Multichannel Approaches Research Articles

A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures

Vibration-based damage identification methods are crucial for structural health monitoring. In recent years, deep learning methods, such as convolutional neural networks (CNNs), have been widely used for damage identification due to their superior performance. To enhance the accuracy of damage identification for complex frame structures and effectively utilize data measured from multiple sensors, this study proposes and compares four different damage identification methods based on CNN and four different kinds of multi-channel data fusion approaches, i.e., data splicing, matrix reconstruction, data dimension elevation, and sub-model integration. Firstly, the original one-dimensional vibration signals of the frame structures are measured. Then, multi-channel data fusion is performed on the signals to construct datasets used as input to train the CNN model. Finally, the sensitive damage features are automatically extracted using the trained CNN models after parameter tuning, and the structural damage patterns are identified. The effectiveness of these damage identification methods is evaluated using the numerical model of IASC-ASCE Benchmark structure, the Qatar University Grandstand Simulator test, and a three-layer stainless steel frame structure test. The efficiency of damage identification methods using multi-channel data is compared to those using only single-channel data. Additionally, the impact of noise on damage identification accuracy is also analyzed. The results demonstrate that the proposed four different damage identification methods based on CNN and multi-channel data fusion, outperform the methods that solely rely on single-channel data. Furthermore, these methods significantly enhance the accuracy of damage identification.

Fusion of Single and Integral Multispectral Aerial Images

An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.

Bound and autoionizing potential energy curves in the CH molecule

This article presents a method of computing bound state potential curves and autoionizing curves using fixed-nuclei R-matrix data extracted from the Quantemol-N software suite. It is a method based on two related multichannel quantum-defect theory approaches. One is applying bound-state boundary conditions to closed-channel asymptotic solution matrices, and the other is searching for resonance positions via eigenphase shift analysis. We apply the method to the CH molecule to produce dense potential-curve datasets presented as graphs and supplied as tables in the publication supplement.

Efficacy and feasibility of SMS m-Health for the detection of adverse events following immunisation (AEFIs) in resource-limited setting-The Zimbabwe stimulated telephone assisted rapid safety surveillance (Zm-STARSS) randomised control trial

IntroductionThe mHealth active participant centred (MAPC) adverse events following immunisation (AEFI) surveillance is a promising area for early AEFI detection resulting in risk minimisation. Passive (spontaneous) AEFI surveillance is the backbone for vaccine pharmacovigilance, but has inherent drawbacks of under reporting, and requires strengthening with active surveillance methods. AimThe Zimbabwe stimulated telephone assisted rapid safety surveillance (Zm-STARSS) randomised controlled trial (RCT) sought to evaluate the efficacy and feasibility of AEFI detection using a short message service (SMS) and computer assisted telephone interview (CATI) approach. MethodA multicentre Zm-STARSS RCT enrolled consented adult vaccinees or parents or guardians of children receiving vaccines, including COVID-19 vaccines, at study vaccination clinics. At enrolment study participants were randomised to either SMS-CATI group or control group. SMS prompts were sent on days 0–2 and 14 post-vaccination to SMS-CATI group to ascertain if a medically attendance or attention due to an Adverse event following immunisation (AEFI) had occurred. However, no SMSs were sent to the control group. SMS-CATI group who responded “Yes” to SMS prompts were interviewed by research healthcare workers (RHCWs) who completed a CATI to determine if an AEFI had occurred whilst an AEFI in control group was determined from passive AEFI reporting channels. The primary study outcome was the AEFI detection rate in the SMS-CATI group compared to the control group. ResultsA total of 4560 participants were enrolled after signed informed consent, all were encouraged to report AEFIs and randomised automatically on 1:1 basis into two arms SMS CATI intervention group (n = 2280) and a control passive AEFI surveillance group (n = 2280) on day 0. A total of 704 (31 %) participants responded to the SMS prompts, with 75 % (528/704) indicating “No” and 25 % (176/704) reporting “Yes” to seeking medical attention or attendance post-immunisation. 69 % (121/176) completed a CATI survey but in only 36 % (44/121) was the AEFI confirmed. There were no AEFIs reported in control group participants. The detection rate of a AEFI associated with medically attendance or attention using the SMS-CATI methodology was 2 % (44/2280) on an intention to treat cohort. ConclusionDespite the low SMS response and CATI completion rate, we demonstrated that Zm-STARSS SMS system improves AEFI detection compared to passive AEFI surveillance. We recommend that this and similar approaches are explored further using cost-effective multi-channel digital approaches for holistic pharmacovigilance to improve AEFI detection in Low Middle-Income Countries (LMICs) for all vaccines.

Health Communication as a Viable Tool in the Control and Prevention of Meningitis in Nigeria: An Analytical Audience Perspective

Meningitis continues to be a significant public health challenge in Nigeria, leading to substantial morbidity and mortality rates. Effective control and prevention strategies are crucial in mitigating the impact of this infectious disease. It is in the light of this that this study therefore, from an analytical audience perspective, examines health communication as a viable tool in the control and prevention of meningitis in Nigeria. The objective of this study is basically to examine the impact of health communication interventions on the knowledge and preventive behavior related to meningitis among the target audience in Nigeria. This study is anchored on the attitude change theory; and data were collected through the use of questionnaires. Findings from this study show that health communication interventions have been impactful because it has influenced the knowledge and preventive behavior of the respondents as regards to the control and prevention of meningitis. The study also revealed that health communication strategies have been highly effective in the control and prevention of meningitis in Nigeria. This study recommends that in order to enhance the effectiveness of health communication as a viable tool in the control and prevention of meningitis in Nigeria, the multi-channel communication approaches should be strengthened.

Exploring Customer Journeys in the Context of Dentistry: A Case Study.

This case study aims to explore how customer journey concepts can apply to a hypothetical scenario, centring on a patient (customer persona) within the dentistry arena, and with a particular focus on special care dentistry. As an educational exercise, this paper may inform dental and allied professionals on how aspects of the customer journey notion may be embedded into their own practices, so that patient-centricity might be better optimised. The hypothetical scenario considers the organisational context, customer persona, contemporary customer purchase decision-making models, and marketing approaches. These components are used to create a customer journey map to help visualise and identify the varying customer-business interactions. The customer journey, focussing on the awareness, initial consideration, active evaluation, pre-purchase, purchase and post-purchase stages, is then conceptually analysed. The analyses reveal that there are areas of friction, attributable to numerous factors. The case study recommends that by introducing digitalisation and omnichannel marketing, alongside existing internally generated and multi-channel marketing approaches, considerable improvements may be achievable. As the patient technology landscape becomes more digital and dental organisations face fiercer competition, dental care providers relying on traditional marketing approaches may well need to adapt and introduce innovative, yet cost-effective digitalisation and omnichannel marketing approaches. Nevertheless, dental care providers, and dental and allied professionals must uphold an underlying duty of care, ensuring that all practises are legal, decent, honest, truthful, and above all ethical.

High-throughput and energy-efficient data gathering in heterogeneous multi-channel wireless sensor networks using genetic algorithm

The need for high throughput in Wireless Sensor Networks (WSNs) necessitates the use of the Multi-Channel (MC) scheme. Existing multi-channel approaches used single or multi-radio sensors to exploit channel resources. The former scheme yields low throughput as well as considerable overhead for channel switching. On the other hand, the latter is costly due to installing multiple radios per node. This study applies the MC scheme to heterogeneous WSNs to bridge this gap. It assumes some super nodes that are equipped with multiple radios. These nodes are used as Cluster Heads (CHs) and are responsible for delivering the gathered data from normal sensors to the Base Station (BS). A high-throughput and energy-efficient algorithm is proposed to perform clustering, routing, and channel assignment in the heterogeneous setting. The proposed method decouples the original problem into two phases and solves them using genetic algorithm. The first phase is devoted to constructing a spanning tree over super nodes and assigning proper channels to their radios. A novel multi-objective cost function is proposed which prolongs network lifetime considerably compared to existing tree construction approaches. It also increases throughput by balancing the observed interference throughout the network. In the second phase of the proposed algorithm, the proper CH and channel per normal node are determined. The extensive simulations demonstrate that the proposed algorithm obtains high throughput using multiple channels. Additionally, it improves total energy consumption and energy exhaustion per delivered bit to the BS by 21.6% and 48.3% compared to the previous schemes.

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

Colored Texture Analysis Fuzzy Entropy Methods with a Dermoscopic Application.

Texture analysis is a subject of intensive focus in research due to its significant role in the field of image processing. However, few studies focus on colored texture analysis and even fewer use information theory concepts. Entropy measures have been proven competent for gray scale images. However, to the best of our knowledge, there are no well-established entropy methods that deal with colored images yet. Therefore, we propose the recent colored bidimensional fuzzy entropy measure, , and introduce its new multi-channel approaches, and , for the analysis of colored images. We investigate their sensitivity to parameters and ability to identify images with different irregularity degrees, and therefore different textures. Moreover, we study their behavior with colored Brodatz images in different color spaces. After verifying the results with test images, we employ the three methods for analyzing dermoscopic images of malignant melanoma and benign melanocytic nevi. , , and illustrate a good differentiation ability between the two—similar in appearance—pigmented skin lesions. The results outperform those of a well-known texture analysis measure. Our work provides the first entropy measure studying colored images using both single and multi-channel approaches.

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review.

The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.

Processing of Multichannel Sliding Spotlight SAR Data with Large Pulse Bandwidth and Azimuth Steering Angle

Azimuth multichannel synthetic aperture radar (SAR) working in the sliding spotlight mode can effectively achieve ultrahigh resolution and wide swath imaging. However, multichannel sliding spotlight SAR echoes with large pulse bandwidth and azimuth steering angle are difficult to reconstruct and process due to the large range frequency-dependent Doppler centroid varying frequency support. To resolve this problem, two azimuth preprocessing approaches both based on the range frequency-dependent deramping strategy are proposed in this article. In both approaches, echoes are handled by a range frequency-dependent azimuth deramping function at first, which assign the signal measurements to unambiguous Doppler frequencies. Afterward, in the first preprocessing approach, azimuth multichannel raw data can be reconstructed and combined before azimuth upsampling in the conventional two-step preprocessing technique. The other way to handle the deramped multichannel raw data is the modified full aperture azimuth multichannel sliding spotlight reconstruction approach, which embeds azimuth multichannel reconstruction into azimuth convolution between the deramped raw data and the selected range frequency-dependent azimuth deramping function. Furthermore, the azimuth data in the second approach should be resampled to obtain a uniform azimuth sampling interval. Computational complexity and effects on the following imaging algorithm of both approaches are analyzed and compared. Simulation results validate the proposed two azimuth multichannel preprocessing approaches.

Time-Slot Reservation and Channel Switching Using Markovian Model for Multichannel TDMA MAC in VANETs

Efficient communication in the intelligent transport system requires the vehicles to transmit safety and non-safety messages timely without any loss. In multi-channel MAC, SCH channels have different properties in terms of the frequency range, frequency limit, power limit, distance covered, and channel parameters. Based on these channel properties, messages must be transmitted using the most appropriate channel. Existing multi-channel MAC approaches randomly choose the available SCH channel to transmit the messages. Random selection of the channel leads to overuse of the channel resources or lack of the resources due to inappropriate channel selection. The proposed approach provides a way to reserve the most appropriate SCH leading to optimized resource utilization. For collision-free allocation of resources to a large number of contending vehicles, we have proposed a variable size TDMA approach for SCH channel allocation. The paper also focuses on reducing the transmission collision and re-usability of the time slot for timely message transmission. Markovian modeling determines the probability of successfully reserving the time slot and switching to the required SCH. Simulation results prove that the probability of time slot reservation in the required SCH is approximately 0.6 for the proposed approach and approximately 0.2 for other approaches.

Successive multivariate variational mode decomposition based on instantaneous linear mixing model

In this paper, a novel Successive Multivariate Variational Mode Decomposition (SMVMD) is presented. Different from most existing multichannel signal decomposition approaches, the proposed SMVMD does not need to predefine the mode number and is able to extract the joint or common modes successively. Firstly, a general decomposition form for multichannel multicomponent signals is formulated based on the instantaneous linear mixing model, which is commonplace in the blind source separation (BSS) problem. Then, four key criteria are introduced to establish the successive variation optimization function. Finally, the alternate direction method of multipliers (ADMM) algorithm is employed to solve this optimization problem. The effectiveness and advantages of the proposed SMVMD are demonstrated by a series of numerical examples. The utility of the proposed approach is also highlighted by the analysis of real-life EEG and vibration signals.

Single Versus Multi-channel Dispersion Analysis of Ultrasonic Guided Waves Propagating in Long Bones.

Ultrasonic guided wave techniques have been applied to characterize cortical bone for osteoporosis assessment. Compared with the current gold-standard X-ray-based diagnostic methods, ultrasound-based techniques pose some advantages such as compactness, low cost, lack of ionizing radiation, and their ability to detect the mechanical properties of the cortex. Axial transmission technique with a source-receiver offset is employed to acquire the ultrasound data. The dispersion characteristics of the guided waves in bones are normally analyzed in the transformed domains using the dispersion curves. The transformed domain can be time-frequency map using a single channel or wavenumber-frequency (or phase velocity-frequency) map with multi-channels. In terms of acquisition effort, the first method is more cost- and time-effective than the latter. However, it remains unclear whether single-channel dispersion analysis can provide as much quantitative guided-wave information as the multi-channel analysis. The objective of this study is to compare the two methods using numerically simulated and ex vivo data of a simple bovine bone plate and explore their advantages and disadvantages. Both single- and multi-channel signal processing approaches are implemented using sparsity-constrained optimization algorithms to reinforce the focusing power. While the single-channel data acquisition and processing are much faster than those of the multi-channel, modal identification and analysis of the multi-channel data are straightforward and more convincing.

Multi-channel measurement-based identification methods for mode estimation in power systems

Mode identification from post-disturbance ringdown responses is a valuable tool for the analysis of the dynamic performance and the stability margins of power systems. In this aspect, several techniques have been proposed, focusing mainly to single-signal analysis. However, considering large-scale power systems and especially future scenarios with high penetration of distributed energy resources, detailed network analysis at all voltage levels is required. As a result of these concerns, multi-channel mode identification algorithms have been developed. Scope of this paper is to evaluate the applicability and the performance of the most known multi-channel measurement-based identification approaches for the modal analysis of modern power systems incorporating active distribution networks. The algorithmic details and distinct characteristics of each method are briefly discussed. The examined methods are used to identify the dominant inter-area modes contained in ringdown responses at different levels of a combined transmission-distribution network.

Deep Learning Assisted Time-Frequency Processing for Speech Enhancement on Drones

This article fills the gap between the growing interest in signal processing based on Deep Neural Networks (DNN) and the new application of enhancing speech captured by microphones on a drone. In this context, the quality of the target sound is degraded significantly by the strong ego-noise from the rotating motors and propellers. We present the first work that integrates single-channel and multi-channel DNN-based approaches for speech enhancement on drones. We employ a DNN to estimate the ideal ratio masks at individual time-frequency bins, which are subsequently used to design three potential speech enhancement systems, namely single-channel ego-noise reduction (DNN-S), multi-channel beamforming (DNN-BF), and multi-channel time-frequency spatial filtering (DNN-TF). The main novelty lies in the proposed DNN-TF algorithm, which infers the noise-dominance probabilities at individual time-frequency bins from the DNN-estimated soft masks, and then incorporates them into a time-frequency spatial filtering framework for ego-noise reduction. By jointly exploiting the direction of arrival of the target sound, the time-frequency sparsity of the acoustic signals (speech and ego-noise) and the time-frequency noise-dominance probability, DNN-TF can suppress the ego-noise effectively in scenarios with very low signal-to-noise ratios (e.g. SNR lower than −15 dB), especially when the direction of the target sound is close to that of a source of the ego-noise. Experiments with real and simulated data show the advantage of DNN-TF over competing methods, including DNN-S, DNN-BF and the state-of-the-art time-frequency spatial filtering.

Cyclic alternating pattern estimation based on a probabilistic model over an EEG signal

A probabilistic model for sleep analysis is proposed in this work, modeling the temporal relation between the sleep structure and the presence of the electroencephalogram (EEG) Cyclic Alternating Pattern (CAP) with a Hidden Markov Model (HMM). Sleep scoring is frequently performed by assigning a state to each thirty second epoch. However, this approach does not provide enough time resolution to efficiently detect the CAP since, by definition, the CAP cycles are assessed by applying the scoring rules to one second epochs of the EEG signal. Thus, a clustering analysis was employed, with a one second epoch, over the EEG signal to create clusters that were then encoded using symbolic dynamics to produce words. Two algorithms for clustering were analyzed, specifically the self-organizing map and the Gaussian Mixture Model (GMM). The words were then fed to the HMM to determine the presence of the CAP. Both single-channel and multi-channel (based on sensor fusion) approaches were tested. The best results were attained using the GMM with three Gaussians, achieving an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 72%, 66%, 75% and 0.71 for single-channel and 76%, 61%, 85% and 0.73 for multi-channel. This results are in the specialist agreement range with visual analysis. Therefore, the proposed model is capable of providing a new view over the CAP cycles by simplifying the complex EEG signal to a simple sequence of symbols. Such analysis can be significantly challenging to perform in more abstract models.

A multichannel magnetic stimulation system using submillimeter-sized coils: system development and experimental application to rodent brain in vivo

Objective. Single coil-based systems for magnetic stimulation are widely used for neurostimulation in neuroscience research and clinical treatment of neurological diseases. However, parallelization of magnetic stimulation with multiple coils may generate far greater potential than a single coil, and could thus expand the scope of brain area stimulation. Therefore, we examined whether a multiple coil-based system could improve the effectiveness and focality of conventional single coil-based magnetic stimulation. Approach. We designed and tested a micromagnetic stimulation (µMS) device with multiple submillimeter-sized coils as a possible substitute for one large coil. Our design concept is spatially-distributed stimulation strategy involving the small number of coils to be able to mimic desired electric field profiles. To this end, the cost function of the error between the desired and coil-induced electric fields was firstly calculated, and coil currents were repetitively estimated to achieve the smaller number of coils under a certain criterion: a minimum error with spatial sparsity. Using these approaches, we evaluated the capability of our multi-channel µMS via numerical simulations and demonstrated responsive results in animal experiments. Main results. Our approach can enhance control of neural excitation and improve the concentration of the excitation field induced by magnetic stimulation with reduced power consumption. Furthermore, in vivo electrophysiological recordings of mouse brain performed to evaluate our proposed approach for brain stimulation demonstrated experimentally that our multi-channel µMS device can yield more effective stimulation than the single-channel device. In addition, our device permitted electronic spatial adjustment of the stimulus shape and location without moving the coils. Significance. The development of new multichannel µMS-based therapeutic approaches may be useful because the µMS affects only a restricted brain area. Indeed, the small size of micro-coils and their finer focality with multichannel contribution might be suitable for chronic use, which is difficult using conventional large transcranial magnetic stimulation (TMS) with simple round or figure-eight coils. Thus, our findings support new opportunities to explore magnetic stimulation as a therapeutic approach for neurological disorders.

Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise

Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.