Joint data detection and dominant singular mode estimation in time varying reciprocal MIMO systems

This paper proposes an algorithm for joint data detection and tracking of the dominant singular mode of a time varying channel at the transmitter and receiver of a time division duplex multiple input multiple output beamforming system. The method proposed is a modified expectation maximization algorithm which utilizes an initial estimate to track the dominant modes of the channel at the transmitter and the receiver blindly; and simultaneously detects the un known data. Furthermore, the estimates are constrained to be within a confidence interval of the previous estimate in order to improve the tracking performance and mitigate the effect of error propagation. Monte-Carlo simulation results of the symbol error rate and the mean square inner product between the estimated and the true singular vector are plotted to show the performance benefits offered by the proposed method compared to existing techniques.

Robust GNSS signal detection in the presence of navigation data bits

This paper considers the problem of weak signal detection in the presence of navigation data bits for Global Navigation Satellite System (GNSS) receivers. Typically, a set of partial coherent integration outputs are non-coherently accumulated to combat the effects of model uncertainties such as the presence of navigation data-bits and/or frequency uncertainty, resulting in a sub-optimal test statistic. In this work, the test-statistic for weak signal detection is derived in the presence of navigation data-bits from the likelihood ratio. It is highlighted that averaging the likelihood ratio based test-statistic over the prior distributions of the unknown data bits and the carrier phase uncertainty leads to the conventional Post Detection Integration (PDI) technique for detection. To improve the performance in the presence of model uncertainties, a novel cyclostationarity based sub-optimal PDI technique is proposed. The test statistic is analytically characterized, and shown to be robust to the presence of navigation data-bits, frequency, phase and noise uncertainties. Monte Carlo simulation results illustrate the validity of the theoretical results and the superior performance offered by the proposed detector in the presence of model uncertainties.

A hybrid RTS-BP algorithm for improved detection of large-MIMO M-QAM signals

Low-complexity near-optimal detection of large-MIMO signals has attracted recent research. Recently, we proposed a local neighborhood search algorithm, namely reactive tabu search (RTS) algorithm, as well as a factor-graph based belief propagation (BP) algorithm for low-complexity large-MIMO detection. The motivation for the present work arises from the following two observations on the above two algorithms: i) Although RTS achieved close to optimal performance for 4-QAM in large dimensions, significant performance improvement was still possible for higher-order QAM (e.g., 16-, 64-QAM). ii) BP also achieved near-optimal performance for large dimensions, but only for {±1} alphabet. In this paper, we improve the large-MIMO detection performance of higher-order QAM signals by using a hybrid algorithm that employs RTS and BP. In particular, motivated by the observation that when a detection error occurs at the RTS output, the least significant bits (LSB) of the symbols are mostly in error, we propose to first reconstruct and cancel the interference due to bits other than LSBs at the RTS output and feed the interference cancelled received signal to the BP algorithm to improve the reliability of the LSBs. The output of the BP is then fed back to RTS for the next iteration. Simulation results show that the proposed algorithm performs better than the RTS algorithm, and semi-definite relaxation (SDR) and Gaussian tree approximation (GTA) algorithms.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Frequency detection from multiplexed compressed sensing of noisy signals

Signal acquisition under a compressed sensing scheme offers the possibility of acquisition and reconstruction of signals sparse on some basis incoherent with measurement kernel with sub-Nyquist number of measurements. In particular when the sole objective of the acquisition is the detection of the frequency of a signal rather than exact reconstruction, then an undersampling framework like CS is able to perform the task. In this paper we explore the possibility of acquisition and detection of frequency of multiple analog signals, heavily corrupted with additive white Gaussian noise. We improvise upon the MOSAICS architecture proposed by us in our previous work to include a wider class of signals having non-integral frequency components. This makes it possible to perform multiplexed compressed sensing for general frequency sparse signals.

A transient detection algorithm for audio using iterative analysis of STFT

We propose an iterative algorithm to detect transient segments in audio signals. Short time Fourier transform(STFT) is used to detect rapid local changes in the audio signal. The algorithm has two steps that iteratively - (a) calculate a function of the STFT and (b) build a transient signal. A dynamic thresholding scheme is used to locate the potential positions of transients in the signal. The iterative procedure ensures that genuine transients are built up while the localised spectral noise are suppressed by using an energy criterion. The extracted transient signal is later compared to a ground truth dataset. The algorithm performed well on two databases. On the EBU-SQAM database of monophonic sounds, the algorithm achieved an F-measure of 90% while on our database of polyphonic audio an F-measure of 91% was achieved. This technique is being used as a preprocessing step for a tempo analysis algorithm and a TSR (Transients + Sines + Residue) decomposition scheme.

Improved Detection of Remote Homologues Using Cascade PSI-BLAST: Influence of Neighbouring Protein Families on Sequence Coverage

Background: Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. Methodology/Principal Findings: We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of similar to 100% and Mathew's correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. Conclusions/Significance: Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the `bridging' role of related families.

Fluoranthene based derivatives for detection of trace explosive Nitroaromatics

A series of fluoranthene derivatives (F1-F5) varied with nature and type of substituents were synthesized via Diels-Alder reaction followed by in situ decarbonylation. The solid state structures have been established through single crystal X-ray diffraction (XRD). The presence of extended conjugation and having two alkyloxy chains on phenyl rings induces flexibility to orient opposite to each other and interacts with another fluoranthene unit with weak pi-pi interactions and show unique supramolecular arrangements. The envisaged photophysical and DFT studies demonstrated that HOMO-LUMO levels were effectively tuned by different substituents with an optical band gap from 3.44 to 3.88 eV provoked to examine as sensitive fluorescent chemosensors for the detection of nitroaromatic compounds (NACs). The sensitivity toward the detection of NACs was evaluated through fluorescence quenching in solution (aqueous and non-aqueous) and solid state (vapor and contact mode). Fluorescence studies demonstrated that electron transfer occurs from the electron rich fluoranthene fluorophores to the electron deficient NACs by the dominant static quenching mechanism and the quenching process is reversible. It was found that the detection sensitivity increases with extent of conjugation on fluoranthene unit. The contact mode approach using thin layer silica chromatographic plates exhibits a femtogram (1.15 fg/cm(2)) detection limit for trinitrotoluene (TNT) and picric acid (PA), while the solution state fluorescence quenching shows for PA detection at the 2-20 ppb level. The sensing performance of fluoranthene thin films to NACs in aqueous solution reveals that fluorophores are highly selective towards the detection of PA. The smart performances of thin film fluorophores with high photostability have great advantage than those of conjugated polymers with superior sensitive detection of PA in groundwater.

Low temperature growth of SnO2 nanowires by electron beam evaporation and their application in UV light detection

For the first time, high quality tin oxide (SnO2) nanowires have been synthesized at a low substrate temperature of 450 degrees C via vapor-liquid-solid mechanism using an electron beam evaporation technique. The grown nanowires have shown length of 2-4 mu m and diameter of 20-60 nm. High resolution transmission electron microscope studies on the grown nanowires have shown the single crystalline nature of the SnO2 nanowires. We investigated the effect of growth temperature and oxygen partial pressure on SnO2 nanowires growth. Variation of substrate temperature at a constant oxygen partial pressure of 4 x 10(-4) mbar suggested that a temperature equal to or greater than 450 degrees C was the best condition for phase pure SnO2 nanowires growth. The SnO2 nanowires grown on a SiO2 substrate were subjected to UV photo detection. The responsivity and quantum efficiency of SnO2 NWs photo detector (at 10V applied bias) was 12 A/W and 45, respectively, for 12 mu W/cm(2) UV lamp (330 nm) intensity on the photo detector.

Ratiometric, reversible, and parts per billion level detection of multiple toxic transition metal ions using a single probe in micellar media

We present the selective sensing of multiple transition metal ions in water using a synthetic single probe. The probe is made up of pyrene and pyridine as signaling and interacting moiety, respectively. The sensor showed different responses toward metal ions just by varying the medium of detection. In organic solvent (acetonitrile), the probe showed selective detection of Hg2+ ion. In water, the fluorescence quenching was observed with three metal ions, Cu2+, Hg2+, and Ni2+. Further, just by varying the surface charge on the micellar aggregates, the probe could detect and discriminate the above-mentioned three different toxic metal ions appropriately. In neutral micelles (Brij 58), the probe showed a selective interaction with Hg2+ ion as observed in acetonitrile medium. However, in anionic micellar medium (sodium dodecyl sulfate, SDS), the probe showed changes with both Cu2+ and Ni2+. under UV-vis absorption spectroscopy. The discrimination between these two ions was achieved by recording their emission spectra, where it showed selective quenching with Cu2+.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Employing denaturation for rapid electrochemical detection of myoglobin using TiO2 nanotubes

An alternative antibody-free strategy for the rapid electrochemical detection of cardiac myoglobin has been demonstrated here using hydrothermally synthesized TiO2 nanotubes (Ti-NT). The denaturant induced unfolding of myoglobin led to easy access of the deeply buried electroactive heme center and thus the efficient reversible electron transfer from protein to electrode surface. The sensing performance of the Ti-NT modified electrodes were compared vis a vis commercially available titania and GCEs. The tubular morphology of the Ti-NT led to facile transfer of electrons to the electrode surface, which eventually provided a linear current response (obtained from cyclic voltammetry) over a wide range of Mb concentration. The sensitivity of the Ti-NT based sensor was remarkable and was equal to 18 mu A mg(-1) ml (detection limit = 50 nM). This coupled with the rapid analysis time of a few tens of minutes (compared to a few days for ELISA) demonstrates its potential usefulness for the early detection of acute myocardial infarction (AMI).

Detection limit of etched fiber bragg grating sensors

While Fiber Bragg Grating (FBG) sensors have been extensively used for temperature and strain sensing, clad etched FBGs (EFBGs) have only recently been explored for refractive index sensing. Prior literature in EFBG based refractive index sensing predominantly deals with bulk refractometry only, where the Bragg wavelength shift of the sensor as a function of the bulk refractive index of the sample can be analytically modeled, unlike the situation for adsorption of molecular thin films on the sensor surface. We used a finite element model to calculate the Bragg wavelength change as a function of thickness and refractive index of the adsorbing molecular layer and compared the model with the real-time, in-situ measurement of electrostatic layer-by-layer (LbL) assembly of weak polyelectrolytes on the silica surface of EFBGs. We then used this model to calculate the layer thickness of LbL films and found them to be in agreement with literature. Further, we used this model to arrive at a realistic estimate of the limit of detection of EFBG sensors based on nominal measurement noise levels in current FBG interrogation systems and found that sufficiently thinned EFBGs can provide a competitive platform for real-time measurement of molecular interactions while simultaneously leveraging the high multiplexing capabilities of fiber optics.

Resonance raman detection and estimation in the aqueous phase using water dispersible cyclodextrin: reduced-graphene oxide sheets

Resonance Raman spectroscopy is a powerful analytical tool for detecting and identifying analytes, but the associated strong fluorescence background severely limits the use of the technique. Here, we show that by attaching beta-cyclodextrin (beta-CD) cavities to reduced graphene-oxide (rGO) sheets we obtain a water dispersible material (beta-CD: rGO) that combines the hydrophobicity associated with rGO with that of the cyclodextrin cavities and provides a versatile platform for resonance Raman detection. Planar aromatic and dye molecules that adsorb on the rGO domains and nonplanar molecules included within the tethered beta-CD cavities have their fluorescence effectively quenched. We show that it is possible using the water dispersible beta-CD: rGO sheets to record the resonance Raman spectra of adsorbed and included organic chromophores directly in aqueous media without having to extract or deposit on a substrate. This is significant, as it allows us to identify and estimate organic analytes present in water by resonance Raman spectroscopy.

Nonparametric distributed sequential detection via universal source coding

We consider nonparametric or universal sequential hypothesis testing when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution. These algorithms are primarily motivated from spectrum sensing in Cognitive Radios and intruder detection in wireless sensor networks. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. The algorithms are first proposed for discrete alphabet. Their performance and asymptotic properties are studied theoretically. Later these are extended to continuous alphabets. Their performance with two well known universal source codes, Lempel-Ziv code and KT-estimator with Arithmetic Encoder are compared. These algorithms are also compared with the tests using various other nonparametric estimators. Finally a decentralized version utilizing spatial diversity is also proposed and analysed.