (rectangular and Dragon tile) are well known. In all of them we compute the H\older regularity in $L_2$ of corresponding Haar functions. The technique of calculating the H\older regularity is well known for univariate wavelets and it was recently expended into multivariate wavelets. These values also give us the information about the rate of convergence of the corresponding subdivision algorithms and the rate of convergence of the cascade algorithm of the corresponding Haar decomposition.

Building Information Modeling enabled Cascading Formwork Management Tool

Abstract Formwork systems are accountable for a significant share of the cost of reinforced concrete structures. The application of constructability principles to the design, selection and management of formwork systems in the preconstruction phase can significantly reduce the cost of reinforced concrete construction projects. Although many studies have developed tools and methodologies to automate the design and selection of formwork systems, few studies have explored the benefits of improving the process of managing formwork. The focus of this paper is on the use of BIM along with a cascading tool to maximize the return on formwork investment and improve the management of formwork. This paper presents an approach to utilize data drawn from the building information models coupled with a cascading algorithm to efficiently manage the formwork inventory on a construction project by generating a scheduled formwork reuse plan and calculating the minimum quantity of formwork required for the project. The paper discusses the use of BIM to extract data required for the cascading tool, working of the cascading algorithm and the development of the tool. The paper ends by presenting a case study where the developed tool was applied on a construction project in Cincinnati, Ohio and 13% savings in formwork material cost was reported.

Cascading Target Tracking Control in Wireless Camera Sensor and Actuator Networks

AbstractRecent years have witnessed the fast development of camera sensor networks (CSNs), but most of the existing research methods are based on homogeneous CSNs, which are not cost‐effective and are hard to evolve from traditional wireless sensor networks (WSNs). In this paper, we focus on the target tracking problem in heterogeneous wireless camera sensor and actuator networks which consists of a large number of common static sensors and a small number of mobile camera sensors. The objective is to control camera sensors to track targets dependably with maximized effective monitoring time and short moving distance. We first design a novel tracking algorithm for tracking one target where multiple camera sensors cooperate with each other to track the target in a cascading scheme. Moreover, this cascading algorithm is extended for a multi‐target scenario in consideration of tracking performance and loading balance for multiple targets. The effectiveness of proposed control algorithms is validated by extensive simulation results, which show that they can achieve high monitoring efficiency and comparatively short moving distance.

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.

Mathematical modelling of radiation defect formation processes in the materials irradiated with protons and alpha particles

Metals and alloys still remain constructional, instrumental and other materials basis. An important factor in their properties formation are the crystalline grid nano-defects arising, in particular, in case of radiation. By the ionic bombing receiving high-quality, dense metal films and coverings on substrates could be realized. Such materials search and development rely on clear understanding nano-defects radiation creation mechanisms. In the work process of interaction protons and alpha particles with substance and radiation defects formations is considered. For calculation the cascade and probabilistic functions, the primary beaten-out atoms (PBOA) ranges, radiation defects concentration needs to execute approximating expression selection and to find approximation coefficients for interaction section. Interaction section for protons and alpha particles is calculated by Rutherford's formula. Analytical expression on cascade and probabilistic function taking into account energy losses for protons and alpha particles from recurrence relations for transition probabilities is received. The cascade and probabilistic function calculation algorithm, primary beaten-out atoms range, radiation defects concentration is given. G M T Detect language Afrikaans Albanian Arabic Armenian Azerbaijani Basque Belarusian Bengali Bosnian Bulgarian Catalan Cebuano Chichewa Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Esperanto Estonian Filipino Finnish French Galician Georgian German Greek Gujarati Haitian Creole Hausa Hebrew Hindi Hmong Hungarian Icelandic Igbo Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Lao Latin Latvian Lithuanian Macedonian Malagasy Malay Malayalam Maltese Maori Marathi Mongolian Myanmar (Burmese) Nepali Norwegian Persian Polish Portuguese Punjabi Romanian Russian Serbian Sesotho Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tajik Tamil Telugu Thai Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Yiddish Yoruba Zulu Afrikaans Albanian Arabic Armenian Azerbaijani Basque Belarusian Bengali Bosnian Bulgarian Catalan Cebuano Chichewa Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Esperanto Estonian Filipino Finnish French Galician Georgian German Greek Gujarati Haitian Creole Hausa Hebrew Hindi Hmong Hungarian Icelandic Igbo Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Lao Latin Latvian Lithuanian Macedonian Malagasy Malay Malayalam Maltese Maori Marathi Mongolian Myanmar (Burmese) Nepali Norwegian Persian Polish Portuguese Punjabi Romanian Russian Serbian Sesotho Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tajik Tamil Telugu Thai Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Yiddish Yoruba Zulu Text-to-speech function is limited to 200 characters Options : History : Feedback : Donate Close

The Cascadic Multigrid Method of the Weak Galerkin Method for Second-Order Elliptic Equation

This paper is devoted to the analysis of the cascadic multigrid algorithm for solving the linear system arising from the weak Galerkin finite element method. The proposed cascadic multigrid method is optimal for conjugate gradient iteration and quasi-optimal for Jacobi, Gauss-Seidel, and Richardson iterations. Numerical results are also provided to validate our theoretical analysis.

Simultaneous detection of multiple debris via a cascade of numerical evaluations and a voting scheme for lines in an image sequence

This paper presents a novel method to simultaneously detect multiple trajectories of space debris in an observation image sequence to establish a reliable model for space debris environment in Geosynchronous Earth Orbit (GEO). The debris in GEO often appear faintly in image sequences due to the high altitude. A simple but steady way to detect such faint debris is to decrease a threshold value of binarization applied to an image sequence during preprocessing. However, a low threshold value of binarization leads to extracting a large number of objects other than debris that become obstacles to detect debris trajectories. In order to detect debris from binarized image frames with massive obstacles, this work proposes a method that utilizes a cascade of numerical evaluations and a voting scheme to evaluate characteristics of the line segments obtained by connecting two image objects in different image frames, which are the candidates of debris trajectories. In the proposed method, the line segments corresponding to objects other than debris are filtered out using three types of characteristics, namely displacement, direction, and continuity. First, the displacement and direction of debris motion are evaluated to remove irrelevant trajectories. Then, the continuity of the remaining line segments is checked to find debris by counting the number of image objects appearing on or close to the line segments. Since checking the continuity can be regarded as a voting scheme, the proposed cascade algorithm can take advantage of the properties of voting method such as the Hough transform, i.e., the robustness against heavy noises and clutters, and ability of detecting multiple trajectories simultaneously. The experimental tests using real image sequences obtained in a past observation campaign demonstrate the effectiveness of the proposed method.

Personalized gesture interactions for cyber-physical smart-home environments

A gesture-based interaction system for smart homes is a part of a complex cyber-physical environment, for which researchers and developers need to address major challenges in providing personalized gesture interactions. However, current research efforts have not tackled the problem of personalized gesture recognition that often involves user identification. To address this problem, we propose in this work a new event-driven service-oriented framework called gesture services for cyber-physical environments (GS-CPE) that extends the architecture of our previous work gesture profile for web services (GPWS). To provide user identification functionality, GS-CPE introduces a two-phase cascading gesture password recognition algorithm for gesture-based user identification using a two-phase cascading classifier with the hidden Markov model and the Golden Section Search, which achieves an accuracy rate of 96.2% with a small training dataset. To support personalized gesture interaction, an enhanced version of the Dynamic Time Warping algorithm with multiple gestural input sources and dynamic template adaptation support is implemented. Our experimental results demonstrate the performance of the algorithm can achieve an average accuracy rate of 98.5% in practical scenarios. Comparison results reveal that GS-CPE has faster response time and higher accuracy rate than other gesture interaction systems designed for smart-home environments.

Hardware-Based Implementation of a PIDR Controller for Single-Phase Power Factor Correction

In a single-phase power factor correction (PFC), the standard cascaded control algorithm using a proportional-integral-derivative (PID) controller has two main drawbacks: an inability to track sinusoidal current reference and low harmonic compensation capability. These drawbacks cause poor power factor and high harmonics in grid current. To improve these drawbacks, this paper uses a proportional-integral-derivative-resonant (PIDR) controller which combines a type-III PID with proportional-resonant (PR) controllers in the PFC. Based on a small signal model of the PFC, the type-III PID controller was implemented taking into account the bandwidth and phase margin of the PFC system. To adopt the PR controllers, the spectrum of inductor current of the PFC was analyzed in frequency domain. The hybrid PIDR controller were simulated using PSCAD/EMTDC and implemented on a 3kW PFC prototype hardware. The performance results of the hybrid PIDR controller were compared with those of an individual type-III PID controller. Both controllers were implemented successfully in the single-phase PFC. The total harmonic distortion of the proposed controller were much better than those of the individual type-III PID controller.

Analysis and construction of a family of refinable functions based on generalized Bernstein polynomials

In this paper, we construct a new family of refinable functions from generalized Bernstein polynomials, which include pseudo-splines of Type II. A comprehensive analysis of the refinable functions is carried out. We then prove the convergence of cascade algorithms associated with the new masks and construct Riesz wavelets whose dilation and translation form a Riesz basis for $L_{2}(\mathbb{R})$ . Stability of the subdivision schemes, regularity and approximation orders are obtained. We also illustrate the symmetry of the corresponding refinable functions.

Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.

Haar Cascade Algorithm for the Visually Impaired to Detect and Recognize Objects

Haar Cascade Algorithm for the Visually Impaired to Detect and Recognize Objects

Design of Time–Frequency Optimal Three-Band Wavelet Filter Banks with Unit Sobolev Regularity Using Frequency Domain Sampling

In this paper, we design three-band time---frequency-localized orthogonal wavelet filter banks having single vanishing moment. We propose new expressions to compute mean and variances in time and frequency from the samples of the Fourier transform of the asymmetric band-pass compactly supported wavelet functions. We determine discrete-time filter of length eight that generates the time---frequency optimal time-limited scaling and wavelet functions using cascade algorithm. Time---frequency product (TFP) of a function is defined as the product of its time variance and frequency variance. The TFP of the designed functions is close to 0.25 with unit Sobolev regularity. Three-band filter banks are designed by minimizing a weighted combination of TFPs of wavelets and scaling functions. Interestingly, empirical results show that time---frequency optimal, filter banks of length nine, designed with the proposed methodology, have unit Sobolev regularity, which is maximum achievable with single vanishing moment. Design examples for length six and length nine filter banks are given to demonstrate the effectiveness of the proposed design methodology.

On the Exact Values of Daubechies Wavelets

In this paper we propose an algorithm for the calculation of the exact values of compactly-supported Daubechies wavelet functions. The algorithm is iterative, performing a single convolution operation at each step. It requires solving, at the first step only, a linear system of a relatively small size. The novelty of the algorithm is that once the values at dyadic points at a certain level j are calculated they do not need to be updated at the next step. We find that this algorithm is superior to the well-known cascade algorithm proposed by Ingrid Daubechies. This algorithm can serve well in wavelet based methods for the numerical solutions of differential equations. The algorithm is tested on Daubechies scaling functions as well as Daubechies coiflets. Comparison with the values obtained using the cascade algorithm is made. We found that the cascade algorithm results converge to ours

Compactly Supported Tight and Sibling Frames Based on Generalized Bernstein Polynomials

We obtain a family of refinable functions based on generalized Bernstein polynomials to provide derived properties. The convergence of cascade algorithms associated with the new masks is proved, which guarantees the existence of refinable functions. Then, we analyze the symmetry, regularity, and approximation order of the refinable functions, which are of importance. Tight and sibling frames are constructed and interorthogonality of sibling frames is demonstrated. Finally, we give numerical examples to explicitly illustrate the construction of the proposed approach.

CONTROL SYSTEM OF ALTERNATIVE ENERGY SOURCES WITH PREDICTION OF A CONDITION

Describes the main approaches to the creation control systems complexes of alternative energy sources. The paper describes the algorithm control with prediction of condition. The features of this algorithm is to predict condition the objects of the complex, dynamic optimization of the equipment and heat analysis needs of the consumer. The results of numerical simulation of the algorithm, and its comparison with the cascade algorithm.