Mapping of large scale 158 mu m [CII] line emission: Orion A

We present the first results of an observational programme undertaken to map the fine structure line emission of singly ionized carbon ([ CII] 157 : 7409 mum) over extended regions using a Fabry Perot spectrometer newly installed at the focal plane of a 100 cm balloon- borne far- infrared telescope. This new combination of instruments has a velocity resolution of similar to 200 km s(-1) and an angular resolution of 1.'5. During the first flight, an area of 30' x 15' in Orion A was mapped. These observations extend over a larger area than previous observations, the map is fully sampled and the spectral scanning method used enables reliable estimation of the continuum emission at frequencies adjacent to the [ CII] line. The total [ CII] line luminosity, calculated by considering up to 20% of the maximum line intensity is 0.04% of the luminosity of the far- infrared continuum. We have compared the [ CII] intensity distribution with the velocity- integrated intensity distributions of (CO)-C-13(1- 0), CI(1- 0) and CO( 3- 2) from the literature. Comparison of the [ CII], [ CI] and the radio continuum intensity distributions indicates that the largescale [ CII] emission originates mainly from the neutral gas, except at the position of M 43, where no [ CI] emission corresponding to the [ CII] emission is seen. Substantial part of the [ CII] emission from here originates from the ionized gas. The observed line intensities and ratios have been analyzed using the PDR models by Kaufman et al. ( 1999) to derive the incident UV flux and volume density at a few selected positions. The models reproduce the observations reasonably well at most positions excepting the [ CII] peak ( which coincides with the position of theta(1) Ori C). Possible reason for the failure could be the simplifying assumption of a homogeneous plane parallel slab in place of a more complicated geometry.

GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Hardware implementation of 4x4 DCT/Quantization block using multiplication and error-free algorithm

The 4ÃÂ4 discrete cosine transform is one of the most important building blocks for the emerging video coding standard, viz. H.264. The conventional implementation does some approximation to the transform matrix elements to facilitate integer arithmetic, for which hardware is suitably prepared. Though the transform coding does not involve any multiplications, quantization process requires sixteen 16-bit multiplications. The algorithm used here eliminates the process of approximation in transform coding and multiplication in the quantization process, by usage of algebraic integer coding. We propose an area-efficient implementation of the transform and quantization blocks based on the algebraic integer coding. The designs were synthesized with 90 nm TSMC CMOS technology and were also implemented on a Xilinx FPGA. The gate counts and throughput achievable in this case are 7000 and 125 Msamples/sec.

FREP: A Soft-Error Resilient Pipelined RISC Architecture

Soft error has become one of the major areas of attention with the device scaling and large scale integration. Lot of variants for superscalar architecture were proposed with focus on program re-execution, thread re-execution and instruction re-execution. In this paper we proposed a fault tolerant micro-architecture of pipelined RISC. The proposed architecture, Floating Resources Extended pipeline (FREP), re-executes the instructions using extended pipeline stages. The instructions are re-executed by hybrid architecture with a suitable combination of space and time redundancy.

Distributed Source Coding for Sensor Data Model and Estimation of Cluster Head Errors Using Bayesian and K-Near Neighborhood Classifiers in Deployment of Dense Wireless Sensor Networks

The lifetime calculation of large dense sensor networks with fixed energy resources and the remaining residual energy have shown that for a constant energy resource in a sensor network the fault rate at the cluster head is network size invariant when using the network layer with no MAC losses.Even after increasing the battery capacities in the nodes the total lifetime does not increase after a max limit of 8 times. As this is a serious limitation lots of research has been done at the MAC layer which allows to adapt to the specific connectivity, traffic and channel polling needs for sensor networks. There have been lots of MAC protocols which allow to control the channel polling of new radios which are available to sensor nodes to communicate. This further reduces the communication overhead by idling and sleep scheduling thus extending the lifetime of the monitoring application. We address the two issues which effects the distributed characteristics and performance of connected MAC nodes. (1) To determine the theoretical minimum rate based on joint coding for a correlated data source at the singlehop, (2a) to estimate cluster head errors using Bayesian rule for routing using persistence clustering when node densities are the same and stored using prior probability at the network layer, (2b) to estimate the upper bound of routing errors when using passive clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori. In this paper we evaluate many MAC based sensor network protocols and study the effects on sensor network lifetime. A renewable energy MAC routing protocol is designed when the probabilities of active nodes are not known a priori. From theoretical derivations we show that for a Bayesian rule with known class densities of omega1, omega2 with expected error P* is bounded by max error rate of P=2P* for single-hop. We study the effects of energy losses using cross-layer simulation of - large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications due to unknown node densities. The simulation results show that even though the lifetime is comparable the expected Bayesian posterior probability error bound is close or higher than Pges2P*.

Modified Stability Checking for On-Line Error Detection

The paper propose a unified error detection technique, based on stability checking, for on-line detection of delay, crosstalk and transient faults in combinational circuits and SEUs in sequential elements. The proposed method, called modified stability checking (MSC), overcomes the limitations of the earlier stability checking methods. The paper also proposed a novel checker circuit to realize this scheme. The checker is self-checking for a wide set of realistic internal faults including transient faults. Extensive circuit simulations have been done to characterize the checker circuit. A prototype checker circuit for a 1mm2 standard cell array has been implemented in a 0.13mum process.

Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.

Digital Deadbeat Control Tuning for dc-dc Converters Using Error Correlation

This letter proposes a simple tuning algorithm for digital deadbeat control based on error correlation. By injecting a square-wave reference input and calculating the correlation of the control error, a gain correction for deadbeat control is obtained. The proposed solution is simple, it requires a short tuning time, and it is suitable for different DC-DC converter topologies. Simulation and experimental results on synchronous buck converters confirm the properties of the proposed tuning algorithm.