SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using 6 datasets shows that our proposed algorithm can achieve sub-metre accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm show that our algorithm can reduce the error by 10-60 cm for the same compression ratio

Optimization of Planck/LFI on--board data handling

To asses stability against 1/f noise, the Low Frequency Instrument (LFI) onboard the Planck mission will acquire data at a rate much higher than the data rate allowed by its telemetry bandwith of 35.5 kbps. The data are processed by an onboard pipeline, followed onground by a reversing step. This paper illustrates the LFI scientific onboard processing to fit the allowed datarate. This is a lossy process tuned by using a set of 5 parameters Naver, r1, r2, q, O for each of the 44 LFI detectors. The paper quantifies the level of distortion introduced by the onboard processing, EpsilonQ, as a function of these parameters. It describes the method of optimizing the onboard processing chain. The tuning procedure is based on a optimization algorithm applied to unprocessed and uncompressed raw data provided either by simulations, prelaunch tests or data taken from LFI operating in diagnostic mode. All the needed optimization steps are performed by an automated tool, OCA2, which ends with optimized parameters and produces a set of statistical indicators, among them the compression rate Cr and EpsilonQ. For Planck/LFI the requirements are Cr = 2.4 and EpsilonQ <= 10% of the rms of the instrumental white noise. To speedup the process an analytical model is developed that is able to extract most of the relevant information on EpsilonQ and Cr as a function of the signal statistics and the processing parameters. This model will be of interest for the instrument data analysis. The method was applied during ground tests when the instrument was operating in conditions representative of flight. Optimized parameters were obtained and the performance has been verified, the required data rate of 35.5 Kbps has been achieved while keeping EpsilonQ at a level of 3.8% of white noise rms well within the requirements.Comment: 51 pages, 13 fig.s, 3 tables, pdflatex, needs JINST.csl, graphicx, txfonts, rotating; Issue 1.0 10 nov 2009; Sub. to JINST 23Jun09, Accepted 10Nov09, Pub.: 29Dec09; This is a preprint, not the final versio

FPGA-Based Co-processor for Singular Value Array Reconciliation Tomography

This thesis describes a co-processor system that has been designed to accelerate computations associated with Singular Value Array Reconciliation Tomography (SART), a method for locating a wide-band RF source which may be positioned within an indoor environment, where RF propagation characteristics make source localization very challenging. The co-processor system is based on field programmable gate array (FPGA) technology, which offers a low-cost alternative to customized integrated circuits, while still providing the high performance, low power, and small size associated with a custom integrated solution. The system has been developed in VHDL, and implemented on a Virtex-4 SX55 FPGA development platform. The system is easy to use, and may be accessed through a C program or MATLAB script. Compared to a Pentium 4 CPU running at 3 GHz, use of the co-processor system provides a speed-up of about 6 times for the current signal matrix size of 128-by-16. Greater speed-ups may be obtained by using multiple devices in parallel. The system is capable of computing the SART metric to an accuracy of about -145 dB with respect to its true value. This level of accuracy, which is shown to be better than that obtained using single precision floating point arithmetic, allows even relatively weak signals to make a meaningful contribution to the final SART solution

SoC Test: Trends and Recent Standards

The well-known approaching test cost crisis, where semiconductor test costs begin to approach or exceed manufacturing costs has led test engineers to apply new solutions to the problem of testing System-On-Chip (SoC) designs containing multiple IP (Intellectual Property) cores. While it is not yet possible to apply generic test architectures to an IP core within a SoC, the emergence of a number of similar approaches, and the release of new industry standards, such as IEEE 1500 and IEEE 1450.6, may begin to change this situation. This paper looks at these standards and at some techniques currently used by SoC test engineers. An extensive reference list is included, reflecting the purpose of this publication as a review paper

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented