Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone.

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of -17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring.

An Adaptive Search Algorithm for Detecting Respiratory Artifacts Using a Wireless Passive Wearable Device.

With the use of a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor, the proposed algorithm can estimate biomedical feedback such as respiratory activity. Variations in physical properties of Radio Frequency Identification (RFID) signals can be used to wirelessly detect physiological processes and states. However, it is typical for ambient noise artifacts to appear in the RFID signal making it difficult to identify physiological processes. This paper introduces a new technique for finding these repetitive physiological signals and identifying them into two states, active and inactive, using k-means clustering. The algorithm detects these biomedical events without the need to completely remove the noise components using a semi-unsupervised approach, and with these results, predict the next biomedical event using these classification results. This approach enables real-time noninvasive monitoring for use with actuating medical devices for therapy. Using this approach, the algorithm predicts the onset of respiratory activity in a simulated environment within approximately one second.

Unsupervised Learning for Monaural Source Separation Using Maximization⁻Minimization Algorithm with Time⁻Frequency Deconvolution.

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time⁻frequency deconvolution with optimized fractional ß-divergence. The ß-divergence is a group of cost functions parametrized by a single parameter ß. The Itakura⁻Saito divergence, Kullback⁻Leibler divergence and Least Square distance are special cases that correspond to ß=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of ß that includes fractional values. It describes a maximization⁻minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time⁻frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional ß value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.

Physiological Recording in the MRI Environment (PRiME): MRI-Compatible Hemodynamic Recording System.

Hemodynamic recording during interventional cardiovascular procedures is essential for procedural guidance, monitoring patient status, and collection of diagnostic information. Recent advances have made interventions guided by magnetic resonance imaging (MRI) possible and attractive in certain clinical scenarios. However, in the MRI environment, electromagnetic interference (EMI) can cause severe distortions and artifacts in acquired hemodynamic waveforms. The primary aim of this paper was to develop and validate a system to minimize EMI on electrocardiogram (ECG) and invasive blood pressure (IBP) signals. A system was developed which incorporated commercial MRI compatible ECG leads and pressure transducers, custom electronics, user interface, and adaptive signal processing. Measurements were made on pediatric patients (N = 6) during MRI-guided catheterization. Real-time interactive scanning, which is known to produce significant EMI due to fast gradient switching and varying imaging plane orientations, was selected for testing. The effectiveness of the adaptive algorithms was determined by measuring the reduction of noise peaks, amplitude of noise peaks, and false QRS triggers. During real-time gradient-intensive imaging sequences, peak noise amplitude was reduced by 80% and false QRS triggers were reduced to a median of 0. There was no detectable interference on the IBP channels. A hemodynamic recording system front-end was successfully developed and deployed, which enabled high-fidelity recording of ECG and IBP during MRI scanning. The schematics and assembly instructions are publicly available to facilitate implementation at other institutions. Researchers and clinicians are provided a critical tool in investigating and implementing MRI guided interventional cardiovascular procedures.

Cloud-based adaptive exon prediction for DNA analysis.

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database.

Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance.

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman-LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

A novel approach for SEMG signal classification with adaptive local binary patterns.

Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

Development of a method to compensate for signal quality variations in repeated auditory event-related potential recordings.

Reliable measurements are mandatory in clinically relevant auditory event-related potential (AERP)-based tools and applications. The comparability of the results gets worse as a result of variations in the remaining measurement error. A potential method is studied that allows optimization of the length of the recording session according to the concurrent quality of the recorded data. In this way, the sufficiency of the trials can be better guaranteed, which enables control of the remaining measurement error. The suggested method is based on monitoring the signal-to-noise ratio (SNR) and remaining measurement error which are compared to predefined threshold values. The SNR test is well defined, but the criterion for the measurement error test still requires further empirical testing in practice. According to the results, the reproducibility of average AERPs in repeated experiments is improved in comparison to a case where the number of recorded trials is constant. The test-retest reliability is not significantly changed on average but the between-subject variation in the value is reduced by 33-35%. The optimization of the number of trials also prevents excessive recordings which might be of practical interest especially in the clinical context. The efficiency of the method may be further increased by implementing online tools that improve data consistency.

Real-time estimation of pathological tremor parameters from gyroscope data.

This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading.