Single-channel Method Research Articles

A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes

Background and Objective: A fusion of multi-slice computed tomography (MSCT) and single photon emission computed tomography (SPECT) represents a powerful tool for chronic obstructive pulmonary disease (COPD) analysis. In this paper, a novel and high-performance MSCT/SPECT non-rigid registration algorithm is proposed to accurately map the lung lobe information onto the functional imaging. Such a fusion can then be used to guide lung volume reduction surgery. Methods: The multi-modality fusion method proposed here is developed by a multi-channel technique which performs registration from MSCT scan to ventilation and perfusion SPECT scans simultaneously. Furthermore, a novel function with less parameters is also proposed to avoid the adjustment of the weighting parameter and to achieve a better performance in comparison with the exisitng methods in the literature. Results: A lung imaging dataset from a hospital and a synthetic dataset created by software are employed to validate single- and multi-modality registration results. Our method is demonstrated to achieve the improvements in terms of registration accuracy and stability by up to 23% and 54% respectively. Our multi-channel technique proposed here is also proved to obtain improved registration accuracy in comparison with single-channel method. Conclusions: The fusion of lung lobes onto SPECT imaging is achievable by accurate MSCT/SPECT alignment. It can also be used to perform lobar lung activity analysis for COPD diagnosis and treatment.

RELATIONSHIPS BETWEEN LAND USE/LAND COVER AND LAND SURFACE TEMPERATURE IN TABRIZ FROM 2000 TO 2017

Abstract. Urban Heat Island (UHI) refers to the development of higher urban temperatures of an urban area compared to the temperatures of surrounding suburban and rural areas. Highly reflective urban materials to solar radiation present a significantly lower surface temperature and contribute to reducing the sensible heat released in the atmosphere and mitigating the urban heat island. Many studies of the UHI effect have been based on Land Surface Temperature (LST) measurements from remote sensors. The remotely sensed UHI has been termed the surface urban heat island (SUHI) effect. This study examines Tabriz city land use/land cover (LULC) and LST changes using Landsat satellite images between 2000 and 2017. Maximum likelihood classification and single channel methods were used for LULC classification and LST retrieval respectively. Results show that impervious surface has increased 13.79% and bare soil area has decreased 16.2%. The results also revealed bare soil class LST after a constant trend become increasing. It also revealed the impervious surface LST has a decreasing trend between 2000 and 2011 and has a little change. Using materials that have low absorption and high reflectance decrease the effect of heat island considerably.

Single-channel method for measuring the reflectance spectra of grazing incidence mirrors in the extreme ultraviolet range.

This Letter presents a one-channel method of measurements of a grazing incidence reflection coefficient. The idea is to use the quasi-flat field extreme ultraviolet spectrometer to produce direct and reflected from the sample images of the spectral lines simultaneously at the same detector matrix. The analysis of a set of spectral lines in the working spectral range of the spectrometer gives the spectral dependence of the reflection coefficient. The results of the refection coefficient measurements for a plane ruthenium mirror for grazing angles of 6° and 10° in the spectral range 15-40nm are presented.

Comparison and real-time implementation of fixed and adaptive beamformers for speech enhancement on smartphones for hearing aid study

In this paper, we compare the performance of fixed and adaptive beamformers as an application to speech enhancement (SE) algorithm. The proposed signal processing pipeline works in real-time for hearing aid devices using only a smartphone with no additional hardware as an assistive tool. The proposed method consists of a Wiener filter based single-channel SE method along with beamformer as a pre-filter using only two built-in microphones of the smartphone. In this work, multiple beamforming approaches like delay and sum, minimum variance distortionless response (MVDR) and generalized sidelobe canceller (GSC) beamformers are considered and compared. The use of beamformers is shown to improve the signal to noise ratio (SNR) of speech signal in real-world noisy conditions through the results presented in this paper. Objective test measures are used to evaluate perceptual quality and intelligibility of enhanced speech achieved by different approaches. Test results show the performance comparison of different approaches as well as the efficiency of proposed method at different SNR levels.

Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

Optical frequency comb-based multichannel parallel continuous-variable quantum key distribution.

Continuous-variable quantum key distribution (CVQKD) provides an approach for secure communication in optical fiber communication systems. However, its practical implementation has been hindered by low secret key bit rates that are usually limited to several bits/s to hundreds of kbits/s at distances of more than 25 kilometers. In this paper, we use a pair of optical frequency combs (OFCs) for both multiple parallel transmission and coherent reception, which assign multiple sub-channels involving multiple independent secret keys in a single fiber to increase the key bit rate. The first and last sub-channels are selected for propagating phase references to compensate the phase offset between two free-running combs. We analyze possible excess noise caused by dispersive walk-off in the transmission, imperfect phase compensation in the reception and photon leakage from the phase references. Compared to the previous single-channel CVQKD method, simulation results show more than a factor of 20 increase in the secret key rate at a transmission distance of 35 km and the number of comb lines of 35.

Regional and Residual Gravity Anomaly Separation Using Singular Spectrum based Frequency Filtering Methods: A Case Study of Shallow Subsurface Modeling from Nagpur, India

We present here the application of a single and multichannel singular spectrum analysis (SSA)-based data filtering algorithm for regional and residual gravity field separation. Initially, the single-channel SSA method was tested on synthetic gravity profile data generated using Gravity and Magnetics Modelling System (GM-SYS), a gravity modeling software, for a sedimentary basin structure corrupted with Gaussian white noise and compared with the convnetional Fast Fourier transform (FFT)-based wave number (non-data adaptive) and discrete wavelet transform (semi-data-adaptive) filtering approaches. The comparative results show that the residual filtering of gravity data using the SSA method produces less/minimal artifacts compared to FFT-based wave number filtering and wavelet filtering methods. Following the synthetic examples, we further employed multichannel SSA-based frequency filtering on 2D gridded gravity data from Umred, Nagpur district, Maharashtra, India, to separate the residual anomaly for modelling the shallow subsurface structures. The modelled depth sections of two mutually perpendicular profiles suggest the presence of alluvium, Lameta, Kamthi and Barakar formations with varying thicknesses. The formations identified from the models show a clear match with the local geology and nearby borehole information. We suggest that the single and multichannel SSA based frequency filtering are robust approaches for separating regional and residual components from gravity anomalies.

Comparison of Leads Mapping in the Arctic Ocean Between Landsat and MODIS Ice Surface Temperature Products

Abstract. Leads are linear or wedge-shaped openings in the sea ice cover. They account for about half of the sensible heat transfer from the Arctic Ocean to the atmosphere in winter, though the sea surface area covered by them is only 1%~2% of the total sea ice area, thus monitoring leads changes and mapping leads distributions become an essential role on Arctic researches. Sea ice surface temperature (IST) product from Moderate Resolution Imaging Spectroradiometer (MODIS) is the most used source of leads monitoring and mapping, however, due to the coarse spatial resolution (1km at swath level), it suffers from mixed pixel effect when describes the temperature variations on thin leads (10m~100m), thus an IST product with a finer spatial resolution is needed. Though several surface temperature retrieval algorithms had been introduced based on Landsat 8 thermal imagery, none of them were validated in Arctic sea ice region. Given that the special weather conditions such as air temperature inversion were not taken into consideration, these algorithms may not always suitable for IST acquisition in Arctic. In this paper, we applied five mainstream IST algorithms (three split window algorithms and two single channel methods) on Arctic sea ice, compared the Landsat 8 IST with corresponding MODIS IST product, and validated all the satellite ISTs by in situ temperature measurements from drifting buoys. Compared to the buoy ISTs, the single channel method through web-based atmosphere correction tool provided by Barsi et al. (2003) offers the best accuracy. The split window algorithm proposed by Du et al. (2015) ranks the second, but constrained by the banding effect due to the stripe noise. Split window algorithm introduced by Jiménez-Muñoz et al. (2014) coincides with MODIS IST product best. All of the three methods mentioned above have slightly better accuracy than MODIS IST, particular in thin leads areas, which indicated that Landsat based leads map will provide us a better insight of Arctic sea ice. All the satellite ISTs tend to underestimate the surface temperature than those measured by buoys.

Land Surface Temperature Retrieval by LANDSAT 8 Thermal Band: Applications of Laboratory and Field Measurements

Land surface temperature (LST) plays an important role in a wide variety of scientific studies. Several methodologies to retrieve LST and correct the atmospheric effects for thermal infrared satellite imagery have been developed and all of them require prior knowledge of the land surface emissivity (LSE). The techniques developed for LSE and LST retrieval need to be validated with field measurements. However, in situ measurements are a challenge, being essential to investigate the particularities of each instrument to verify the best approach to collect data and validate the algorithms. Fourier Transform Infrared (FT-IR) spectrometer has been widely used to obtain emissivity of different targets and calculate temperature. The instrument may be used to validate remote sensing data. Moreover, FT-IR allows to collect both emissivity and temperature at the laboratory, thus being an alternative to field validation. We investigated the emissivity dependence of the temperature. In addition, we evaluated the possibility of replacing in situ measurements by laboratory-controlled measurements. We have chosen two single-channel methods to calculate LST and perform the analysis in a Landsat 8 image. We also performed field measurements at the same time as the satellite overpass. FT-IR showed great potential to validate remotely sensed data. However, the instrument requires some time to acquire stability and attention in the calibration process. Laboratory measurements can replace field data, producing approximately 2% of the difference in the LSE. Both single-channel methods provide good accuracy for LST retrieval. Nonetheless, the improved single-channel has superior performance for the study area conditions.

Comparison of Single-channel and Split-window Methods for Estimating Land Surface Temperature from Landsat 8 Data

Abstract: Landsat 8 is the eighth satellite in the Landsat program, which provides images at 11 spectral channels, including 2 thermal infrared bands at a spatial resolution of 100 m (band 10 (10,30÷11,30 µm) and band 11 (11,50÷12,50 µm)). Until now, most studies have used only band 10 of Landsat 8 image to calculate land surface temperature. In this paper, we compare the results of determining a land surface temperature from Landsat 8 thermal infrared data when using a single band (single-channel method) and using both thermal infrared bands (split-window method). 02 Landsat 8 scenes in the dry season 2015 - 2016 in Loc Ninh district (Binh Phuoc province) and Lam Ha district (Lam Dong province) were used to calculate the land surface temperature according to the SC and SW methods. The results obtained in both experiments showed that the land surface temperature, determined from band 10 of Landsat 8 images was significantly higher than using band 11. Meanwhile, the method using both thermal infrared bands of Landsat 8 data (SW method) to calculate land surface temperature has higher accuracy when compared with the method using band 10 or band 11 only (SC method).
 Keywords: Landsat 8, thermal infrared, land surface temperature, split-window algorithm, single-channel algorithm.

An Efficient Method for Single-Channel SAR Target Reconstruction Under Severe Deceptive Jamming

Deceptive jamming can severely degrade synthetic aperture radar (SAR) image quality by introducing high-fidelity false targets. In this letter, a simultaneous deceptive jamming suppression and target reconstruction method for a single-channel SAR system is proposed. The signal model is formulated by constructing a joint dictionary based on different time–frequency distributions of the actual targets and false targets. Then, an efficient algorithm is proposed based on the alternating direction method of multipliers (ADMMs) to simultaneously recover the actual and false targets. Several strategies are also proposed to accelerate the computation. Compared with other existing single-channel SAR deceptive jamming suppression methods, the proposed method has lower reconstruction error and computational load. Simulation results demonstrate the superior performance of the proposed algorithm.

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model

ObjectiveSleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. MethodIn this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies. ResultsUsing a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4–87.4%) and (77.6–80.4%) with Kappa values in the range of 0.7–0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method. Comparison with existing method(s)Our method outperformed the existing methods for all multi-stage classification. ConclusionsThe proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.

A Single-Line Single-Channel Method With Closed-Form Formulas for the Characterization of Dielectric Liquids

This paper presents a method for the measurement of liquid permittivity without using liquid reference materials or calibration standards. The method uses a single transmission line and a single microfluidic channel which intercept the line twice. As a result, two transmission line segments are formed with channel sections to measure liquid samples. In single-connection measurements, the scattering parameters of three transmission line states can be obtained. The three states are both segments filled with air, one segment filled with a liquid sample while the other with air, and both segments filled with liquid. By choosing a 2:1 ratio for the two line segment lengths, closed-form formulas are provided to calculate line propagation constants directly from measured S-parameters. Then, sample permittivity values are obtained. A coplanar waveguide is built and tested with deionized water, methanol, ethanol, and 2-propanol from 0.1 to 9 GHz. The obtained line performance agrees with the simulation results. The obtained sample permittivity values agree with commonly accepted values.

Using Landsat-8 Data in Preliminary Exploration for Geothermal Resources

To characterize geothermal potential areas, conventional surface exploration activities involve field surveys, gathering geothermal information from locals and review of any existing geothermal literature. This is not only time consuming and costly but also unreliable for inaccessible geothermal potential areas. Thus, this study explores the cost-effectiveness and powerful tools of satellite remote sensing in preliminary land surface characterization for expansive geothermal exploration. The main approach entailed the use of free-access Landsat-8 and atmospheric data to retrieve land surface temperature (LST) using split-window and single channel algorithm, analysis of retrieved surface products, validation using in-situ ground temperature data, and finally delineation of surface thermal anomalies associated with geothermal features. Gilgil district and Baringo County in Kenya made the study areas. The former is a known and confirmed geothermal area while the latter is only a geothermal prospect. The two areas sit on the central section of the Kenyan rift; geothermal belt, and combined form a suitable case study for preliminary exploration using Landsat-8 data. The main objective of the study was to demonstrate the use of satellite remote sensing data to identify surface thermal anomalies associated with geothermal features as a cost-effective geothermal exploration support tool. Identify the best LST retrieval method between split window and single channel method using Landsat 8 data, and finally employ the better retrieval method to characterize geothermal prospect area and suggest targets for further investigations. Results showed that free-access satellite remote sensing imagery can conveniently be used to identify and map surface thermal anomalies associated with geothermal features and thus can be employed to complement the main geothermal exploration studies namely geological, geochemical and geophysical. Further, single channel method had better LST retrieval results compared to split-window method when using Landsat-8 data

Feature Extraction Method for EEG based Biometrics

Distinct feature extraction methods are simultaneously used to describe single channel Electroencephalography (EEG) based biometrics. This study proposes a new strategy to extract features from EEG signals. Based on the time and frequency information, the statistics features are obtained from the EEGs. For the dichotomize process, the support vector machine classifier is used with 10-cross-fold in this research. The main contribution of this paper is to propose a simple but effective single-channel EEG feature extraction method and consider feature selection to optimize classification efficiency. In the experiments, the EEG data is obtained from a human-computer interaction environment when the subjects are under the non-stationary states with different emotions. The results show that this proposed method achieves better classification performance on a single-channel EEG system than previous work.

A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses.

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 102 ). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.

Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.

Spatial-temporal variation of land surface temperature of Jubail Industrial City, Saudi Arabia due to seasonal effect by using Thermal Infrared Remote Sensor (TIRS) satellite data

The studies on spatial and temporal variations of Land Surface Temperature (LST) is very essential for understanding the heat energy balance and thermal flux of urban areas. They are also useful for making urban heat transfer models, water resource management, climate change modelling and environmental studies. The present study is to analyze the spatial and temporal variations of the LST of Jubail Industrial City, one of the biggest industrial area in the world. The surface temperature has been estimated by using Landsat 8 Thermal Infrared Remote Sensor (TIRS) satellite data through the single-channel (SC) method. The study provides the variations of surface temperature of the industrial city and reveals that the temperature is relatively low ranging from 10 to 26 °C during the winter month of January and February. However, some parts of the residential area and the industrial of the city have more temperature than the rest of the area. From the month of March, the temperature increases gradually and reaches high in the summer of month of June and July. During this summer period, the surface temperature in the residential area of the city is high around 40 - 50 °C. The temperature in the sub urban areas are moderate, however high temperature (50 - 55 °C) have been recorded in the industrial area of the city. The surface temperature gradually decreases from the month of September up to the peak winter period. However, significant heat islands of temperature more than 60 °C have been noted near the iron and steel factories of the industrial area in all the periods. Correlation analysis has also been carried out to assess relationship between the seasonal changes of the land surface temperature and air temperature of the city. There is no significant strong relationship has been is noticed to correlate the LST and air-temperature throughout the entire period of the study, however the LST and air-temperature shows good relationship during the summer period than the winter season.

Remote sensing application to estimate fish kills by Saprolegniasis in a reservoir

Saprolegniasis is one of the most economical and ecologically harmful diseases in different species of fish. Low water temperature is one of the most important factors which increases stress and creates favourable conditions for the proliferation of Saprolegniasis. Therefore, the monitoring of water surface temperature (WST) is fundamental for a better understanding of Saprolegniasis. The objective of this study was to develop a predictive algorithm to estimate the probability of fish kills caused by Saprolegniasis in Río Tercero reservoir (Argentina). WST was estimated by Landsat 7 and 8 imagery using the Single-Channel method. Logistic regression was used to relate WST estimated from 2007 to 2017 with different episodes of fish kills by Saprolegniasis registered in the reservoir during this period of time. Results showed that the algorithm created with the first quartile (25th percentile) of the WST values estimated by Landsat sensors was the most suitable model to estimate Saprolegniasis in the studied reservoir.

A new apparent shear stress-based approach for predicting discharge in uniformly roughened straight compound channels

Accurately predicting flow discharge in a compound river channel becomes increasingly important when in river eco-environment design and flood risk management, in which the conventional methods, such as divided channel method (DCM) and single channel method, do not meet precision requirement. In the present paper, a new approach is proposed to improve the prediction of flow based on the concept of the apparent shear stress at the diagonal interface between main channel and floodplains. The new approach is compared with a wide range of the author's experimental data and the data available in the literature. The 27 datasets used in this study cover homogenous symmetric channels (22 datasets) and asymmetric channels (5 datasets), which have various width ratios [channel total width (B) at bankfull / main channel bottom (b) = 1.5 ~ 15.8] and bed slopes (So = 2.6 × 10−4 ~2.5 × 10−3). It was found that the new approach can significantly improve the accuracy of flow prediction compared with the conventional DCM for all the datasets, particularly for relatively low flow depths of floodplain (e.g. Dr < 0.15) where the flow discharge was very difficult to predict correctly. The new approach predicts the total discharge well for straight homogeneously symmetric and asymmetric channels, within a mean absolute percentage error (MAPE) of 5%. Furthermore, compared with the DCM, the new approach can also have a better prediction of zonal discharge percentage and it has less variation of discharge prediction error over the flow depth.