No abstract

Face detection and recognition are being studied extensively for their vast applications in security, biometrics, healthcare, and marketing. As a step towards presenting an almost accurate solution to the problem in hand, this paper proposes a face detection and face recognition pipeline - face detection and recognition embedNet (FDREnet). The proposed FDREnet involves face detection through histogram of oriented gradients and uses Siamese technique and contrastive loss to train a deep learning architecture (EmbedNet). The approach allows the EmbedNet to learn how to distinguish facial features apart from recognizing them. This flexibility in learning due to contrastive loss accounts for better accuracy than using traditional deep learning losses. The dataset’s embeddings produced from the trained FDREnet result accuracy of 98.03%, 99.57% and 99.39% for face94, face95, and face96 datasets respectively through SVM clustering. Accuracy of 97.83%, 99.57%, and 99.39% was observed for face94, face95, and face96 datasets respectively through KNN clustering.

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

WSNs can be considered a distributed control system designed to react to sensor information with an effective and timely action. For this reason, in WSNs it is important to provide real-time coordination and communication to guarantee timely execution of the right actions. In this paper a new communication protocol RRRT to support robust real-time and reliable event data delivery with minimum energy consumption and with congestion avoidance in WSNs is proposed. The proposed protocol uses the fault tolerant optimal path for data delivery. The proposed solution dynamically adjust their protocol configurations to adapt to the heterogeneous characteristics of WSNs. Specifically, the interactions between contention resolution and congestion control mechanisms as well as the physical layer effects in WSNs are investigated.

Energy being the very key concern area with sensor networks, so the main focus lies in developing a mechanism to increase the lifetime of a sensor network by energy balancing. To achieve energy balancing and maximizing network lifetime we use an idea of clustering and dividing the whole network into different clusters. In this paper we propose a dynamic cluster formation method where clusters are refreshed periodically based on residual energy, distance and cost.Refreshing clustering minimizes workload of any single node and in turn enhances the energy conservation. Sleep and wait methodology is applied to the proposed protocol to enhance the network lifetime by turning the nodes on and off according to their duties. The node that has some data to be transmitted is in on state and after forwarding its data to the cluster head it changes its state to off which saves the energy of entire network. Simulations have been done using MAT lab. Simulation results prove the betterment of our proposed method over the existing Leach protocol.