A study of acoustic characteristics of voluntary expiratory sounds produced before and immediately after swallowing

To evaluate the expiratory sounds produced during swallowing recorded simultaneously with videofluorographic examination of swallowing (VF) using fast Fourier transform (FFT), and to examine the relationship between dysphagia and its acoustic characteristics. A total of 348 samples of expiratory sounds were collected from 61 patients with dysphagia whose expiratory sounds were recorded during VF. The VF results were evaluated by one dentist and categorized into three groups: safe group (SG), penetration group (PG), and aspiration group (AG). The duration and maximum amplitude of expiratory sounds produced were measured as the domain characteristics on the time waveform of these sounds and compared among the groups. Time window-length appropriate for FFT and acoustic discriminate values (AD values) of SG, PG, and AG were also investigated. The groups were analyzed using analysis of variance and Scheffé's multiple comparison method. The maximum amplitude of SG was significantly smaller than those of PG and AG. The mean duration in SG (2.05 s) was significantly longer than those in PG (0.84 s) and AG (0.96 s). The AD value in SG was significantly lower than those in PG and AG. AD value detects penetration or aspiration, and can be useful in screening for dysphagia.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

A Novel Real-Time Echo Separation Processing Architecture for Space–Time Waveform-Encoding SAR Based on Elevation Digital Beamforming

Space–time waveform-encoding (STWE) SAR can receive echoes from multiple sub-swaths simultaneously with a single receive window. The echoes overlap each other in the time domain. To separate the echoes from different directions, traditional schemes adapt single-null steering techniques for digital receive beam patterns. However, the problems of spaceborne DBF-SAR, in practice, such as null extension loss, terrain undulation, elevation angle of arrival extension, and spaceborne antenna beam control, make the conventional scheme unable to effectively separate the echoes from different sub-swaths, which overlap each other in the time domain.A novel multi-null constrained echo separation scheme is proposed to overcome the shortcomings of the conventional scheme. The proposed algorithm can flexibly adjust the width of the notch to track the time-varying pulse extension angle with less resource consumption. Moreover, the hardware implementation details of the corresponding real-time processing architecture are discussed. The two-dimensional simulation results indicate that the proposed scheme can effectively improve the performance of echo separation. The effectiveness of the proposed method is verified by raw data processing instance of an X-band 16-channel DBF-SAR airborne system.

Flow Index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation

Background Flow Index, a numerical expression of the shape of the inspiratory flow-time waveform recorded during pressure support ventilation, is associated with patient inspiratory effort. The aim of this study was to assess the accuracy of Flow Index in detecting high or low inspiratory effort during pressure support ventilation and to establish cutoff values for the Flow index to identify these conditions. The secondary aim was to compare the performance of Flow index,of breathing pattern parameters and of airway occlusion pressure (P0.1) in detecting high or low inspiratory effort during pressure support ventilation. Methods Data from 24 subjects was included in the analysis, accounting for a total of 702 breaths. Breaths with high inspiratory effort were defined by a pressure developed by inspiratory muscles (Pmusc) greater than 10 cmH2O while breaths with low inspiratory effort were defined by a Pmusc lower than 5 cmH2O. The areas under the receiver operating characteristic curves of Flow Index and respiratory rate, tidal volume,respiratory rate over tidal volume and P0.1 were analyzed and compared to identify breaths with low or high inspiratory effort. Results Pmusc, P0.1, Pressure Time Product and Flow Index differed between breaths with high, low and intermediate inspiratory effort, while RR, RR/VT and VT/kg of IBW did not differ in a statistically significant way. A Flow index higher than 4.5 identified breaths with high inspiratory effort [AUC 0.89 (CI 95% 0.85–0.93)], a Flow Index lower than 2.6 identified breaths with low inspiratory effort [AUC 0.80 (CI 95% 0.76–0.83)]. Conclusions Flow Index is accurate in detecting high and low spontaneous inspiratory effort during pressure support ventilation.

Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

Damage accumulation comparison for various vibration test profile generation methods applied to a complex payload

There are potential sources of uncertainty when using default or generic profiles for vibration testing, particularly for large and complex payloads ( Warren and Joiner, 2019 ). Replacing these severities with custom profiles generated using measured data offers one alternative; however, care must be taken not to select an inappropriate or inaccurate method. This research details the methodology and results of a trial which involved the design and implementation of a series of vibration tests, each to simulate a mixed-terrain environment applied to a complex payload. A systematic approach was used to develop and execute a test plan via the design of experiments method. An estimate of the damage potential for each was formed using Miner’s rule (linear damage accumulation) applied to direct strain gauge data. This approach was compared with damage measured during a time waveform replication style test to be used as a baseline. The results of this test can be used to guide test specifiers and programme-level acceptance test guidelines and may be useful in implementing or guiding standards in this space. In addition, the damage measured in a series of increasing amplitude vibration tests (with an identical power spectral density) was compared with predicted values to assess the gain linearity assumptions often used in accelerated vibration testing. The research is unique as the authors could not source a thorough trial comparing the damage produced using a range of different profile development methods with a complex, real-world payload. Test houses and test specifiers seeking to improve the accuracy of their tests should consider the key findings and guidance in this article.