Search results for "Networking & Telecommunications"

Demo - Dynamic Adaptations of WiFi Channel Widths Without TX/RX Coordination

2017

Most modern standards for wireless communications support physical layer adaptations, in terms of dynamic selection of channel central frequency, transmission power, modulation format, etc., in order to increase link robustness under time-varying propagation and interference conditions. In this demo, we demonstrate that another powerful solution for extending physical layer flexibility in OFDM-based technologies is the dynamic adaptation of the channel width. Although some standards already define the possibility of utilizing multiple channel widths (e.g. 20MHz, 10MHz, 5MHz for IEEE 802.11a standards), such an utilization is limited to a static configuration of a value defined during the ne…

Stochastic Graph Filtering Under Asymmetric Links in Wireless Sensor Networks

2018

Wireless sensor networks (WSN s) are often characterized by random and asymmetric packet losses due to the wireless medium, leading to network topologies that can be modeled as random, time-varying and directed graphs. Most of existing works related to graph filtering in the context of WSNs assume that the probability of delivering an information from one node to a neighbor node is the same as in the reverse direction. This assumption is not realistic due to the typical link asymmetry in WSNs caused by interferences and background noise. In this work, we analyze the problem of applying stochastic graph filtering over random time-varying asymmetric network topologies. We show that it is poss…

Channel Modeling and System Concepts for Future Terahertz Communications: Getting Ready for Advances Beyond 5G

2020

Future terahertz (THz) communications will utilize the frequency spectrum around 300 GHz. This technology provides a promising solution for future high-data-rate communications due to the high available bandwidth. However, the unfavorable channel characteristics are a major barrier for the realization of THz communications. To address the challenges caused by the unfavorable propagation conditions, the world?s first THz communication standard includes a channel-modeling document (CMD).

A User-Centered Approach to Digital Household Risk Management

2020

Internet of Things (IoT) is expected to become as common as electricity (OECD 2016) and there is a high probability for connected homes to become central parts of critical societal. IoT technologies might access, manage and record sensitive data about citizens and, as they become more and more pervasive, unintended data breaches reports increase every week. However, most of the tools designed to protect users’ privacy and personal data on IoT devices fail to contemplate the experience of persons with disabilities, elderly and other vulnerable categories of people. As a consequence, they are forced to rely on the help of family members or other related persons with technical skills, as frequ…

Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring

2021

The paper presents the design and implementation of an electromagnetic field monitoring sensor for the measurement of the Terrestrial Truncked Radio (TETRA) signals using low-cost software defined radio (SDR) platforms. The sensor includes: an SDR platform, a Global Positioning System (GPS) module and a hardware control module. Several SDR platforms having different resolutions of the analog–digital converters were tested in the first phase. The control module was implemented in two variants: a fixed one, using a laptop, and a mobile one, using a Raspberry Pi. The tests demonstrate the following achieved performances: instantaneous acquisition band of 5.12 MHz

Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis

2019

Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical polyadic tensor decomposition (GLCPTD) model that is well suited to exploiting the linking nature in multi-block tensor analysis. To address GLCPTD model, an efficient algorithm based on hierarchical alternating least squa res (HALS) method is proposed, termed as GLCPTD-HALS algorithm. The proposed algorithm enables the simultaneous extraction of common components, individual components and core tensors from tensor blocks. Simulation experiments of synthetic EEG data analysi…

Structure and Chemical Bonds in Black Ti(C, N, O) Thin Films

2010

A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

2020

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns…

Multi-objective optimization for computation offloading in mobile-edge computing

2017

Mobile-edge cloud computing is a new cloud platform to provide pervasive and agile computation augmenting services for mobile devices (MDs) at anytime and anywhere by endowing ubiquitous radio access networks with computing capabilities. Although offloading computations to the cloud can reduce energy consumption at the MDs, it may also incur a larger execution delay. Usually the MDs have to pay cloud resource they used. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and price cost of offloading process in a mobile-edge cloud system. Specifically, both wireless transmission and computing capabilities are explicitly and jointly co…

One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals

2021

Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…