Design Of Recognition System Research Articles

Structural-functional organization of the visual system in ensuring goal-directed activity

A review of the results of half a century of research into the visual system as a hierarchical structure is presented: a multichannel, multilayer “pyramid”, each layer of which has a different spatiotemporal resolution, but together provides an invariant description of images for their classification, decision making, organization of eye movements and target search. An analysis of the multichannel organization of the human visual system was carried out, as the most effective and most economical. The “periscope and telescopic vision” systems, unique in their morphological and functional characteristics, are identified, providing gaze translation and recognition when searching and achieving a goal. Models of the pyramidal organization of the visual system have justified their existence by having an exceptional influence on the development of engineering solutions for the design of recognition systems operating in real time and the creation of artificial neural networks.

Design and experimental evaluation of an intelligent sugarcane stem node recognition system based on enhanced YOLOv5s

Design and experimental evaluation of an intelligent sugarcane stem node recognition system based on enhanced YOLOv5s

EXPRESS: Status Downgrade: the Impact of Losing Status on a User-generated Content Platform

Non-financial incentives such as badges, ranks, and status are often used to encourage user participation on online platforms. This study focuses on the effect of one such incentive, “status,” in the context of a thirdparty restaurant-review platform. In contrast to previous research that has mainly focused on the effects of such incentives on subsequent contributions from users who gained statuses, we explore how the intrinsic and perceived quality of content generated by users is impacted after users lose their statuses. Using natural language processing (NLP) techniques to extract quality metrics from online reviews in our dataset, we exploit a quasi-experimental setting and demonstrate that even though the intrinsic quality of reviews significantly decreases after a reviewer is demoted by a platform, consumers on the platform nonetheless perceive these reviews as disproportionately useful. We draw on inequity theory and the elaboration likelihood model to theoretically support our empirical results, as well as conduct mechanism analyses to rule out alternative explanations. Furthermore, we find that temporal associations with a platform or with an elevated status do not moderate the effect of status loss on the intrinsic and perceived quality of reviews written post-demotion. The implications of our findings are significant for platform managers who manage the design of status-driven recognition systems and must determine how the change in status should be displayed on the platform.

Design and analysis of face recognition system based on VGGFace-16 with various classifiers

This research presents a face recognition system based on different classifiers that deal with various face positions. The proposed system involves the extraction of features through the VGG-Face-16 deep neural network, which only extracts essential features of input images, leading to an improved recognition step and enhanced algorithm efficiency, while the recognition involves the radial basis function in support vector machine (SVM) classifier and evaluate the performance of the system. Also, the system is designed and implemented later by using other classifiers; they are K-neareste2 neighbour (KNN) classifiers, logistic regression (LR), gradient boosting (XGBoost), decision tree classifier (DT) and Naive Bayes classifier (NB). The proposed algorithm was tested with the four face databases: AT&T, PINs Face, linear friction welding (LFW) and real database. The database was divided into two groups: One contains a percentage of images that are used for training and the second contains a percentage of images (remainder) which was used for testing. The results show that the classification by RBF in SVM has the highest recognition rate in the case of using small, medium and large databases; it was 100% in AT&T and Real database, while its efficiency appears to be lower when using large-size databases whereas it is 96% in PINs database and 60.1% in LFW database.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.

Design and Visualization Strategy of Brand Recognition System Based on CAD Technology

Design and Visualization Strategy of Brand Recognition System Based on CAD Technology

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Design of Human Posture Recognition System Based on Internet of Things Technology and Image Segmentation Algorithm

Abstract To address the problem of the traditional human posture recognition system being easily affected by environmental factors such as noise, a human posture recognition system based on Internet of Things technology and an image segmentation algorithm was designed. To do this, we selected the attitude sensor, designed the attitude sensor structure based on micro electro mechanical system technology, and had a variety of selectable data output modes. A 36 V power supply was selected as the circuit design index to provide sufficient power for the system. The main control chip was designed, and its state information description and external output were installed in a single chip or the same component. The denoising process based on image segmentation algorithm was designed, the error correction model was constructed, the measured values were normalized, and the image segmentation model was constructed. We extracted the node features of human body posture, used the image segmentation algorithm to construct the structure diagram of human body posture nodes, located the local recognition area of the node structure map, and designed the human body posture recognition system. The experimental results show that the designed human gesture recognition system has better effect and better recognition performance.

Hand Gestures for Personal Computer Control

Abstract: Hand gestures have emerged as a promising modality for enhancing personal computer (PC) control, offering intuitive and natural interaction methods. This research paper explores the design, implementation, and evaluation of a hand gesture recognition system for PC interaction. We review existing methods of PC control and discuss the limitations of traditional input modalities. The paper outlines the process of hand gesture recognition, including data acquisition, preprocessing, feature extraction, and classification. We describe the design considerations and implementation details of the gesture recognition system, highlighting the hardware and software requirements. Empirical evaluations are presented to assess the system's performance in terms of recognition accuracy, response time, and user satisfaction. Furthermore, we explore potential applications of hand gesture control across various domains and discuss challenges and future directions in the field. This research contributes to advancing the state-of-the-art in HCI by demonstrating the feasibility and effectiveness of hand gestures for PC interaction

Design of Badminton Technical Movement Recognition System Based on Improved Agnes Algorithm

The Badminton Technical Movement Recognition System is a technology-driven solution aimed at identifying and analyzing various technical movements performed by badminton players during gameplay. Leveraging advanced sensors, motion tracking devices, and machine learning algorithms, this system captures and interprets data related to player movements, racket swings, footwork, and other key actions on the court. By analyzing this data in real-time or post-match, coaches, players, and analysts can gain valuable insights into performance, technique, and areas for improvement. The system's ability to recognize and quantify specific movements allows for detailed performance assessment, personalized training programs, and strategic game planning. The Badminton Technical Movement Recognition System serves as a powerful tool for enhancing player development and optimizing performance in the sport of badminton. The paper presents a comprehensive study on the application of the Multi-Modal AGNES algorithm across diverse domains, encompassing movement pattern estimation, modal feature extraction, coordinate estimation, and badminton feature estimation. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. For instance, the algorithm achieved an average accuracy of 90% in classifying movement patterns in a dataset of 1000 observations. Additionally, it accurately estimated modal features such as swing speed, racket angle, and shuttlecock speed with a mean error rate of less than 5%.

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

Image Recognition System for Mechanical Parts Based on SSD and YOLOv5

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Design and implementation of a real-time face recognition system based on artificial intelligence techniques

Design and implementation of a real-time face recognition system based on artificial intelligence techniques

Design and Implementation of Apple Leaf Disease Recognition System Based on ResNet50

Timely identification of apple leaf diseases is critical to preventing crop losses and safeguarding yields. Spotted leaf drop, brown spot, gray spot, mosaic and rust are all common types of apple leaf diseases, and their presence signals a potential risk that could lead to significant reductions in fruit and crop yields. The apple industry is thus at risk of economic losses. In order to solve this problem, this study applies ResNet50, a deep learning model for image recognition and classification of apple leaf diseases, and develops an intelligent recognition system for apple leaf diseases by combining PyQt technology. The purpose of this system is to overcome the shortcomings of traditional recognition methods. Through experimental validation, the ResNet50 model achieves a high accuracy rate of 93.19% in apple leaf disease recognition, demonstrating its efficiency and practicality in practical applications.

IOT BASED ATTENDANCE SYSTEM USING FINGERPRINT SENSOR

To provide reliable, time-saving and automatic class attendance system, the concept of Internet of Things (IoT) based class attendance monitoring system using embedded Linux platform is presented in this project . The study is focused on the design and implementation of fingerprint detection and recognition system using Arduino Uno. The system takes fingerprint of students, and analyzes, detects and recognizes fingerprint using sensor .After collecting processing data, the system generates a final attendance record and uploads it in a cloud server. The cloud server has been implemented using python based web framework. The record can be accessed remotely from a user-friendly, web application using the Internet. Finally, the system is also capable of sending an email notification with the final record to the teachers and students in a specific time. Tests and performance analysis were done to verify the efficiency of this system

Design of a Cow Body Recognition System Based on Deep Learning

Design of a Cow Body Recognition System Based on Deep Learning

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

AbstractThe field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

Design of Motor Skill Recognition and Hierarchical Evaluation System for Table Tennis Players

Design of Motor Skill Recognition and Hierarchical Evaluation System for Table Tennis Players

Design and implementation of a silent speech recognition system based on sEMG signals: A neural network approach

Silent speech recognition (SSR) is a system that can recognize verbal expressions when speech signals are not accessible. It has found versatile applications not only in helping those who suffer a speech impediment, but also in human–computer interaction. In this paper, we propose an SSR system based on surface electromyography (sEMG) signals. We first design a Mandarin corpus which contains 135-class utterances and collect 8-channel sEMG data from 12 subjects. After preprocessing the raw sEMG signals, we extract a time–frequency hybrid feature map for each sample. Then, we use two data augmentation strategies to obtain more data in order to improve the performance of our model. Finally, we test our proposed models on two tasks: the multi-subject mixed classification task and the single subject classification task. We use a 5-fold cross-validation to evaluate our model on both tasks. The GRU model performs best, achieving a recognition accuracy of 88.01% on the multi-subject mixed classification task and a maximum recognition accuracy of 97.19% on the single subject classification task. Experiments results demonstrate the effectiveness of our proposed system.