Data Traffic Reduction with Compressed Sensing in an AIoT System

To provide Artificial Intelligence(AI)services such as object detection,Internet of Things(IoT)sensor devices should be able to send a large amount of data such as images and videos.However,this inevitably causes IoT networks to be severely overloaded.In this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object detection.It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing(CS)rate,an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing,and a YOLOv5 deep learning function for object detection,and an IoT server.By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device,we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices.In addition,we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall,precision,mAP50,and mAP,and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate.Consequently,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

Fingerprint-Based Millimeter-Wave Beam Selection for Interference Mitigation in Beamspace Multi-User MIMO Communications

Millimeter-wave communications are suitable for application to massive multiple-input multiple-output systems in order to satisfy the ever-growing data traffic demands of the next-generation wireless communication.However,their practical deployment is hindered by the high cost of complex hardware,such as radio frequency(RF)chains.To this end,operation in the beamspace domain,through beam selection,is a viable solution.Generally,the conventional beam selection schemes focus on the feedback and exhaustive search techniques.In addition,since the same beam in the beamspace may be assigned to a different user,conventional beam selection schemes suffer serious multi-user interference.In addition,some RF chains may be wasted,since they do not contribute to the sum-rate performance.Thus,a fingerprint-based beam selection scheme is proposed to solve these problems.The proposed scheme conducts offline group-based fingerprint database construction and online beam selection to mitigate multi-user interference.In the offline phase,the contributing users with the same best beam are grouped.After grouping,a fingerprint database is created for each group.In the online phase,beam selection is performed for purposes of interference mitigation using the information contained in the group-based fingerprint database.The simulation results confirm that the proposed beam selection scheme can achieve a signal-to-interference-plus-noise ratio and sum-rate performance which is close to those of a fully digital system,and having much higher energy efficiency.