Detection of multicode STBC signals in frequency selective fading

Multicode operation in space-time block coded (STBC) multiple input multiple output (MIMO) systems can provide additional degrees of freedom in code domain to achieve high data rates. In such multicode STBC systems, the receiver experiences code domain interference (CDI) in frequency selective fading. In this paper, we propose a linear parallel interference cancellation (LPIC) approach to cancel the CDI in multicode STBC in frequency selective fading. The proposed detector first performs LPIC followed by STBC decoding. We evaluate the bit error performance of the detector and show that it effectively cancels the CDI and achieves improved error performance. Our results further illustrate how the combined effect of interference cancellation, transmit diversity, and RAKE diversity affect the bit error performance of the system.

False Error Study of On-line Soft Error Detection Mechanisms

With technology scaling, vulnerability to soft errors in random logic is increasing. There is a need for on-line error detection and protection for logic gates even at sea level. The error checker is the key element for an on-line detection mechanism. We compare three different checkers for error detection from the point of view of area, power and false error detection rates. We find that the double sampling checker (used in Razor), is the simplest and most area and power efficient, but suffers from very high false detection rates of 1.15 times the actual error rates. We also find that the alternate approaches of triple sampling and integrate and sample method (I&S) can be designed to have zero false detection rates, but at an increased area, power and implementation complexity. The triple sampling method has about 1.74 times the area and twice the power as compared to the Double Sampling method and also needs a complex clock generation scheme. The I&S method needs about 16% more power with 0.58 times the area as double sampling, but comes with more stringent implementation constraints as it requires detection of small voltage swings.

On the Average Case Communication Complexity for Detection in Sensor Networks

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space viewpoint is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces $\mathcal{S_I}$ and $\mathcal{S_C}$ and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating $\mathcal{S_I}$ and $\mathcal{S_C}$ is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. The average case CC of the relevant greater-than (GT) function is characterized within two bits. In the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm.

Optimal sleep-wake scheduling for quickest intrusion detection using sensor networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

Thiourea based novel chromogenic sensor for selective detection of fluoride and cyanide anions in organic and aqueous media

Novel chromogenic thiourea based sensors 4,4'-bis-[3-(4-nitrophenyl) thiourea] diphenyl ether 1 and 4,4'-bis-[3-(4-nitrophenyl) thiourea] diphenyl methane 2 having nitrophenyl group as signaling unit have been synthesized and characterized by spectroscopic techniques and X-ray crystallography. The both sensors show visual detection, UV-vis and NMR spectral changes in presence of fluoride and cyanide anions in organic solvent as well as in aqueous medium. The absorption spectra indicated the formation of complex between host and guest is in 1:2 stoichiometric ratios. (C) 2010 Elsevier B.V. All rights reserved.

Multiple excitation nano-spot generation and confocal detection for far-field microscopy

An imaging technique is developed for the controlled generation of multiple excitation nano-spots for far-field microscopy. The system point spread function (PSF) is obtained by interfering two counter-propagating extended depth-of-focus PSF (DoF-PSF), resulting in highly localized multiple excitation spots along the optical axis. The technique permits (1) simultaneous excitation of multiple planes in the specimen; (2) control of the number of spots by confocal detection; and (3) overcoming the point-by-point based excitation. Fluorescence detection from the excitation spots can be efficiently achieved by Z-scanning the detector/pinhole assembly. The technique complements most of the bioimaging techniques and may find potential application in high resolution fluorescence microscopy and nanoscale imaging.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Mesh Processing for Computerized Facial Anthropometry

Understanding of the shape and size of different features of the human body from scanned data is necessary for automated design and evaluation of product ergonomics. In this paper, a computational framework is presented for automatic detection and recognition of important facial feature regions, from scanned head and shoulder polyhedral models. A noise tolerant methodology is proposed using discrete curvature computations, band-pass filtering, and morphological operations for isolation of the primary feature regions of the face, namely, the eyes, nose, and mouth. Spatial disposition of the critical points of these isolated feature regions is analyzed for the recognition of these critical points as the standard landmarks associated with the primary facial features. A number of clinically identified landmarks lie on the facial midline. An efficient algorithm for detection and processing of the midline, using a point sampling technique, is also presented. The results obtained using data of more than 20 subjects are verified through visualization and physical measurements. A color based and triangle skewness based schemes for isolation of geometrically nonprominent features and ear region are also presented. [DOI: 10.1115/1.3330420]

Novel fluorescence detection technique for non-contact temperature sensing in microchip PCR

DNA amplification using Polymerase Chain Reaction (PCR) in a small volume is used in Lab-on-a-chip systems involving DNA manipulation. For few microliters of volume of liquid, it becomes difficult to measure and monitor the thermal profile accurately and reproducibly, which is an essential requirement for successful amplification. Conventional temperature sensors are either not biocompatible or too large and hence positioned away from the liquid leading to calibration errors. In this work we present a fluorescence based detection technique that is completely biocompatible and measures directly the liquid temperature. PCR is demonstrated in a 3 ILL silicon-glass microfabricated device using non-contact induction heating whose temperature is controlled using fluorescence feedback from SYBR green I dye molecules intercalated within sensor DNA. The performance is compared with temperature feedback using a thermocouple sensor. Melting curve followed by gel electrophoresis is used to confirm product specificity after the PCR cycles. (c) 2007 Elsevier B.V. All rights reserved.

Self-Assembly of a Nanoscopic Prism via a New Organometallic Pt3 Acceptor and Its Fluorescent Detection of Nitroaromatics

A nanoscale-sized cage with a trigonal prismatic shape is prepared by coordination-driven self-assembly of a predesigned organometallic Pt-3 acceptor with an organic clip-type ligand. This trigonal prism is fluorescent and undergoes efficient fluorescence quenching by nitroaromatics, which are the chemical signatures of many explosives.

Performance of RT-PCR-ELISA for the detection of peste des petits ruminants virus

A highly sensitive and specific reverse transcription polymerase chain reaction enzyme linked immunosorbent assay (RT-PCR-ELISA) was developed for the objective detection of nucleoprotein (N) gene of peste des petits ruminants (PPR) virus from field outbreaks or experimentally infected sheep. Two primers (IndF and Np4) and one probe (Sp3) available or designed for the amplification/probing of the 'N' gene of PPR virus, were chosen for labeling and use in RT-PCR-ELISA based on highest analytical sensitivity of detection of infective virus or N-gene containing recombinant plasmid, higher nucleotide homology at the primer binding sites of the 'N' gene sequences available and the ability to amplify PPR viral genome from different sources of samples. RT-PCR was performed with unlabeled IndF and Np4 digoxigenin labeled primers followed by a microplate hybridization probe reaction with biotin labeled Sp3 probe. RT-PCR-ELISA was found to be 10-fold more sensitive than the conventional RT-PCR followed by agarose gel based detection of PCR product. Based on the Mean (mean +/- 3S.D.) optical density (OD) values of 47 RT-PCR negative samples, OD values above 0.306 were considered positive in RT-PCR-ELISA. A total of 82 oculo-nasal swabs and tissue samples from suspected PPR cases were analyzed by RT-PCR and RT-PCR-ELISA, which revealed 54.87 and 58.54% positivity, respectively. From an experimentally infected sheep, both RT-PCR and RT-PCR-ELISA could detect the virus from 6 days post-infection up to 9 days in oculo-nasal swabs. On post-mortem, PPR viral genome was detected in spleen, lymph node, lung, heart and liver. The correlation co-efficient between RT-PCR-ELISA OD values and either TCID50 of virus or molecules of DNA was 0.622 and 0.657, respectively. The advantages of RT-PCR-ELISA over the conventional agarose gel based detection of RT-PCR products are discussed. (c) 2006 Elsevier B.V. All rights reserved.