dB Signal-to-noise Ratio Research Articles

Modeling and investigation on the performance enhancement of hovering UAV-based FSO relay optical wireless communication systems under pointing errors and atmospheric turbulence effects

This paper investigates and enhances unmanned aerial vehicle (UAV) relay-assisted free-space optics (FSO) optical wireless communication (OWC) systems under the effects of pointing errors (PEs) and atmospheric turbulences (ATs). The incorporation of UAVs as buffer-aided moving relays in the conventional FSO (CFSO) relay-assisted systems is proposed for enhancing the performance of PEs through AT. Using M-PSK (phase shift keying) and M-QAM (quadrature amplitude modulation), the impact of PEs on transmission quality is evaluated in this work. We evaluate and optimize the symbol error rate, outage probability (OP), and signal-to-noise ratio (SNR) for the UAV-to-ground station-based FSO communications systems. The spatial diversity-based relay-assisted CFSO systems can enhance the performance of the UAV-UAV FSO links. In this paper, a new FSO (NFSO) channel model for the hovering UAV-FSO OWC fluctuations under the PEs, AT effects, jitter, deviation, receiving an error, and wind resistance effects are established. To improve the performance of hovering UAV-based FSO relay OWC systems. We reduce the influence of UAV-FSO OWC fluctuations under PEs and AT effects. By receiving incoherent signals at various locations, the spatial diversity-based relay-assisted NFSO systems can significantly increase the system's redundancy and enhance connection stability. Numerical results show that to achieve a bit-error-rate (BER) of le 10^{ - 5}, the required SNR is ≥ 23 dB when the wind variance of the UAVs sigma_{alpha }^{2} increases from 0 to 7 mrad with FSO link distance L = 2000 m. The required SNR is ≥ 25 dB when the wind variance sigma_{alpha }^{2} is 1 mrad at an OP of 10^{ - 6}. To obtain an average BER of 10^{ - 6}, the SNR should be 16.23 dB, 17.64 dB, and 21.45 dB when sigma_{alpha }^{2} is 0 mrad, 1 mrad, and 2 mrad, respectively. Using 8-PSK modulation without PEs requires 23.5 dB at BER of 10^{ - 8} while 16-QAM without PEs requires 26.5 dB to maintain the same BER of 10^{ - 8}. Compared with 16-QAM without PEs, the SNR gain of 8-PSK without PEs is 3 dB. The results show the relay-assisted UAV-FSO system with five stationary relays can achieve BER 10^{ - 8} at 25 dB SNR in the ideal case and 10^{ - 5} at 27 dB SNR with AT and PE at FSO length 1000 m. The results show the relay UAV-FSO system outperforms the CFSO at the BER and SNR performance. The effects of UAV’FSO s fluctuation increase when the UAV-FSO link length, {text{L}}_{{{text{fso}}}} increases. The results of the weak turbulence achieve better SER compared with MT and ST. The obtained results show that decreasing {text{L}}_{{{text{fso}}}} can compensate for the effects of UAV-FSO link fluctuation on the proposed system. Finally, we investigated the CFSO relay-assisted UAV-FSO system with aided NFSO-UAVs spatial diversity-based relay-based on NFSO OWC and revealed the benefits of the resulting hybrid architecture.

The Relationship Between Cochlear Implant Speech Perception Outcomes and Electrophysiological Measures of the Electrically Evoked Compound Action Potential.

This study assessed the relationship between electrophysiological measures of the electrically evoked compound action potential (eCAP) and speech perception scores measured in quiet and in noise in postlingually deafened adult cochlear implant (CI) users. It tested the hypothesis that how well the auditory nerve (AN) responds to electrical stimulation is important for speech perception with a CI in challenging listening conditions. Study participants included 24 postlingually deafened adult CI users. All participants used Cochlear Nucleus CIs in their test ears. In each participant, eCAPs were measured at multiple electrode locations in response to single-pulse, paired-pulse, and pulse-train stimuli. Independent variables included six metrics calculated from the eCAP recordings: the electrode-neuron interface (ENI) index, the neural adaptation (NA) ratio, NA speed, the adaptation recovery (AR) ratio, AR speed, and the amplitude modulation (AM) ratio. The ENI index quantified the effectiveness of the CI electrodes in stimulating the targeted AN fibers. The NA ratio indicated the amount of NA at the AN caused by a train of constant-amplitude pulses. NA speed was defined as the speed/rate of NA. The AR ratio estimated the amount of recovery from NA at a fixed time point after the cessation of pulse-train stimulation. AR speed referred to the speed of recovery from NA caused by previous pulse-train stimulation. The AM ratio provided a measure of AN sensitivity to AM cues. Participants' speech perception scores were measured using Consonant-Nucleus-Consonant (CNC) word lists and AzBio sentences presented in quiet, as well as in noise at signal-to-noise ratios (SNRs) of +10 and +5 dB. Predictive models were created for each speech measure to identify eCAP metrics with meaningful predictive power. The ENI index and AR speed individually explained at least 10% of the variance in most of the speech perception scores measured in this study, while the NA ratio, NA speed, the AR ratio, and the AM ratio did not. The ENI index was identified as the only eCAP metric that had unique predictive power for each of the speech test results. The amount of variance in speech perception scores (both CNC words and AzBio sentences) explained by the eCAP metrics increased with increased difficulty under the listening condition. Over half of the variance in speech perception scores measured in +5 dB SNR noise (both CNC words and AzBio sentences) was explained by a model with only three eCAP metrics: the ENI index, NA speed, and AR speed. Of the six electrophysiological measures assessed in this study, the ENI index is the most informative predictor for speech perception performance in CI users. In agreement with the tested hypothesis, the response characteristics of the AN to electrical stimulation are more important for speech perception with a CI in noise than they are in quiet.

Delta-band neural envelope tracking predicts speech intelligibility in noise in preschoolers

Behavioral tests are currently the gold standard in measuring speech intelligibility. However, these tests can be difficult to administer in young children due to factors such as motivation, linguistic knowledge and cognitive skills. It has been shown that measures of neural envelope tracking can be used to predict speech intelligibility and overcome these issues. However, its potential as an objective measure for speech intelligibility in noise remains to be investigated in preschool children. Here, we evaluated neural envelope tracking as a function of signal-to-noise ratio (SNR) in 14 5-year-old children. We examined EEG responses to natural, continuous speech presented at different SNRs ranging from −8 (very difficult) to 8 dB SNR (very easy). As expected delta band (0.5-4 Hz) tracking increased with increasing stimulus SNR. However, this increase was not strictly monotonic as neural tracking reached a plateau between 0 and 4 dB SNR, similarly to the behavioral speech intelligibility outcomes. These findings indicate that neural tracking in the delta band remains stable, as long as the acoustical degradation of the speech signal does not reflect significant changes in speech intelligibility. Theta band tracking (4-8 Hz), on the other hand, was found to be drastically reduced and more easily affected by noise in children, making it less reliable as a measure of speech intelligibility. By contrast, neural envelope tracking in the delta band was directly associated with behavioral measures of speech intelligibility. This suggests that neural envelope tracking in the delta band is a valuable tool for evaluating speech-in-noise intelligibility in preschoolers, highlighting its potential as an objective measure of speech in difficult-to-test populations.

Segmented Radon Fourier Transform for Long-Time Coherent Radars

Coherent pulse integration is a well-known technique for effective target detection with increased output signal-to-noise ratio (SNR). Radon fourier transform (RFT) uses long-time coherent integration technique for improved target detection in a 3-dimensional search but increases the parameters search time. This paper presents a new approach of Segmented Radon Fourier Transform (SRFT) for fast detection of high speed targets without significantly degrading the detection performance. SRFT divides the received echo data matrix into an arbitrary number of segments along the slow time dimension of the data matrix. Each segment is a complete independent data matrix which can proceed independently and in parallel fashion. This paper implements a complete pipeline of radar receiver with SRFT to validate its integration in a radar signal processor. Experiments show that the parameters search time of SRFT is reduced by N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> times when compared with RFT, where N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> is the number of segments. There is a negligible degradation of 0.035dB in SNR when compared with standard RFT for 120 pulses. The difference is further reduced as the number of pulses are increased.

Fault Detection and Classification to Design a Protection Scheme for Utility Grid with High Penetration of Wind and Solar Energy

This paper designed a protection scheme for utility grid with high share of renewable energy (RE) generated from wind energy and solar energy plants. This is based on extraction of features from the current using Stockwell transform (ST), Hilbert transform (HT), and alienation coefficient. A Stockwell index (SI) is designed by extracting current features using ST, a Hilbert index (HI) is designed by extracting current features using HT, and an alienation index (AI) is designed by extracting current features using an alienation coefficient. A fault index (FI) is formulated by multiplying the SI, HI, AI, and WF (weight factor). This FI is implemented for fault detection. Fault classification is achieved considering number of fault phases and ground fault index (GFI). This GFI is designed by processing zero sequence currents applying ST. GFI effectively identifies the ground involved during event of a fault. A designed protection scheme is effective to identify faults in the scenario of high RE share and during various cases of study which includes the variations of fault impedance, different fault occurrence angles (FOA), fault incident at different nodes, and noisy condition. This protection scheme effectively discriminates the fault events from the operational events such as feeder operation, load, and capacitor switching. Performance of hybrid protection method formulated in this paper is better relative to alienation coefficient-based protection scheme (ACPS) reported in literature. The ACPS has maximum error and mean error of fault detection equal to 9.54% and 5.99%, respectively, which is relatively high compared to the respective values for the proposed method which are 1.89% and 0.978%, respectively. ACPS is effective for detecting the fault events in noise level of 30 dB SNR (signal-to-noise ratio) whereas the proposed method effectively identifies the faults in the high noise scenario of 20 dB SNR.

RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results: The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Toward a Listening Training Paradigm: Evaluation in Normal-Hearing Young and Middle-Aged Adults.

Adults' most common complaint relates to understanding speech in noisy environments. Hearing aids can compensate for sensory loss but do not restore hearing to normal. Listening training has the potential to partially remediate these problems. In this study, we propose and evaluate a Flemish version of a listening training paradigm, which involves the use of cognitive control as well as auditory perception. The paradigm entails a discrimination task where participants are cued to attend to one of two concurrent talkers with target speakers randomly varying between a female and a male voice. We evaluate learning effects, different scenarios, and masking types. In total, 70 young and 54 middle-aged adults participated in this study. Each adult performed one or more conditions. Participants' hearing was screened before participation, all middle-aged adults passed a cognitive screening task. Analyses indicated learning effects across scenarios that were similar in terms of speech intelligibility. Our results indicated better speech intelligibility when the female speaker was the target, but speech intelligibility did not differ when the target was the male speaker. An unintelligible masker noise results in worse speech intelligibility than a competing talker masker. Our results suggest that listeners could use an intensity cue to identify and/or select the target speaker when presented at a lower signal to noise ratio (SNR). Error analysis suggested higher cognitive control demands when the target and masker were presented at similar levels (i.e., around 0 dB SNR). The addition of independent trials with the intensity of target and masker reversed improved speech intelligibility. Inhibitory control, but not task switching, was reliably correlated with listening performance. The proposed paradigm turned out to be feasible and practicable, demonstrating its potential for training speech intelligibility in noise. We believe that this training paradigm can generate real-life benefits including for persons with hearing loss. This latter application awaits future evaluation.

A Comparison of the Performance of Four Acoustic Modulation Techniques for Robot Communication in Pipes

Autonomous robotics is an emerging technology for the inspection of a vast network of underground pipes. Communication between autonomous robots is essential to optimise their efficiency and network coverage. However, sending a message acoustically is not a well-researched topic because most of the existing literature is devoted to the study of the acoustic properties of the pipe for the purpose of sensing rather than communication. In particular, the influence of multi-modal propagation and background noise on the quality of acoustic communication in pipes has not been well understood. This paper studies the performance of four standard acoustic communication techniques in a dry drainage pipe to fill this knowledge gap. The noise resistance, communication range and data rate of these techniques are estimated through numerical simulations and laboratory experiments. It has been found that the techniques based on shift keying requires at least 5--10 dB signal to noise ratio (SNR) to function properly while the chirp linear frequency modulation technique can operate reliably with SNR = 0 dB or event lower SNR. The results also suggest that the multi-modal propagation in the pipe has significant effects on the package error rate. The frequency dependent sound attenuation in the pipe also affects the communication range and data rate. In particular, for a 150 mm diameter dry pipe the maximum robot operation distance is likely to be limited to 50--100 m with the highest carrier frequency of around 10 kHz and data rates below 6300 bps. The results of this work pave the way to the development of acoustic communication modules to be deployed on tetheless robots designed to inspect a buried pipe network autonomously and collaboratively.

A Simple Capacity-Achieving Scheme for Channels With Polarization-Dependent Loss

We demonstrate, for a widely used model of channels with polarization dependent loss (PDL), that channel capacity is achieved by a simple interference cancellation scheme in conjunction with a universal precoder. Crucially, the proposed scheme is not only information-theoretically optimal, but it is also exceptionally simple and concrete. It transforms the PDL channel into separate scalar additive white Gaussian noise channels, allowing off-the-shelf coding and modulation schemes designed for such channels to approach capacity. The signal-to-noise ratio (SNR) penalty incurred under 6 dB of PDL is reduced to the information-theoretic minimum of a mere 1 dB as opposed to the 4 dB SNR penalty incurred under naive over-provisioning.

Experimental impact of power re-optimization in a mesh network

We experimentally observe the signal-to-noise ratio (SNR) degradation of previously established services induced by loading new services in a network, and we mitigate this degradation by periodic power re-optimization via two different strategies: (1) a static strategy based on end-of-life parameters and (2) a dynamic strategy based on real-time monitoring to the current state of the network. We use a mesh network testbed of four nodes and five links with commercial equipment only. We observe up to 3.4 dB SNR degradation on the previously established services due to the loading of new services. Then we demonstrate an improvement of up to 3.2 dB in the network margin achieved by applying our proposed power re-optimization strategy.

Individual variability in speech recognition with compression and beamforming at positive signal-to-noise ratios

Individuals with hearing loss often struggle to communicate in noisy conditions. Aggressive hearing aid (HA) processing, such as fast-acting wide dynamic range compression (WDRC), may reduce speech recognition under some circumstances, and there is concern about disproportionate reductions in listeners with poor working memory. We investigated how speech recognition is affected when WDRC is combined with highly directional beamforming intended to reduce noise from lateral and rear locations. We also sought to determine how listener factors—working memory, age, and hearing loss—affect such interactions in typical listening conditions, including positive signal-to-noise ratios (SNR) and different spatial noise locations. To-date 22 listeners with mild to moderately severe sensorineural hearing loss repeated low-context sentences (0°) mixed with six-talker interference at + 3 and + 8 dB SNRs, distributed equally in two ( + /−90°) or three ( + /−90°/180°) locations. Wearable HAs presented four signal processing combinations, matched for audibility: microphone directionality (omnidirectional or beamformer) and WDRC (fast- or slow-acting). Data show higher speech recognition with beamforming (re omnidirectional), especially at + 3 dB SNR, regardless of WDRC and spatial locations. Relative contributions of listener factors will be assessed in mixed models in the context of combined signal distortions from HA processing and interferers. [Work supported by NIH-K01DC018324.]

Auditory Electrophysiological and Perceptual Measures in Student Musicians with High Sound Exposure

This study aimed to determine (a) the influence of noise exposure background (NEB) on the peripheral and central auditory system functioning and (b) the influence of NEB on speech recognition in noise abilities in student musicians. Twenty non-musician students with self-reported low NEB and 18 student musicians with self-reported high NEB completed a battery of tests that consisted of physiological measures, including auditory brainstem responses (ABRs) at three different stimulus rates (11.3 Hz, 51.3 Hz, and 81.3 Hz), and P300, and behavioral measures including conventional and extended high-frequency audiometry, consonant-vowel nucleus-consonant (CNC) word test and AzBio sentence test for assessing speech perception in noise abilities at -9, -6, -3, 0, and +3 dB signal to noise ratios (SNRs). The NEB was negatively associated with performance on the CNC test at all five SNRs. A negative association was found between NEB and performance on the AzBio test at 0 dB SNR. No effect of NEB was found on the amplitude and latency of P300 and the ABR wave I amplitude. More investigations of larger datasets with different NEB and longitudinal measurements are needed to investigate the influence of NEB on word recognition in noise and to understand the specific cognitive processes contributing to the impact of NEB on word recognition in noise.

A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios

The present work, unlike others, does not try to reduce the noise in hyperspectral images to increase the semantic segmentation performance metrics; rather, we present a classification framework for noisy Hyperspectral Images (HSI), studying the classification performance metrics for different SNR levels and where the inputs are compressed. This framework consists of a 3D Convolutional Neural Network (3DCNN) that uses as input data a spectrally compressed version of the HSI, obtained from the Tucker Decomposition (TKD). The advantage of this classifier is the ability to handle spatial and spectral features from the core tensor, exploiting the spatial correlation of remotely sensed images of the earth surface. To test the performance of this framework, signal-independent thermal noise and signal-dependent photonic noise generators are implemented to simulate an extensive collection of tests, from 60 dB to −20 dB of Signal-to-Noise Ratio (SNR) over three datasets: Indian Pines (IP), University of Pavia (UP), and Salinas (SAL). For comparison purposes, we have included tests with Support Vector Machine (SVM), Random Forest (RF), 1DCNN, and 2DCNN. For the test cases, the datasets were compressed to only 40 tensor bands for a relative reconstruction error less than 1%. This framework allows us to classify the noisy data with better accuracy and significantly reduces the computational complexity of the Deep Learning (DL) model. The framework exhibits an excellent performance from 60 dB to 0 dB of SNR for 2DCNN and 3DCNN, achieving a Kappa coefficient from 0.90 to 1.0 in all the noisy data scenarios for a representative set of labeled samples of each class for training, from 5% to 10% for the datasets used in this work. The source code and log files of the experiments used for this paper are publicly available for research purposes.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

Do Visual Cues Aid Comprehension of a Dialogue?

Do Visual Cues Aid Comprehension of a Dialogue?

Binaural Hearing Using the ADHEAR Bone Conduction System in the Monaurally Occluded Ear

Introduction: The study aimed to investigate binaural cues in the rehabilitation of unilateral occluded ears with a bone conduction hearing aid. Methods: The study sample consisted of 40 adult volunteers with normal hearing. Unilateral pseudo-conductive hearing loss was induced by inserting an earplug into the external auditory canal (EAC) and silicone material in the concha for ear impression. The adaptive speech-in-noise test (Italian Matrix test) was performed in three spatial orientations to assess binaural cues (summation, squelch, and head shadow effects). All evaluations were performed in the normal condition, after EAC occlusion, and after application of an adhesive bone conduction hearing aid. Binaural contrast differences were calculated in the three conditions. Results: In the EAC occlusion condition, there was a significant increase in the signal-to-noise ratio (SNR) in both the S₀N₀ (2.4 dB) and the S₉₀N₋₉₀ (7.7 dB) settings, and a slight albeit significant increase in the S₀N₉₀ setting (1.35 dB). After fitting the BC hearing aid, there was a reduction of −1.8 dB SNR (p < 0.001) in the S₀N₀ setting and −2 dB (p = 0.003) in the S₉₀N₋₉₀ setting. There was no improvement in the SNR (p = 0.405) in evaluation of the squelch effect (S₀N₉₀). These data were corroborated by a better binaural contrast due to a reduction in the summation effect in the monaural occlusion condition and a subsequent reduction in binaural contrast after fitting the hearing aid due to an increase in the summation effect (−2.5 dB vs. 0.3 dB; p < 0.001). Conclusions: Application of a bone conduction hearing aid in unilateral pseudo-conductive hearing loss strengthens speech recognition of noise by improving the summation effect and impeding the shadow effect of the head; however, there appears to be no improvement in speech perception in noise due to spatial release from masking.

Dual-tandem phased array method for imaging of near-vertical defects in narrow-gap welds

Near-vertical flaws in thick-section narrow-gap welds are notoriously difficult to detect and size using traditional Phased Array Ultrasonic Testing (PAUT) techniques. Self-tandem shear inspection has been considered for detection but lacks appropriate full weld sensitivity. The dual-tandem phased array inspection method is proposed to enable multi-mode Total Focusing Method (TFM) pulse-echo imaging with longitudinal through-transmission inspection, by introducing a second, opposite-facing probe on the far weld side. Pulse-echo and through-transmission datasets are obtained in a single Full Matrix Capture (FMC) acquisition, for each weld side. Analysis of the optimum Probe Centre Spacing (PCS) based upon image sensitivity suggests that a spacing corresponding to a longitudinal beam crossover at 2/3 sample thickness provides greatest weld coverage. Testing on near-vertical EDM notches in a 120 mm thick mock narrow-gap carbon steel sample has shown high sensitivity TFM imaging, at notch depths of 27.5 mm, 60 mm and 92.5 mm. Pitch-catch through-weld transmission imaging has exhibited tip-diffraction images with a Signal-to-Noise Ratio (SNR) of up to 17.3 dB. Pulse-echo imaging using multi-mode TFM has shown tip-diffraction and reflection with up to 19.2 dB and 34.8 dB SNR respectively. The possibility of image mixing by considering image defect response type has provided full 2D notch reconstruction. These results demonstrate the strength of the dual-tandem method in enhancing the detection reliability and detection of near-vertical defects in thick-section narrow-gap welds.

Cooperative VLC system using OOK modulation with imperfect CSI

Visible light communication (VLC) is one of the aspirant tools for high data rate communication. VLC system employed in a large room requires cooperative transmission for reliable end-to-end communication. Cooperative communication (CC) utilizes several relay approaches to forward the message from transmitter to envisioned destination, and thus, attains high spatial diversity gain, reduced power consumption, improved transmission reliability, increased coverage area, and potentially enhanced system capacity. This paper investigates the error performance of on-off keying (OOK) modulated VLC system in a cooperative environment, where the relay adopts decode-and-forward (DF) protocol. In particular, two cases of cooperative VLC system are considered; (1) Case I, in which the destination node is in direct link with the previous node while in blockage or outside the field of view (FoV) for all the other nodes, (2) Case II, in which the destination node is in direct link with all upstream nodes. For the practical suitability of the system, the imperfect channel state information (CSI) is considered for performance analysis. The mathematical expressions for bit-error-rate (BER) of the considered systems are derived in the presence of perfect and imperfect CSI. The error performance of the considered systems is investigated for the various system parameters such as signal-to-noise ratio (SNR), channel estimation error and receiver locations. The result shows that for the case of blockage, i.e., Case I, the end-to-end communication for two-hop network can be established with the BER of 2 × 10−4 at 128 dB SNR and channel estimation error of 10−12. Further, in the case of non-blockage, i.e., Case II, the result shows that for two-hop communication, the BER in cooperative transmission improved to 5 × 10−5 in compare to 5 × 10−2 in direct communication, at 128 dB SNR and 10−12 channel estimation error. The extensive simulations are carried out to verify the analytical findings.

A sound source localization method based on improved second correlation time delay estimation

The sound source localization (SSL) system based on the microphone array has important applications in audio and video conference, security monitoring and intelligent cockpit. However, the SSL method based on time difference of arrival is susceptible to ambient noise. Therefore, an improved second correlation delay estimation algorithm is proposed in this paper. The pure source signal is obtained by wavelet denoising, and then the time delay is calculated by the second correlation time delay estimation algorithm with the weighting functions of the smoothed coherence transform and the Roth processor. The position of the sound target is calculated from the time delay. Aiming at the moving target, an extended Kalman filter is introduced to track the moving trajectory of the sound source. The static and moving SSL simulations are conducted and the results of the proposed algorithm are compared with those of the single-weighted quadratic correlation (SQC) algorithm and the high-power quadratic correlation algorithm. The static sound source positioning errors of the proposed algorithm under −10 dB SNR are respectively 3.97 m and 5.86 m smaller than those of the HQC algorithm and the SQC algorithm. The moving SSL trajectory based on the proposed algorithm is still closest to the real track under −10 dB SNR. This indicates that the proposed algorithm has high precision and strong robustness for sound source location in the low signal-to-noise ratio (SNR) environment. In the experiment, the proposed algorithm can accurately calculate the direction of arrival (DOA) of static sound source and stably track DOA of moving sound source. This is consistent with the simulation results, which further verifies the effectiveness and practicability of the algorithm. This novel algorithm with high time delay estimation accuracy is of great significance for SSL in low SNR environment.