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Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are…
Energy is one of the most important resources in wireless sensor networks. Recently, the mobility of base station has been exploited to preserve the energy. But in event driven networks, the mobility issue is quite different from the…
Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator…
Wireless Sensor networks are used to monitor remote areas. Wireless sensor network can be applied to monitor a facility by considering each camera as sensor nodes. Cameras are used as nodes in a wireless sensor network with a central server…
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our…
In this paper, we present a consensus-based framework for decentralized estimation of deterministic parameters in wireless sensor networks (WSNs). In particular, we propose an optimization algorithm to design (possibly complex) sensor gains…
In this work, we consider the task of target localization using quantized data in Wireless Sensor Networks (WSNs). We propose an energy efficient localization scheme by modeling it as an iterative classification problem. We design coding…
Emerging mobile virtual reality (VR) systems will require to continuously perform complex computer vision tasks on ultra-high-resolution video frames through the execution of deep neural networks (DNNs)-based algorithms. Since…
Wireless sensor networks (WSNs) have enabled continuous monitoring of an area of interest (body, room, region, etc.) while eliminating expensive wired infrastructure. Typically in such applications, wireless sensor nodes report the sensed…
Preventing and detecting intrusions and attacks on wireless networks has become an important and serious challenge. On the other hand, due to the limited resources of wireless nodes, the use of monitoring nodes for permanent monitoring in…
Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different…
Wireless Sensor Network holds a pivotal position and gained a lot of attention from researchers in recent years. Sensor nodes have been used in vast applications such as environment monitoring, security purpose applications, and target…
We propose a simple yet efficient scheme for a set of energy-harvesting sensors to establish secure communication with a common destination (a master node). An eavesdropper attempts to decode the data sent from the sensors to their common…
One of the limitations of wireless sensor nodes is their inherent limited energy resource. Besides maximizing the lifetime of the sensor node, it is preferable to distribute the energy dissipated throughout the wireless sensor network in…
In this paper, we present optimization techniques for WSNs. Our main goal is to minimize the power consumption and latency. We address the problem of minimizing the energy consumption in WSNs including hardware. ZigBee protocol is used to…
This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmitters. We consider an edge device equipped with a camera…
A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. In addition, Wireless Sensor networks is an emerging technology and have great potential to be employed in critical…
Deep Neural Networks (DNNs) can achieve state-of-the-art accuracy in many computer vision tasks, such as object counting. Object counting takes two inputs: an image and an object query and reports the number of occurrences of the queried…
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce the prohibitive computational complexity by a novel attempt…
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with bounding box…