Limited Receptive Area Research Articles

A novel device-free Wi-Fi indoor localization using a convolutional neural network based on residual attention.

These days, location-based services, or LBS, are used for various consumer applications, including indoor localization. Due to the ease with which Wi-Fi can be accessed in various interior settings, there has been increasing interest in Wi-Fi-based indoor localisation. Deep learning in indoor localisation systems that use channel state information (CSI) fingerprinting has seen widespread adoption. Usually, these systems comprise two primary components: a positioning network and a tracking system. The positioning network is responsible for learning the planning from high-dimensional CSI to physical positions, and the following system uses historical CSI to decrease positioning error. This work presents a novel localization method that combines high accuracy and generalizability. However, existing convolutional neural network (CNN) fingerprinting placement algorithms have a limited receptive area, limiting their effectiveness since important data in CSI has not been thoroughly explored. We offer a unique attention-augmented residual CNN to remedy this issue so that the data acquired and the global context in CSI may be utilized to their full potential. On the other hand, while considering the generalizability of a monitoring device, we uncouple the scheme from the CSI environments to make it feasible to use a single tracking system across all contexts. To be more specific, we recast the tracking issue as a denoising task and then used a deep route before solving it. The findings illuminate perspectives and realistic interpretations of the residual attention-based CNN (RACNN) in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprinting. In addition, we study how the precision change of different inertial dimension units may negatively influence the tracking performance, and we implement a solution to the problem of exactness variance. The proposed RACNN model achieved a localization accuracy of 99.9%, which represents a significant improvement over traditional methods such as K-nearest neighbors (KNN) and Bayesian inference. Specifically, the RACNN model reduced the average localization error to 0.35 m, outperforming these traditional methods by approximately 14% to 15% in accuracy. This improvement demonstrates the model's ability to handle complex indoor environments and proves its practical applicability in real-world scenarios.

Automatic pest detection on bean and potato crops by applying neural classifiers

Instrumentation and Artificial Intelligence (AI) recognition techniques were developed for automatic pest detection. This system is based on pest detection and monitoring, and it improves the efficiency of vegetable and fruit farming and food production. This paper presents an automatic pest detection system that applies artificial neural networks. The system automatically detects two defoliating pests on potato and bean crops: Mexican Bean Beetle (MBB) and Colorado Potato Beetle (CPB) in the adult stage. The neural classifiers utilized for the beetle detection are RSC (Random Subspace Classifier) and LIRA (Limited Receptive Area). The MBB images that were employed as inputs to the classifiers were obtained on Mexican plantations. The CPB images were collected from various Internet sources. We compared the results obtained with both classifiers on image databases. The RSC classifier demonstrates the better result for recognition, which is 89%, while LIRA presents a recognition rate of 88%. These results are good for pest detection and can be used for the diagnosis of pest locations in crops. The purpose was to contribute to the development of automatic detection applications based on images of potato and bean plantations. In Mexico and other countries, it is of great importance to solve pest problems in agriculture. We chose insect recognition due to the importance of potato and bean crop production and consumption. Pest detection in the adult phase is of high priority because of the high rate of crop defoliation and destruction. Our automatic pest-detection system can be employed in pest recognition in monitoring activities.

FPGA realization of the LIRA neural classifier

Different types of neural networks can be used to classify images. We propose to apply LIRA (LImited Receptive Area) neural classifier to work with images. To accelerate the neural network functioning we propose a digital implementation of the LIRA neural classifier. We begin with a neuron design, and then continue with the neural network simulation. The advantage of neural network is its parallel structure and possibility of the training. FPGA (Field Programmable Gate Array) allows the implementation of these parallel algorithms in a single device. Speed of classification is one of the most important requirements in adaptive control systems based on computer vision. The contribution of this article is LIRA neural classifier implementation with FPGA for two classes to accelerate the training and recognition processes.

Improved neural classifier for microscrew shape recognition

We propose a neural network based vision system for attending micropieces manufacturing process in micromechanics. The system permits us to recognize the shape of micropieces (screws of 3 mm diameter) in order to get information for controlling and improving the manufacturing process. The neural classifier used for the shape recognition task is termed Limited Receptive Area Grayscale (LIRA Grayscale). The developed vision system has a recognition rate of 98.90%. This work is motivated by the idea of obtaining an automated control system for micromachines. This paper contains a detailed description of the model and learning rules, and discusses future perspectives.

Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces

The limited receptive area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It may be applied in systems that have to recognize position and orientation of complex work pieces during micromechanical device assembly as well as in surface quality inspection systems. The performance of the proposed classifier was tested on a specially created image database with four texture types corresponding to metal surfaces after milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.8% was obtained.

Neural classifier for micro work piece recognition

The aim of this article, is to describe a technical vision system for automation of micromanufacturing and microassembly processes. One of the principal problems is connected with the recognition of work pieces and detection of their positions. For this purpose we use neural classifier named limited receptive area (LIRA) grey scale. This classifier was developed for wide range of image recognition tasks. A special software was designed. We describe some experiments and results of application of LIRA in the recognition of micro work pieces and their positions for automated handling system. The best recognition rate obtained was 94%.

LIRA neural classifier for handwritten digit recognition and visual controlled microassembly

In this paper, limited receptive area neural classifiers are described which are based upon Rosenblatt's perceptron. These networks can be used for both binary and gray-level images. A method is reviewed for greatly expanding the amount of available training data. A training algorithm, based upon that of Rosenblatt, is given. The networks are applied to the handwritten numeral recognition problem, and to task in microassembly image recognition.

Improved method of handwritten digit recognition tested on MNIST database

We have developed a novel neural classifier LImited Receptive Area (LIRA) for the image recognition. The classifier LIRA contains three neuron layers: sensor, associative and output layers. The sensor layer is connected with the associative layer with no modifiable random connections and the associative layer is connected with the output layer with trainable connections. The training process converges sufficiently fast. This classifier does not use floating point and multiplication operations. The classifier was tested on two image databases. The first database is the MNIST database. It contains 60,000 handwritten digit images for the classifier training and 10,000 handwritten digit images for the classifier testing. The second database contains 441 images of the assembly microdevice. The problem under investigation is to recognize the position of the pin relatively to the hole. A random procedure was used for partition of the database to training and testing subsets. There are many results for the MNIST database in the literature. In the best cases, the error rates are 0.7, 0.63 and 0.42%. The classifier LIRA gives error rate of 0.61% as a mean value of three trials. In task of the pin–hole position estimation the classifier LIRA also shows sufficiently good results.