Spectral Sensing for Cognitive Radio: Estimation of Adaptive Frequency Hopping Signal

The issue of dynamic spectrum scene analysis in any cognitive radio network becomes extremely complex when low probability of intercept, spread spectrum systems are present in environment. The detection and estimation become more complex if frequency hopping spread spectrum is adaptive in nature. In this paper, we propose two phase approach for detection and estimation of frequency hoping signals. Polyphase filter bank has been proposed as the architecture of choice for detection phase to efficiently detect the presence of frequency hopping signal. Based on the modeling of frequency hopping signal it can be shown that parametric methods of line spectral analysis are well suited for estimation of frequency hopping signals if the issues of order estimation and time localization are resolved. An algorithm using line spectra parameter estimation and wavelet based transient detection has been proposed which resolves above issues in computationally efficient manner suitable for implementation in cognitive radio. The simulations show promising results proving that adaptive frequency hopping signals can be detected and demodulated in a non cooperative context, even at a very low signal to noise ratio in real time.

Effect of Crowding Agents, Signal Peptide, and Chaperone SecB on the Folding and Aggregation of E. coli Maltose Binding Protein

SecB, a soluble cytosolic chaperone component of the Secexport pathway, binds to newly synthesized precursor proteins and prevents their premature aggregation and folding and subsequently targets them to the translocation machinery on the membrane. PreMBP, the precursor form of maltose binding protein, has a 26-residue signal sequence attached to the N-terminus of MBP and is a physiological substrate of SecB. We examine the effect of macromolecular crowding and SecB on the stability and refolding of denatured preMBP and MBP. PreMBP was less stable than MBP (ΔTm =7( 0.5 K) in both crowded and uncrowded solutions. Crowding did not cause any substantial changes in the thermal stability ofMBP(ΔTm=1(0.4 K) or preMBP (ΔTm=0(0.6 K), as observed in spectroscopically monitored thermal unfolding experiments. However, both MBP and preMBP were prone to aggregation while refolding under crowded conditions. In contrast to MBP aggregates, which were amorphous, preMBP aggregates form amyloid fibrils.Under uncrowded conditions, a molar excess of SecB was able to completely prevent aggregation and promote disaggregation of preformed aggregates of MBP. When a complex of the denatured protein and SecB was preformed, SecB could completely prevent aggregation and promote folding of MBP and preMBP even in crowded solution. Thus, in addition to maintaining substrates in an unfolded, export-competent conformation, SecB also suppresses the aggregation of its substrates in the crowded intracellular environment. SecB is also able to promote passive disaggregation of macroscopic aggregates of MBP in the absence of an energy source such as ATP or additional cofactors. These experiments also demonstrate that signal peptide can reatly influence protein stability and aggregation propensity.

Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence

We present here the first statistically calibrated and verified tree-ring reconstruction of climate from continental Southeast Asia.The reconstructed variable is March-May (MAM) Palmer Drought Severity Index (PDSI) based on ring widths from 22 trees (42 radial cores) of rare and long-lived conifer, Fokienia hodginsii (Po Mu as locally called) from northern Vietnam. This is the first published tree ring chronology from Vietnam as well as the first for this species. Spanning 535 years, this is the longest cross-dated tree-ring series yet produced from continental Southeast Asia. Response analysis revealed that the annual growth of Fokienia at this site was mostly governed by soil moisture in the pre-monsoon season. The reconstruction passed the calibration-verification tests commonly used in dendroclimatology, and revealed two prominent periods of drought in the mid-eighteenth and late-nineteenth enturies. The former lasted nearly 30 years and was concurrent with a similar drought over northwestern Thailand inferred from teak rings, suggesting a ``mega-drought'' extending across Indochina in the eighteenth century. Both of our reconstructed droughts are consistent with the periods of warm sea surface temperature (SST)anomalies in the tropical Pacific. Spatial correlation analyses with global SST indicated that ENSO-like anomalies might play a role in modulating droughts over the region, with El Nio (warm) phases resulting in reduced rainfall. However, significant correlation was also seen with SST over the Indian Ocean and the north Pacific,suggesting that ENSO is not the only factor affecting the climate of the area. Spectral analyses revealed significant peaks in the range of 53.9-78.8 years as well as in the ENSO-variability range of 2.0 to 3.2 years.

The Mechanisms of Hedgehog Signal Transduction

Studies in both vertebrates and invertebrates have identified proteins of the Hedgehog (Hh) family of secreted signaling molecules as key organizers of tissue patterning. Initially discovered in Drosophila in 1992, Hh family members have been discovered in animals with body plans as diverse as those of mammals, insects and echinoderms. In humans three related Hh genes have been identified: Sonic, Indian and Desert hedgehog (Shh, Ihh and Dhh). Transduction of the Hh signal to the cytoplasm utilizes an unusual mechanism involving consecutive repressive interactions between Hh and its receptor components, Patched (Ptc) and Smoothened (Smo). Several cytoplasmic proteins involved in Hh signal transduction are known in Drosophila, but mammalian homologs are known only for the Cubitus interruptus (Ci) transcription factor (GLI(1-3)) and for the Ci/GLI-associated protein, Suppressor of Fused (Su(fu)). In this study I analyzed the mechanisms of how the Hh receptor Ptc regulates the signal transducer Smo, and how Smo relays the Shh signal from the cell surface to the cytoplasm ultimately leading to the activation of GLI transcription factors. In Drosophila, the kinesin-like protein Costal2 (Cos2) is required for suppression of Hh target gene expression in the absence of ligand, and loss of Cos2 causes embryonic lethality. Cos2 acts by bridging Smo to the Ci. Another protein, Su(Fu) exerts a weak suppressive influence on Ci activity and loss of Su(Fu) causes subtle changes in Drosophila wing pattern. This study revealed that domains in Smo that are critical for Cos2 binding in Drosophila are dispensable for mammalian Smo function. Furthermore, by analyzing the function of Su(Fu) and the closest mouse homologs of Cos2 by protein overexpression and RNA interference I found that inhibition of the Hh response pathway in the absence of ligand does not require Cos2 activity, but instead critically depends on the activity of Su(Fu). These results indicate that a major change in the mechanism of action of a conserved signaling pathway occurred during evolution, probably through phenotypic drift made possible by the existence in some species of two parallel pathways acting between the Hh receptor and the Ci/GLI transcription factors. In a second approach to unravel Hh signaling we cloned > 90% of all human full-length protein kinase cDNAs and constructed the corresponding kinase-activity deficient mutants. Using this kinome resource as a screening tool, two kinases, MAP3K10 and DYRK2 were found to regulate Shh signaling. DYRK2 directly phosphorylated and induced the proteasome dependent degradation of the key Hh-pathway regulated transcription factor, GLI2. MAP3K10, in turn, affected GLI2 indirectly by modulating the activity of DYRK2.

Short- and long-term movement patterns in the freshwater whipray (Himantura dalyensis) determined by the signal processing of passive acoustic telemetry data.

Patterns of movement in aquatic animals reflect ecologically important behaviours. Cyclical changes in the abiotic environment influence these movements, but when multiple processes occur simultaneously, identifying which is responsible for the observed movement can be complex. Here we used acoustic telemetry and signal processing to define the abiotic processes responsible for movement patterns in freshwater whiprays (Himantura dalyensis). Acoustic transmitters were implanted into the whiprays and their movements detected over 12 months by an array of passive acoustic receivers, deployed throughout 64 km of the Wenlock River, Qld, Australia. The time of an individual's arrival and departure from each receiver detection field was used to estimate whipray location continuously throughout the study. This created a linear-movement-waveform for each whipray and signal processing revealed periodic components within the waveform. Correlation of movement periodograms with those from abiotic processes categorically illustrated that the diel cycle dominated the pattern of whipray movement during the wet season, whereas tidal and lunar cycles dominated during the dry season. The study methodology represents a valuable tool for objectively defining the relationship between abiotic processes and the movement patterns of free-ranging aquatic animals and is particularly expedient when periods of no detection exist within the animal location data.

'A programmable digital signal averager' Reply

In a recent paper, Srinivasan et al (1980) have described a programmable digital signal averager with facility for programming the sampling period, number of channels and number of sweeps. We have examined this paper in some detail and find that some points need clarification.

Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

LPG Gas-Sensing System With SnO2 Thin-Film Transducer and 0.7-mu m CMOS Signal Conditioning ASIC

The design and implementation of a complete gas sensor system for liquified petroleum gas (LPG) gas sensing are presented. The system consists of a SnO2 transducer, a lowcost heater, an application specific integrated circuit (ASIC) with front-end interface circuitry, and a microcontroller interface for data logging. The ASIC includes a relaxation-oscillator-based heater driver circuit that is capable of controlling the sensor operating temperature from 100degC to 425degC. The sensor readout circuit in the ASIC, which is based on the resistance to time conversion technique, has been designed to measure the gas sensor response over three orders of resistance change during its interaction with gases.

Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis. In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.

Three-dimensional reconstruction of black tiger prawn (Penaeus monodon) spermatozoa using serial block-face scanning electron microscopy

Serial Block-Face Scanning Electron Microscopy (SBF-SEM) was used in this study to examine the ultrastructural morphology of Penaeus monodon spermatozoa. SBF-SEM provided a large dataset of sequential electron-microscopic-level images that facilitated comprehensive ultrastructural observations and three-dimensional reconstructions of the sperm cell. Reconstruction divulged a nuclear region of the spermatophoral spermatozoon filled with decondensed chromatin but with two apparent levels of packaging density. In addition, the nuclear region contained, not only numerous filamentous chromatin elements with dense microregions, but also large centrally gathered granular masses. Analysis of the sperm cytoplasm revealed the presence of degenerated mitochondria and membrane-less dense granules. A large electron-lucent vesicle and "arch-like" structures were apparent in the subacrosomal area, and an acrosomal core was found in the acrosomal vesicle. The spermatozoal spike arose from the inner membrane of the acrosomal vesicle, which was slightly bulbous in the middle region of the acrosomal vesicle, but then extended distally into a broad dense plate and to a sharp point proximally. This study has demonstrated that SBF-SEM is a powerful technique for the 3D ultrastructural reconstruction of prawn spermatozoa, that will no doubt be informative for further studies of sperm assessment, reproductive pathology and the spermiocladistics of penaeid prawns, and other decapod crustaceans. J. Morphol., 2016. (c) 2016 Wiley Periodicals, Inc.

Iterative solution of a class of linear equations with application to reconstruction of three-dimensional object arrays

An iterative algorithm baaed on probabilistic estimation is described for obtaining the minimum-norm solution of a very large, consistent, linear system of equations AX = g where A is an (m times n) matrix with non-negative elements, x and g are respectively (n times 1) and (m times 1) vectors with positive components.

Insights into students' conceptual understanding using textual analysis: A case study in signal processing

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.

Robust and High-resolution Voiced/Unvoiced Classification in Noisy Speech Using A Signal Smoothness Criterion

We propose a novel technique for robust voiced/unvoiced segment detection in noisy speech, based on local polynomial regression. The local polynomial model is well-suited for voiced segments in speech. The unvoiced segments are noise-like and do not exhibit any smooth structure. This property of smoothness is used for devising a new metric called the variance ratio metric, which, after thresholding, indicates the voiced/unvoiced boundaries with 75% accuracy for 0dB global signal-to-noise ratio (SNR). A novelty of our algorithm is that it processes the signal continuously, sample-by-sample rather than frame-by-frame. Simulation results on TIMIT speech database (downsampled to 8kHz) for various SNRs are presented to illustrate the performance of the new algorithm. Results indicate that the algorithm is robust even in high noise levels.