Signal Detectability Research Articles

Noise in the brain: Transcranial random noise stimulation and perceptual noise act on a stochastic resonance-like mechanism.

Stochastic resonance (SR) is a phenomenon in which a certain amount of random noise added to a weak subthreshold stimulus can enhance signal detectability. It is unknown how external noise interacts with neural noise in producing an SR-like phenomenon and whether this interaction results in a modulation of either network efficiency or the efficiency of single neurons. Using random dot motion stimuli and noninvasive brain stimulation, we attempted to unveil the specific mechanism of action of the SR-like phenomenon in motion perception, if present. We aimed to determine whether signal integration efficiency changes with external noise (random dot numerosity) and how electrical transcranial random noise stimulation (tRNS) can affect the peak performance. The participants performed a coherent motion detection task in which the random dot numerosity varied, whereas the signal-to-noise ratio (SNR) remained constant. We applied placebo or tRNS with an amplitude of either 1 or 2mA during task execution. We found peaks in participants' performance both in the case of placebo stimulation and in the case of 1-mA tRNS. In the latter case (i.e., with an additional noise source), the peak emerged at lower random dot numerosity levels than when no additional noise was added (placebo). No clear peak was observed with 2-mA tRNS. An equivalent noise (EN) analysis confirmed that SR arises from a modulation of the network efficiency underlying motion signal integration. These results indicate a joint contribution of external and neural noise (modulated by tRNS) in eliciting an SR-like phenomenon.

Multispectral singlet oxygen luminescent dosimetry (MSOLD) for Photofrin-mediated Photodynamic Therapy.

Direct detection of singlet-state oxygen ([1O2]) constitutes the holy grail dosimetric method for type II PDT, a goal that can be quantified using multispectral singlet oxygen dosimetry (MSOLD). However, the short lifetime and extremely weak nature of the singlet oxygen signal produced has given rise to a need to improve MSOLD signal-to-noise ratio. This study examines methods for optimizing MSOLD signal acquisition, specifically employing an orthogonal arrangement between detection and PDT treatment light, consisting of two fiber optics - connected to a 632-nm laser and an InGaAs detector respectively. Light collected by the InGaAs detector is then passed through a filter wheel, where spectral emission measurements are taken at 1200 nm, 1240 nm, 1250 nm, 1270 nm, and 1300 nm. The data, after fitting to the fluorescence background and a gaussian-fit for the singlet oxygen peak, is established for the background-subtracted singlet oxygen emission signal. The MSOLD signal is then compared with the singlet oxygen explicit dosimetry (SOED) results, based on direct measurements of in-vivo light fluence (rate), in-vivo Photofrin concentration, and tissue oxygenation concentration. This study focuses on validating the sensitivity and minimum detectability of MSOLD signal in various in-vitro conditions. Finally, the MSOLD device will be tested in Photofrin-mediated PDT for mice bearing Radiation-Induced Fibrosarcoma (RIF) tumors.

The effects of porous medium parameters on electroseismic conversion

The generation of electroseismic conversion in fluid-filled porous media is closely related to reservoir parameters, however, its correlation and the influence of reservoir parameters on electroseismic conversion need to be clarified, and there is only a little analysis of measured electroseismic signals. In our study, we use experimental and theoretical methods to investigate the dependency of the reservoir parameters on the electroseismic coupling. A set of experimental devices was built to conduct electroseismic measurements on water-saturated sandstone samples. The electroseismic theory based on the capillary model is used to comprehensively analyze the experimental results and explore the effects of reservoir parameters (porosity, permeability, fluid concentration) on electroseismic conversion. The combined experimental and theoretical analysis show that the electroseismic signal increases with porosity, but its relationship with permeability is complicated. In the lower permeability range, the electroseismic conversion firstly increases with the increase of permeability, while continued increases of permeability lead to a decrease in electroseismic conversion in the high permeability range. The dependency of the electroseismic conversion on pore fluid concentration also presents two trends in different concentration ranges. It decreases rapidly with the increase of fluid concentration in the low concentration range. When the intensity of electroseismic conversion reaches the minimum, it remains almost unchanged even if the fluid concentration continues to increase. The results of the influence of reservoir parameters on electroseismic conversion indicate that electroseismic conversion is as sensitive to the parameters of fluid-filled porous media as seismoelectric conversion. The electroseismic conversion signal is more stable than seismoelectric signal, which means that compared with the difficult detection of seismoelectric signals, the detectability of electroseismic signals is higher. It may be easier to use electroseismic signals to reveal the petrophysical properties of reservoirs.

Gravito-optics and intensity correlations for binary inspiral signal detections

In this paper, we examine the correlation functions associated with intensity interferometry and gravito-optics of gravitational wave (GW) signals from compact binary coalescences (CBC). Previous theoretical studies of the gravito-optics of GWs have concentrated on the characterization of both the classical and the nonclassical properties of signals from cosmological sources in the early Universe. These previous works assume a periodic signal similar to the signals studied widely in optics and quantum optics and do not apply to transient signals. We develop the gravito-optics of intensity correlations for descriptions of the detection of transient signals from CBC and apply these methods to calculate the two-point intensity correlations for the GW discovery. We also discuss the necessary theoretical work required for the description of the quantum gravito-optics of intensity correlations in the detection of signals from binary inspirals.

4D Seismic Least Square Migration in Carbonates: A Case of Successful Matching

4D seismic surveys are now routinely used to monitor hydrocarbon reservoirs. Because seismic reflections are sensitive to formation stress and fluid content, repeated seismic surveys are able to detect pressure changes and fluid exchanges that occur during field production. These measurements can help to optimise recovery strategy and identify areas where hydrocarbons have been bypassed. In practice, the seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. However, by applying a 4D seismic co-processing workflow, we were able to detect and quantify a 4D signal even with suboptimal repeated acquisition geometry. Seismic inversion helped to overcome noise, multiple contamination, and differences in dynamic amplitude range between baseline and monitor seismic surveys. Matching the 4D seismic signal to changes in reservoir production characteristics aided in the investigation of the mechanism underlying the observed 4D signal. Detectability of 4D signals was found to be primarily related to changes in reservoir pressure and fluid saturation, which increased from 2010 to 2020 the time-lapse between the base and monitor survey.

The influence of temporal features of the signal on detectability: Data and signal-detection analyses

Dave Green had an enormous influence on auditory research via signal-detection theory. A major contribution of signal-detection theory is the concept and application of the ideal detector, which establishes the absolute optimal detection performance. To bridge between the ideal performance and the less-than-ideal human performance, Green and Swets (1966) described an energy detector. The energy-detector model relaxed assumptions of the ideal detector to account for the phase insensitivity and limited frequency selectivity of humans, but it retained the assumption that the time window is matched to the signal duration. However, the time-window assumption had no clear support from human data. To help fill this gap, we examined the detectability of signals with expected versus unexpected temporal properties. The results of experiments and signal-detection analyses suggest that listeners listen selectively to signal duration, implying that they match the time window to the overall signal duration as was assumed in the energy-detector model. Additional results suggest that listeners do not also listen selectively to the temporal structure of the signal, implying that the temporal constraint is based on overall signal duration. We are grateful for the opportunity to have worked with Dave.

NEAR–OPTIMUM DETECTION OF SIGNALS IN UNDERWATER ACOUSTIC NOISE USING LOCALLY OPTIMAL DETECTOR IN TIGERS RIVER

Signal detection has been considered important in under-water signal processing and digital communications, and depending upon noise statistics’ knowledge, near-optimum signal detections in the under-water acoustic noises (UWANs) may be realized in a more effective manner. The theory of the normal (i.e. Gaussian) noise permits using matched filter (MF) detectors; for that reason, a locally optimal (LO) detector has been designed in the present work for improving the probability of the detection ( based on knowing the probability density function (PDF) of noise. Under-water noise that has been utilized for the validation represents the real data that had been gathered from the sea with the use of the broad-band hydrophones at Abo Dali district -Kazem Al Ali Village-Tigris Beaches-Baghdad-Iraq. The LO detector performance is compared after that to the conventional matched filter detector and those have been assessed based on their values. For time-varying signals, the probability of false alarms has been identified as 0.010, and of 90%, energy-to-noise ratios (ENRs) of LO are more efficient compared to the ones of Matched Filter by 4.1dB and for the signals with a fixed frequency, LO is more efficient compared to matched filter by 4.7dB.

The Influence of the Rapid Increase in the Number of Adverse Event Reports for COVID-19 Vaccine on the Disproportionality Analysis Using JADER.

The COVID-19 prophylactic vaccine for the prevention of coronavirus infection was approved in Japan on February 14, 2021. Adverse event reports for the vaccine were collected from the Japan Adverse Drug Event Relief (JADER) database, similar to those for drugs. Reported odds ratios (RORs) and proportional reporting ratios (PRRs) are commonly used in disproportionality analysis to detect safety signals. Therefore, adverse event reports from the vaccinated population may affect the detection of safety signals for the registered drugs. This study determined the impact of adverse event reports on the detection of safety signals for a COVID-19 prophylactic vaccine by analyzing the JADER database using disproportionality analysis. We extracted data from the JADER dataset, in which the COVID-19 vaccine was reported as a suspected drug, and selected the top 10 adverse events in terms of the number of reports. We then extracted the top 30 drugs by the amount of information in the selected 10 adverse events and compared the changes in the number of signal detections with and without the COVID-19 vaccine report data. The total number of adverse events reported in the JADER database during the study period was 2,002,564. Of the total number of reports, 85,489 (4.3%) reported adverse events related to the COVID-19 vaccine. Of the top 30 drugs reported in the 10 selected adverse events, the ROR and PRR were found to be lower with the inclusion of COVID-19 vaccine data than without. Detection by ROR excluded 23 out of 245 drugs, and detection by PRR excluded 34 out of 204 drugs. The rapid increase in the number of adverse event reports for the COVID-19 vaccine in JADER may affect the detection of safety signals by disproportionality analysis.

Effect of Source Mispositioning on the Repeatability of 4D Vertical Seismic Profiling Acquired with Distributed Acoustic Sensors.

Vertical seismic profiling (VSP) with distributed acoustic sensing (DAS) is an increasingly popular evolving technique for reservoir monitoring. DAS technology enables permanent fibre installations in wells and simultaneous seismic data recording along an entire borehole. Deploying the receivers closer to the reservoir allows for better detectability of smaller signals. A high level of repeatability is essential for the robust time-lapse monitoring of geological reservoirs. One of the prominent factors of repeatability degradation is a shift between source/receiver locations (mispositioning) during baseline and monitor surveys. While the mispositioning effect has been extensively studied for surface 4D seismic, the number of such studies for VSP is quite limited. To study the effects of source mispositioning on time-lapse data repeatability, we performed two VSP experiments at two on-shore sites with vibroseis. The first study was carried out at the Otway International Test Centre during Stage 3 of the Otway project and showed that the effect of source mispositioning on repeatability is negligible in comparison with the effect of temporal variations of the near-surface conditions. To avoid these limitations, we conducted a same-day controlled experiment at the Curtin University site. This second experiment showed that the effect of source mispositioning on repeatability is controlled by the degree of lateral variations of the near-surface conditions. Unlike in marine seismic measurements, lateral variations of near-surface properties can be strong and rapid and can degrade the repeatability for shifts of the source of a few meters. The greater the mispositioning, the higher the chance of such significant variations. When the near-surface conditions are laterally homogeneous, the effect of typical source mispositioning is small, and in all practical monitoring applications its contribution to non-repeatability is negligible.

On the detectability of the magnetic fields induced by ocean circulation in geomagnetic satellite observations

AbstractDue to their sensitivity to conductivity and oceanic transport, magnetic signals caused by the movement of the ocean are a beneficial source of information. Satellite observed tidal-induced magnetic fields have already proven to be helpful to derive Earth’s conductivity or ocean heat content. However, magnetic signals caused by ocean circulation are still unobserved in satellite magnetometer data. We present a novel method to detect these magnetic signals from ocean circulation using an observing system simulation experiment. The introduced approach relies on the assimilation of satellite magnetometer data based on a Kalman filter algorithm. The separation from other magnetic contributions is attained by predicting the temporal behavior of the ocean-induced magnetic field through presumed proxies. We evaluate the proposed method in different test case scenarios. The results demonstrate a possible detectability of the magnetic signal in large parts of the ocean. Furthermore, we point out the crucial dependence on the magnetic signal’s variability and show that our approach is robust to slight spatial and temporal deviations of the presumed proxies. Additionally, we showed that including simple prior spatial constraints could further improve the assimilation results. Our findings indicate an appropriate sensitivity of the detection method for an application outside the presented observing system simulation experiment. Therefore, we finally discussed potential issues and required advances toward the method’s application on original geomagnetic satellite observations. Graphical Abstract

Mitigating Threshold Effects in Human Control by Stochastic Resonance With Fractional Colored Noise

In industrial applications, mechanical and physiological thresholds may limit the capability of human manipulating machine via control devices, such as joysticks and steering wheels. These thresholds can result in loss of information in the control signals that are kept below the threshold of detection of the device or the human operator. One approach to mitigate these effects is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic resonance</i> , i.e., by injecting additive noise into a signal to raise its energy content over the threshold of detection. Though this noise partially corrupts the signal, it can increase the detectability of the signal by the control device. This article provides, for the first time, research towards using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic resonance</i> to improve human performance in control tasks. In particular, it shows that using adaptive colored noise can improve the detectability of the steering control signals recorded from human participants. The approach converts a signal processing task to an optimization problem, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">particle swarm optimization</i> is employed to obtain the optimal color (or spectral exponent) of the injected additive noise, generated through an intelligent technique with fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals. This method can be widely applicable to other industrial domains, such as energy harvesting and enhancing sensory perception.

Detection of p-mode Oscillations in HD 35833 with NEID and TESS

We report the results of observations of p-mode oscillations in the G0 subgiant star HD 35833 in both radial velocities and photometry with NEID and TESS, respectively. We achieve separate, robust detections of the oscillation signal with both instruments (radial velocity amplitude A RV = 1.11 ± 0.09 m s−1, photometric amplitude A phot = 6.42 ± 0.60 ppm, frequency of maximum power μHz, and mode spacing Δν = 36.65 ± 0.96 μHz) as well as a nondetection in a TESS sector concurrent with the NEID observations. These data shed light on our ability to mitigate the correlated noise impact of oscillations with radial velocities alone and on the robustness of commonly used asteroseismic scaling relations. The NEID data are used to validate models for the attenuation of oscillation signals for exposure times , and we compare our results to predictions from theoretical scaling relations and find that the observed amplitudes are weaker than expected by >4σ, hinting at gaps in the underlying physical models.

18F-PSMA Cerenkov Luminescence and Flexible Autoradiography Imaging in a Prostate Cancer Mouse Model and First Results of a Radical Prostatectomy Feasibility Study in Men.

Intraoperative identification of positive resection margins (PRMs) in high-risk prostate cancer (PC) needs improvement. Cerenkov luminescence imaging (CLI) with 68Ga-PSMA-11 is promising, although limited by low residual activity and artificial signals. Here, we aimed to assess the value of CLI and flexible autoradiography (FAR) with 18F-PSMA-1007. Methods: Mice bearing subcutaneous PSMA-avid RM1-PGLS tumors were administered 18F-PSMA-1007, and PET/CT was performed. After the animals had been killed, organs were excised and measured signals in CLI and FAR CLI were correlated with tracer activity concentrations (ACs) obtained from PET/CT. For clinical assessment, 7 high-risk PC patients underwent radical prostatectomy immediately after preoperative 18F-PSMA PET/CT. Contrast-to-noise ratios (CNRs) were calculated for both imaging modalities in intact specimens and after incision above the index lesion. Results: In the heterotopic invivo mouse model (n = 5), CLI did not detect any lesion. FAR CLI detected a distinct signal in all mice, with a lowest AC of 7.25 kBq/mL (CNR, 5.48). After incision above the index lesion of the prostate specimen, no increased signal was observed at the cancer area in CLI. In contrast, using FAR CLI, a signal was detectable in 6 of 7 patients. The AC in the missed index lesion was 1.85 kBq/mL, resulting in a detection limit of at least 2.06 kBq/mL. Histopathology demonstrated 2 PRMs, neither of which was predicted by CLI or FAR CLI. Conclusion: 18F-PSMA FAR CLI was superior to CLI in tracer-related signal detectability. PC was could be visualized in radical prostatectomy down to 2.06 kBq/mL. However, the detection of PRMs was limited. Direct anatomic correlation of FAR CLI is challenging because of the scintillator overlay.

Ultralight, Elastic, Hybrid Aerogel for Flexible/Wearable Piezoresistive Sensor and Solid-Solid/Gas-Solid Coupled Triboelectric Nanogenerator.

Aerogels have been attracting wide attentions in flexible/wearable electronics because of their light weight, excellent flexibility, and electrical conductivity. However, multifunctional aerogel-based flexible/wearable electronics for human physiological/motion monitoring, and energy harvest/supply for mobile electronics, have been seldom reported yet. In this study, a kind of hybrid aerogel (GO/CNT HA) based on graphene oxide (GO) and carboxylated multiwalled carbon nanotubes (CMWCNTs) is prepared which can not only used as piezoresistive sensors for human motion and physiological signal detections, but also as high performance triboelectric nanogenerator (TENG) coupled with both solid-solid and gas-solid contact electrifications (CE). The repeatedly loading-unloading tests with 20000 cycles exhibit its high and ultrastable piezoresistive sensor performances. Moreover, when the obtained aerogel is used as the electrode of a TENG, high electric output performance is produced due to the synergistic effect of solid-solid, and gas-solid interface CEs (3D electrification: solid-solid interface CE between the two solid electrification layers; gas-solid interface CE between the inner surface of GO/CNT HA and the air filled in the aerogel pores). This kind of aerogel promises good applications for human physiological/motion monitoring and energy harvest/supply in flexible/wearable electronics such as piezoresistive sensors and flexible TENG.

A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains

Negative correlations in the sequential evolution of interspike intervals (ISIs) are a signature of memory in neuronal spike-trains. They provide coding benefits including firing-rate stabilization, improved detectability of weak sensory signals, and enhanced transmission of information by improving signal-to-noise ratio. Primary electrosensory afferent spike-trains in weakly electric fish fall into two categories based on the pattern of ISI correlations: non-bursting units have negative correlations which remain negative but decay to zero with increasing lags (Type I ISI correlations), and bursting units have oscillatory (alternating sign) correlation which damp to zero with increasing lags (Type II ISI correlations). Here, we predict and match observed ISI correlations in these afferents using a stochastic dynamic threshold model. We determine the ISI correlation function as a function of an arbitrary discrete noise correlation function {{,mathrm{mathbf {R}},}}_k, where k is a multiple of the mean ISI. The function permits forward and inverse calculations of the correlation function. Both types of correlation functions can be generated by adding colored noise to the spike threshold with Type I correlations generated with slow noise and Type II correlations generated with fast noise. A first-order autoregressive (AR) process with a single parameter is sufficient to predict and accurately match both types of afferent ISI correlation functions, with the type being determined by the sign of the AR parameter. The predicted and experimentally observed correlations are in geometric progression. The theory predicts that the limiting sum of ISI correlations is -0.5 yielding a perfect DC-block in the power spectrum of the spike train. Observed ISI correlations from afferents have a limiting sum that is slightly larger at -0.475 pm 0.04 (text {mean} pm text {s.d.}). We conclude that the underlying process for generating ISIs may be a simple combination of low-order AR and moving average processes and discuss the results from the perspective of optimal coding.

Passivation Strategies for Enhancing Solution-Gated Carbon Nanotube Field-Effect Transistor Biosensing Performance and Stability in Ionic Solutions.

Interest in point-of-care diagnostics has led to increasing demand for the development of nanomaterial-based electronic biosensors such as biosensor field-effect transistors (BioFETs) due to their inherent simplicity, sensitivity, and scalability. The utility of BioFETs, which use electrical transduction to detect biological signals, is directly dependent upon their electrical stability in detection-relevant environments. BioFET device structures vary substantially, especially in electrode passivation modalities. Improper passivation of electronic components in ionic solutions can lead to excessive leakage currents and signal drift, thus presenting a hinderance to signal detectability. Here, we harness the sensitivity of nanomaterials to study the effects of various passivation strategies on the performance and stability of a transistor-based biosensing platform based on aerosol-jet-printed carbon nanotube thin-film transistors. Specifically, non-passivated devices were compared to devices passivated with photoresist (SU-8), dielectric (HfO2), or photoresist + dielectric (SU-8 followed by HfO2) and were evaluated primarily by initial performance metrics, large-scale device yield, and stability throughout long-duration cycling in phosphate buffered saline. We find that all three passivation conditions result in improved device performance compared to non-passivated devices, with the photoresist + dielectric strategy providing the lowest average leakage current in solution (~2 nA). Notably, the photoresist + dielectric strategy also results in the greatest yield of BioFET devices meeting our selected performance criteria on a wafer scale (~90%), the highest long-term stability in solution (<0.01% change in on-current), and the best average on/off-current ratio (~104), hysteresis (~32 mV), and subthreshold swing (~192 mV/decade). This passivation schema has the potential to pave the path toward a truly high-yield, stable, and robust electrical biosensing platform.

Development of a large-aperture 160-element coherent hydrophone array system for instantaneous wide area ocean acoustic sensing

A large-aperture coherent hydrophone array and processing system has been developed in-house at Northeastern University (NU). The overall acoustic aperture length is 194 m with 160-elements nested into four sub-apertures each with 64 hydrophones and spacing corresponding to half-wavelength at design frequencies in Hertz of 250, 500, 1000, and 2000. Hydrophones with integrated broadband preamplifiers designed with a linear frequency response from 10 to 50 kHz sending differential pair amplified and filtered analog signals to 24-bit, 32-channel Analog to Digital Converters (ADC) with sampling rate programmable up to 100 kHz per channel. Array internals are designed using field replaceable pressure tolerant components including hydrophones, preamplifiers, power modules, telemetry units, and ADC verified by testing in an in-house assembled pressure chamber. Forward and aft modules are equipped with non-acoustic sensor elements to provide depth, heading, pitch, roll, and temperature measurements. The NU array was made without specialized facilities by utilizing modular interchangeable array interconnects allowing for conventional array populating and filling methods. The system implements real-time array data acquisition and processing including beamforming, beamformed spectrogram generation and signal detections. The NU array has been sea tested in 2021 in both shallow and deep water environments of the US Northeast coast.

Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

Higher order spectral analysis of weather signals

Higher Order Spectral Analysis (HOSA) is very useful technic for analysing the statistical relationships between spectral components and their distribution. HOSA can be defined in terms of bispectrum as a third order spectrum. One of the main reasons to consider HOSA on weather signals is that to characterise the Doppler spectrum obtained through the backscattered echoes which are generally skewed and having multiple peaks. The analysis also brings out any deviations from gaussianity and the performance under noisy environment. This will help in identifying and detecting the signals and their by better estimation of moments for further processing The bispectrum estimation technique has been applied to a complex time series data derived from a Polarimetric X-band Doppler Weather Radar (DWR). The results of this analysis are compared with the conventional techniques like Fourier method and pulse pair technique. It is observed that through bispectrum approach the gaussian noise components are significantly removed from the back scattered signals and improves the detectability of the weather signals under noisy condition.