Many commercial database systems maintain histograms to summarize the contents of large relations and permit efficient estimation of query result sizes for use in query optimizers. Delaying the propagation of database updates to the histogram often introduces errors in the estimation. This paper

attention to solve this problem due to its extremely low sampling rate. In this paper, we investigate the quantization effect in the Compressed Sensing architecture. We point out that quanti-zation configuration is a critical factor towards the energy efficiency concern of the entire CS architecture

, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and ab-normal ECG signals. Our simulation results based on two proposed algorithms illustrate 25 % decrease in sampling-rate and a good level

Video sensors with embedded compression offer significant energy savings in transmission but incur energy losses in the complexity of the encoder. Energy efficient video compression architectures for CMOS image sensors with focal-plane change detection are presented and analyzed. The compression

Embedded Systems group:

Abstract—Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because

is used. We introduce a low-power pseudo-random frequency-hopping MAC protocol which is specifically characterized as a duty cycled asynchronous sender-initiated LPL style protocol. We illustrate two flavors of the protocol; one that does not use any dedicated channel and another which allows dedicated

a real-time system for doing so as a culmination of an exploration of novel methods for 3D capture. First, two related systems for 3D imaging are presented, both relying on modeling and algorithms from parametric sampling theory and compressed sensing. Then, a separate system for 3D localization

Abstract—Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression

Abstract. In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse