Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

Intelligent MRI Room Design Using Visible Light Communication with Range Augmentation

Radio waves and strong magneticfields are used by Magnetic Reso-nance Imaging(MRI)scanners to detect tumours,wounds and visualize detailed images of the human body.Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images.The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner.Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality.RF shielding techniques are complex and lead to RF leak-age.VLC(Visible light Communication)is an emerging and efficient technology to avoid RF interference near MRI scanners.Range augmentation with power conservation of the LED is a big challenge in existing VLC systems.The major objective of the proposed work is to develop an intelligent-MRI room design without RF interference using visible light communication and enhance the distance between VLC transmitter and VLC receiver.In this paper,it is proposed to implement VLC using On-Off keying modulation and enhance distance using large active area photodiodes with Automatic Gain Control Circuit(AGC)using software and hardware.The performance of the proposed intelligent MRI-VLC system is analyzed by calculating Bit Error Rate at an inclined distance of 50 cm away from line of sight of the LED.The Experimental results showed that the maximum distance achieved was 400 cm at Bit Error Rate(BER)of 1.5×10^(-5).

Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot

This paper describes a person identifcation method for a mobile robot which performs specifc person following under dynamic complicated environments like a school canteen where many persons exist.We propose a distance-dependent appearance model which is based on scale-invariant feature transform（SIFT） feature.SIFT is a powerful image feature that is invariant to scale and rotation in the image plane and also robust to changes of lighting condition.However,the feature is weak against afne transformations and the identifcation power will thus be degraded when the pose of a person changes largely.We therefore use a set of images taken from various directions to cope with pose changes.Moreover,the number of SIFT feature matches between the model and an input image will decrease as the person becomes farther away from the camera.Therefore,we also use a distance-dependent threshold.The person following experiment was conducted using an actual mobile robot,and the quality assessment of person identifcation was performed.

A novel intelligent chromo capsule endoscope for the diagnosis of neoplastic lesions in the gastrointestinal tract

Background:Chromoendoscopy has not been fully integrated into capsule endoscopy.This study aimded to develop and validate a novel intelligent chromo capsule endoscope(ICCE).Methods:The ICCE has two modes:a white-light imaging(WLI)mode and an intelligent chromo imaging(ICI)mode.The performance of the ICCE in observing colors,animal tissues,and early gastrointestinal(GI)neoplastic lesions in humans was evaluated.Images captured by the ICCE were analysed using variance of Laplacian(VoL)values or image contrast evaluation.Results:For color observation,conventional narrow-band imaging endoscopes and the ICI mode of the ICCE have similar spectral distributions.Compared with the WLI mode,the ICI mode had significantly higher VoL values for animal tissues(2.15461.044 vs 3.80061.491,P=0.003),gastric precancerous lesions and early gastric cancers(2.24260.162 vs 6.64260.919,P<0.001),and colon tumors(3.89661.430 vs 11.88267.663,P<0.001),and significantly higher contrast for differentiating tumor and non-tumor areas(0.06960.046 vs 0.14460.076,P=0.005).More importantly,the sensitivity,specificity,and accuracy of the ICI mode for early GI tumors were 95.83%,91.67%,and 94.64%,respectively,which were significantly higher than the values of the WLI mode(78.33%[P<0.001],77.08%[P=0.01],and 77.98%[P<0.001],respectively).Conclusions:We successfully integrated ICI into the capsule endoscope.The ICCE is an innovative and useful tool for differential diagnosis based on contrast-enhanced images and thus has great potential as a superior diagnostic tool for early GI tumor detection.