Related papers: Towards a disaster response system based on cognit…
Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a…
During a disaster scenario, situational awareness information, such as location, physical status and images of the surrounding area, is essential for minimizing loss of life, injury, and property damage. Today's handhelds make it easy for…
Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for…
Development of smart spectrum sensing techniques is the most important task in the design of a cognitive radio system which uses the available spectrum efficiently. The adaptive SNR estimation based energy detection technique has the dual…
In the present paper a newer application of Artificial Neural Network (ANN) has been developed i.e., predicting response-function results of electrical-mechanical system through ANN. This method is specially useful to complex systems for…
Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by…
Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods…
We propose an energy-efficient network architecture that consists of ad hoc (mobile) cognitive radios (CRs) and infrastructure wireless sensor nodes. The sensor nodes within communications range of each CR are grouped into a cluster and the…
Recognizing seismic waves immediately is very important for the realization of efficient disaster prevention. Generally these systems consist of a network of seismic detectors that send real time data to a central server. The server…
With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are…
Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called "Adaptive relaying scheme…
In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR) technology can improve spectrum utilization in an…
Natural disasters act as a serious threat globally, requiring effective and efficient disaster management and recovery. This paper focuses on classifying natural disaster images using Convolutional Neural Networks (CNNs). Multiple CNN…
Natural disasters often disrupt communication networks and severely hamper emergency response and disaster management. Existing solutions, such as portable communication units and cloud-based network architectures, have improved disaster…
Although various linear log-distance path loss models have been developed, advanced models are requiring to more accurately and flexibly represent the path loss for complex environments such as the urban area. This letter proposes an…
Next-generation networks are expected to be ultra-dense with a very high peak rate but relatively lower expected traffic per user. For such scenario, existing central controller based resource allocation may incur substantial signaling…
We propose a novel search-and-rescue management method that relies on the aerial deployment of Wireless Sensor Network (WSN) for locating victims after floods. The sensor nodes will collect vital information such as heat signatures for…
The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two…
This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)-based approach is proposed to maximize the secrecy rate of the…
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring. Such applications involve a high demand for…