64,914 research outputs found
Benchmarking CPUs and GPUs on embedded platforms for software receiver usage
Smartphones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring supercomputing technology into your pocket (or into our embedded devices). This can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light onto recent embedded processor developments, benchmarks Fast Fourier Transform (FFT) and correlation algorithms on representative embedded platforms and relates the results to the use in GNSS software radios. The use of embedded CPUs for signal tracking seems to be straight forward, but more research is required to fully achieve the nominal peak performance of an embedded GPU for FFT computation. Also the electrical power consumption is measured in certain load levels.Peer ReviewedPostprint (published version
mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks
Knowledge of the battery lifetime of the wireless sensor network is important for many situations,
such as in evaluation of the location of nodes or the estimation of the connectivity,
along time, between devices. However, experimental evaluation is a very time-consuming
task. It depends on many factors, such as the use of the radio transceiver or the distance
between nodes. Simulations reduce considerably this time. They allow the evaluation of
the network behavior before its deployment. This article presents a simulation tool which
helps developers to obtain information about battery state. This simulator extends the
well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications
using an accurate model of the battery consumption and its relation to the radio power
transmission. Although an specific indoor scenario is used in testing of simulation, the simulator
is not limited to this environment. It is possible to work in outdoor scenarios too.
Experimental results validate the proposed model.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570
Software for Wearable Devices: Challenges and Opportunities
Wearable devices are a new form of mobile computer system that provides
exclusive and user-personalized services. Wearable devices bring new issues and
challenges to computer science and technology. This paper summarizes the
development process and the categories of wearable devices. In addition, we
present new key issues arising in aspects of wearable devices, including
operating systems, database management system, network communication protocol,
application development platform, privacy and security, energy consumption,
human-computer interaction, software engineering, and big data.Comment: 6 pages, 1 figure, for Compsac 201
Green multimedia: informing people of their carbon footprint through two simple sensors
In this work we discuss a new, but highly relevant, topic to the multimedia community; systems to inform individuals of their carbon footprint, which could ultimately effect change in community carbon footprint-related activities. The reduction of carbon emissions is now an important policy driver of many governments, and one of the major areas of focus is in reducing the energy demand from the consumers i.e. all of us individually.
In terms of CO2 generated from energy consumption, there are three predominant factors, namely electricity usage, thermal related costs, and transport usage. Standard home electricity and heating sensors can be used to measure the former two aspects, and in this paper we evaluate a novel technique to estimate an individual's transport-related carbon emissions through the use of a simple wearable accelerometer.
We investigate how providing this novel estimation of transport-related carbon emissions through an interactive web site and mobile phone app engages a set of users in becoming more aware of their carbon emissions. Our evaluations involve a group of 6 users collecting 25 million accelerometer readings and 12.5 million power readings vs. a control group of 16 users collecting 29.7 million power readings
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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