Biblio

With the rapid and radical evolution of information and communication technology, energy consumption for wireless communication is growing at a staggering rate, especially for wireless multimedia communication. Recently, reducing energy consumption in wireless multimedia communication has attracted increasing attention. In this paper, we propose an energy-efficient wireless image transmission scheme based on adaptive block compressive sensing (ABCS) and SoftCast, which is called ABCS-SoftCast. In ABCS-SoftCast, the compression distortion and transmission distortion are considered in a joint manner, and the energy-distortion model is formulated for each image block. Then, the sampling rate (SR) and power allocation factors of each image block are optimized simultaneously. Comparing with conventional SoftCast scheme, experimental results demonstrate that the energy consumption can be greatly reduced even when the receiving image qualities are approximately the same.

With the increase in signal's bandwidth, the conventional analog to digital converters (ADCs), operating on the basis of Shannon/Nyquist theorem, are forced to work at very high rates leading to low dynamic range and high power consumptions. This paper here tells about one Analog to Information converter developed based on compressive sensing techniques. The high sampling rates, which is the main drawback for ADCs, is being successfully reduced to 4 times lower than the conventional rates. The system is also accompanied with the advantage of low power dissipation.

Non-intrusive load monitoring (NILM) extracts information about how energy is being used in a building from electricity measurements collected at a single location. Obtaining measurements at only one location is attractive because it is inexpensive and convenient, but it can result in large amounts of data from high frequency electrical measurements. Different ways to compress or selectively measure this data are therefore required for practical implementations of NILM. We explore the use of random filtering and random demodulation, techniques that are closely related to compressed sensing, to offer a computationally simple way of compressing the electrical data. We show how these techniques can allow one to reduce the sampling rate of the electricity measurements, while requiring only one sampling channel and allowing accurate NILM performance. Our tests are performed using real measurements of electrical signals from a public data set, thus demonstrating their effectiveness on real appliances and allowing for reproducibility and comparison with other data management strategies for NILM.