Search results for "Networking & Telecommunications"
showing 10 items of 962 documents
5G IoT System for Real-Time Psycho-Acoustic Soundscape Monitoring in Smart Cities With Dynamic Computational Offloading to the Edge
2021
Environmental noise monitoring for smart cities need to be as much efficient as possible in order to mitigate its significant impact in the health of their inhabitants. 5G Internet of Things (IoT) systems offer a big opportunity to offload the computation from the sensor nodes, since it provides a series of new concepts for dynamic computing that the previous technologies did not offer. In this article, a complete 5G IoT system for psycho-acoustic monitoring has been designed and implemented using different options for offloading computation to different parts of the system. This offloading has been done by developing different functional splittings of the psycho-acoustic metrics algorithms…
Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications
2018
Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharp…
Routing Algorithm for Maximizing Lifetime of Wireless Sensor Network for Broadcast Transmission
2018
In the article we discuss solutions of the maximum lifetime broadcasting problem in wireless sensor networks. Due to limited energy resources of the network nodes to find an optimal transmission route of the broadcasted data we minimize the maximum energy consumed by the nodes. We give an analytical solution of the problem in one dimensional regular sensor network for the point-to-point and point-to-multipoint data transmission scheme. We show that in such a network, when the cost of data transmission is a polynomial function of distance between transmitter and receiver, there exist solutions with an equal energy, i.e., all nodes of the network consume the same amount of energy. We assume t…
Reducing complexity in H.264/AVC motion estimation by using a GPU
2011
H.264/AVC applies a complex mode decision technique that has high computational complexity in order to reduce the temporal redundancies of video sequences. Several algorithms have been proposed in the literature in recent years with the aim of accelerating this part of the encoding process. Recently, with the emergence of many-core processors or accelerators, a new approach can be adopted for reducing the complexity of the H.264/AVC encoding algorithm. This paper focuses on reducing the inter prediction complexity adopted in H.264/AVC and proposes a GPU-based implementation using CUDA. Experimental results show that the proposed approach reduces the complexity by as much as 99% (100x of spe…
Digital background calibration algorithm and its FPGA implementation for timing mismatch correction of time-interleaved ADC
2019
Sample time error can degrade the performance of time-interleaved analog to digital converters (TIADCs). A fully digital background algorithm is presented in this paper to estimate and correct the timing mismatch errors between four interleaved channels, together with its hardware implementation. The proposed algorithm provides low computation burden and high performance. It is based on the simplified representation of the coefficients of the Lagrange interpolator. Simulation results show that it can suppress error tones in all of the Nyquist band. Results show that, for a four-channel TIADC with 10-bit resolution, the proposed algorithm improves the signal to noise and distortion ratio (SN…
LoneStar RAID
2016
The need for huge storage archives rises with the ever growing creation of data. With today’s big data and data analytics applications, some of these huge archives become active in the sense that all stored data can be accessed at any time. Running and evolving these archives is a constant tradeoff between performance, capacity, and price. We present the LoneStar RAID, a disk-based storage architecture, which focuses on high reliability, low energy consumption, and cheap reads. It is designed for MAID systems with up to hundreds of disk drives per server and is optimized for “write once, read sometimes” workloads. We use dedicated data and parity disks, and export the data disks as individu…
VLPZ: The Vehicular Location Privacy Zone
2016
International audience; One of the key challenges in the success of vehicular ad hoc networks (VANETs) is to consider the location privacy of drivers. Although, the pseudonym changing approach is suggested by standardization development organizations such as IEEE and ETSI, the development of an effective pseudonym changing strategy is still an open issue. The existing pseudonym changing strategies are either not effective to protect against the pseudonyms linking attacks or can have a negative impact on the VANETs’ applications. To address these issues, this paper proposes a new pseudonym changing strategy based on the Vehicular Location Privacy Zone (VLPZ), which is a roadside infrastructu…
Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture
2020
Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its differ…
Importance of the Window Function Choice for the Predictive Modelling of Memristors
2021
Window functions are widely employed in memristor models to restrict the changes of the internal state variables to specified intervals. Here, we show that the actual choice of window function is of significant importance for the predictive modelling of memristors. Using a recently formulated theory of memristor attractors, we demonstrate that whether stable fixed points exist depends on the type of window function used in the model. Our main findings are formulated in terms of two memristor attractor theorems, which apply to broad classes of memristor models. As an example of our findings, we predict the existence of stable fixed points in Biolek window function memristors and their absenc…
Joint Graph Learning and Signal Recovery via Kalman Filter for Multivariate Auto-Regressive Processes
2018
In this paper, an adaptive Kalman filter algorithm is proposed for simultaneous graph topology learning and graph signal recovery from noisy time series. Each time series corresponds to one node of the graph and underlying graph edges express the causality among nodes. We assume that graph signals are generated via a multivariate auto-regressive processes (MAR), generated by an innovation noise and graph weight matrices. Then we relate the state transition matrix of Kalman filter to the graph weight matrices since both of them can play the role of signal propagation and transition. Our proposed Kalman filter for MAR processes, called KF-MAR, runs three main steps; prediction, update, and le…