A Deep Trash Classification Model on Raspberry Pi 4

Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloading of garbage dis-posal systems and efficiently promote recycling activities;thus,development of such a classification system is topical and urgent.This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time.An image dataset isfirst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classification module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classification system.This proves the efficiency and high applicability of the proposed system.

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

This article presents a new scheme for dynamic data optimization in IoT(Internet of Things)-assisted sensor networks.The various components of IoT assisted cloud platform are discussed.In addition,a new architecture for IoT assisted sensor networks is presented.Further,a model for data optimization in IoT assisted sensor networks is proposed.A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization(MIDDO)algorithm for IoT assisted sensor network is proposed in this research.The proposed algorithm considers every node data and utilized membership function for the optimized data allocation.The proposed framework is compared with two stage optimization,dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio,coverage ratio and sensing error.Data optimization was performed based on the availability of cloud resource,sensor energy,data flow volume and the centroid of each state.It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%,reliability ratio of 94.74%,coverage ratio of 85.75%and sensing error of 0.154.