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About 6,385 papers for “Cascade algorithm”

CASCADE CONTROL ALGORITHM OF THE ROBOTIC ARM'S SERVOMOTOR

Dumitru Moraru, Ion Fiodorov, Bartolomeu Izvoreanu et al. · Journal of Engineering Science · 2025

In this paper, there have been synthesized the control algorithms in cascade control systems. The control object is the robotic arm which is actuated by a servo motor presented as an automatic cascade control system which is consisting of two loops. The use of multiple loops is justified by the fact that with a single loop, only one parameter of the servo motor is controlled, which leads to a decrease in the reliability of the automatic system. A case study was carried out for a direct current electric motor, where proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) control algorithms were used, with P and PI applied to the inner loop, and P, PI, and PID to the outer loop. The system was simulated with the synthesized algorithms and the results were analyzed, it was established that the system has robustness and high performance for the case of using maximum stability degree method with iterations.

Cited by 0Open access

Two-scale dilation equations and the cascade algorithm

Yang Wang · 1995

We study the two--scaled dilation equation f(x) = n X k=0 c k f(2x \\Gamma k) where the coefficients c k are real and P k c 2k = P k c 2k+1 = 1. By expressing the dilation equation in matrix product form we prove a necessary and sufficient condition for the cascade algorithm (introduced by Daubechies and Lagarias) to converge uniformly to a continuous solution. We also establish several basic relations between the convergence of infinite products of matrices and the existence and regularity of solutions to the two--scale dilation equations. INTRODUCTION Two--scale dilation equations arise in many diverse applications. The general equation is f(x) = N X k=0 c k f(2x \\Gamma k) (1) where X k even c k = X k odd c k = 1: Often equation (1) has compactly supported solutions supp(f) ` [0; N ], and we usually seek the normalized one Z f(x)dx = 1: If c k = \\Gamma N k \\Delta . 2 N \\Gamma1 then f is the normalized B--spline of degree N \\Gamma 1, given by f(x) = (\\Gamma1...

Cited by 32

An Algorithm for Optimal NAND Cascade Logic Synthesis

C. Papachristou · IEEE Transactions on Computers · 1978

This paper is concerned with optimal synthesis of switching logic by a limited depth tree-like network, the NAND cascade. This cascade consists of a number of complete three-level, fan-in restricted NAND trees feeding a NAND collector. The goal of the proposed synthesis is to minimize the number of NAND trees of the cascade, which in turn will minimize its overall depth, i.e., the delay time of the cascade.

Cited by 2

I m Abstract: CASCADE ERROR PROJECTION LEARNING ALGORITHM

Tuan A. Duong, Allen R. Stubberud, Taher Daud · 2008

In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work. This frame work can be used to ob(ain the cascade correlation learning algorithm by choosing a particular set of parameters. Furthemlore, CEP learning algorithm is operated only on one layer, whereas the other set of weights can be calculated deterministically. In association wilh the dynamical stepsize change concept to convert the weight update from infinite space into a finite space, the relation between the current stepsize and the previous energy level is also given and the estimation procedure for optimal stepsize is used for validation of our proposed technique. The weight values of zero are used for starting the learning for every layer, and a single hidden unit is applied instead of using a pool of candidate hidden units as in the cascade correlation scheme. Therefore, simplicity in hardware implementation is also obtained. Furthemwre, this analysis allows us to select jiom other methods (such as the conjugate gradient descent or the Newton’s second order) one of which will be a good candidate for the learning technique. The choice of learning technique depends on the constraints of the problem (e.g., speed, petiorrnance, and hardware implementation); one technique may be more suitable than others. Moreover, for a discrete weight

Cited by 0

A Learning Algorithm for Evolving Cascade Neural Networks

Vitaly Schetinin · ArXiv.org · 2005

A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described. An ECNN starts to learn with one input node and then adding new inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm was successfully applied to classify artifacts and normal segments in clinical electroencephalograms (EEGs). The EEG segments were visually labeled by EEG-viewer. The trained ECNN has correctly classified 96.69% of the testing segments. It is slightly better than a standard fully connected neural network.

Cited by 4Open access

Research on Identification Algorithm of Cascade Control System

Damin Ding, Yagang Wang, Wei Zhang et al. · Mathematical Problems in Engineering · 2022

Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.

Cited by 0Open access

Ocular handled virtual mouse using Haar cascade algorithm

A. Malarmannan, S. Sushmitha, M. Sanjana et al. · 2025

This paper has an executable functionality to dominate the locomotion of the computer's cursor using the motion of iris of eye. The iris of eye is determined precisely and it enhances physically challenged people to operate the cursor in all the directions. It enables people to engage and sink in digital surroundings without using conventional input device like a mouse.

Cited by 0Open access

Fast perceptual image hash based on cascade algorithm

Alexey Ruchay, Vitaly Kober, Evgeniya Evtushenko · 2017

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.

Cited by 6

Autism Detection Using HAAR Cascade Machine Learning Algorithm

Lakshmiprabha, Swapnil M. Patil, Sumit Kakad et al. · 2025

Autism Spectrum Disorder It refers to a big spectrum of conditions that influence social interactions, communication skills, and repetitive behaviors. Traditionally, ASD diagnosis relies on behavioural observations, but there is increasing interest in leveraging technology for earlier detection. This project explores using the HAAR Cascade algorithm, typically employed for object detection like facial recognition, to identify different ASD types. We concentrated on four categories: Asperger Syndrome, Childhood Disintegrative Disorder, Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS), and Classic Autism are all conditions that fall under the umbrella of developmental disorders. Our approach involved training separate HAAR Cascade models for each type using meticulously labelled images. Positive samples highlighted features associated with each condition, while negative samples included unrelated facial characteristics. The system analyzes new images to classify the type of ASD or indicate no detection if relevant features are absent. Although HAAR Cascade is generally used for simpler tasks, this project aimed to assess its capability in this complex application. The success of our system heavily depended on the quality of the training data and the precision of feature identification by each model. This project is an initial exploration into using HAAR Cascade for ASD detection, suggesting that more advanced techniques, such as deep learning, may be necessary for improved accuracy. Our findings could inform future research, potentially leading to more effective combined methods.

Cited by 0Open access

Automated Fire Fighting Robot Using HAAR Cascade Algorithm

Mala Swadi, Spoorthi PA, C. Mohan Raj et al. · 2025

Fire fighting still remains a very high risk profession due to traditional methods requiring majorly on human intervention. This paper presents an innovative prototype fire fighter robot to minimize human involvement by integrating IoT and HAAR Cascade Classifier Algorithm. Arduino Nano is used to control the automated movement of the robot and extinguishing of the fire and Raspberry Pi 4 is used in fire detection of real time camera feed using OpenCV for image processing. The robot uses Pi Camera 2 to provide live video feed via Wi-Fi. A flame sensor is incorporated to detect the proximity of fire, enabling the robot to halt and begin extinguishing operations. The prototype was tested in both dimly-lit and fully-lit rooms, results show that HAAR cascade is more effective and has greater range than traditional methods i.e solely using flame sensors for detection of fire. This IoT based solution not only improves fire detection and suppressing but also significantly reduces risks to human lives by early detection of fire.

Cited by 0

Student Attendance Recognition using the Haar Cascade Algorithm

Lagisetty Keerthi, Poleboina Divya, Sairam Utukuru · 2023

Attendance systems are very important in schools, colleges, and many organizations. It tells about whether a student is regular to the school/college or not, an employee is regular to the work or not. The traditional method of attendance is to take manually by using roll calls and notes in the attendance register given by the organization. In some organizations, students need to sign in the register for attendance. But it is a difficult process, there may be a chance for the occurrence of proxy. It creates a lot of disturbances in class and a waste of time. Proxy leads to the irregularity of the students and decreases performance in academics. It may affect their goals and deviates into other activities. Alternating methods need to be implemented that should rectify the issues that exist with the traditional method, and save both time and manual work.

Cited by 1

Facial Emotion Detection Using Haar Cascade and CNN Algorithm

Nongmeikapam Thoiba Singh, Samridh Rana, Sonal Kumari et al. · 2023

Due to its wide applications in psychology, healthcare, and safety, facial emotion recognition is a crucial machine vision issue that has to be studied. Emotion detection from facial expressions is considered a challenging task because it involves numerous sophisticated processes, including the detection, analysis, and recognition of facial characteristics. In this research, the Haar Cascade algorithm and the Convolutional Neural Network (CNN) are combined to create an innovative approach for identifying facial expressions of emotion. The Haar Cascade algorithm is used to detect facial characteristics, like eyes, nose, and mouth, from an input image. These detected features are then fed into the CNN algorithm, which is responsible for recognising the person's state of mind. The suggested strategy is tested on a publicly available dataset, and the findings reveal that it achieves 98.42% accuracy. This indicates that the approach is highly effective in accurately detecting facial emotions.

Cited by 10

Cascade affine constant recursive algorithm for model-based control

Gregor Černe, Igor Škrjanc · IFAC-PapersOnLine · 2021

Paper tackles the trade-off between slow parameter adaptation and parameter variance of recursive least square estimation (rLSE) after a system change in the identification of a time-variant system. This paper proposes cascade affine constant (CAC) estimation for linear systems, which uses rLSE estimated parameters as an apriori knowledge for affine constant estimation, which can be estimated faster with lower variance because of its simple structure. In this configuration, rLSE uses a slower forgetting rate for more accurate dynamics estimation, while affine constant is used to react faster to changes in the system. The comparison is done in the setting with predictive functional control as its performance is impacted greatly by the quality of model parameters and highly correlated with the quality of model parameters. The metrics of the developed method are overall better than other methods.

Cited by 1Open access

Improve online boosting algorithm from self-learning cascade classifier

Dapeng Luo, Nong Sang, Rui Huang et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010

Online boosting algorithm has been used in many vision-related applications, such as object detection. However, in order to obtain good detection result, combining a large number of weak classifiers into a strong classifier is required. And those weak classifiers must be updated and improved online. So the training and detection speed will be reduced inevitably. This paper proposes a novel online boosting based learning method, called self-learning cascade classifier. Cascade decision strategy is integrated with the online boosting procedure. The resulting system contains enough number of weak classifiers while keeping computation cost low. The cascade structure is learned and updated online. And the structure complexity can be increased adaptively when detection task is more difficult. Moreover, most of new samples are labeled by tracking automatically. This can greatly reduce the effort by labeler. We present experimental results that demonstrate the efficient and high detection rate of the method.

Cited by 0