Breathing is essential to human survival, which carries information about a person’s physiological and psychological state. Mostly breath sound boundaries are marked manually before being used for any task such as classification, spectral analysis, etc., which is very tedious. Various techniques have been proposed to segment breath sounds recorded at the chest, and trachea but vocal breath sounds (VBS) are under-explored. An unsupervised algorithm for VBS segmentation has been proposed in this work. Each breath phase in continuous breaths has been modeled using triangles, where the end points of triangles representing breath boundaries are estimated using dynamic programming. Data from 60 subjects (31 healthy, 29 asthmatic patients) having 307 breaths have been used. The proposed method’s performance was found to be comparable with the manually marked boundaries. Comparable asthmatic versus healthy subject mean(standard deviation) classification accuracy using manually marked and predicted boundaries are 75%(±11%) and 72%(±15%),respectively are found.

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s disease (PD) impacts both articulation and prosody in an individual’s speech. Complex deep neural networks exploit these cues for detection of ALS and PD. These are typically done using recordings in laboratory condition. This study aims to examine the robustness of these cues against background noise and model complexity, which has not been investigated before. We perform classification experiments with pitch and Mel-frequency cepstral coefficients (MFCC) using models of three different complexities and additive white Gaussian noise in four signal-to-noise-ratio (SNR) conditions. The findings are as follows: 1) In clean condition, pitch performs similar to MFCC across most model complexities considered, suggesting that one-dimensional pitch pattern provides discriminative cues for the classification to an extent equal to that of multi-dimensional MFCC, 2) Similar trend is observed in noisy cases when classifiers are trained and tested in matched noise and SNR conditions, 3) When the classifiers trained on clean data are applied in noisy cases, pitch based average classification accuracies are found to be 20.09% and 24.73% higher than those using MFCC for ALS vs. healthy and PD vs. healthy, respectively, suggesting robustness of pitch based classifier against noise and model complexity.

In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD), and Healthy Controls (HC) using a CNNLSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochokinetic rate (DIDK) and Sustained phoneme production (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD, and 60 HC subjects. Classifications using SVM and DNN are considered as baseline schemes. Classification accuracy of ALS and HC (indicated by ALS/HC) using CNN-LSTM has shown an improvement of 10.40%, 4.22% and 0.08% for PHON, SPON and DIDK tasks, respectively over the best of the baseline schemes. Furthermore, the CNN-LSTM network achieves the highest PD/HC classification accuracy of 88.5% for the SPON task and the highest 3-class (ALS/PD/HC) classification accuracy of 85.24% for the DIDK task. Experiments using transfer learning at low resource training data show that data from ALS benefits PD/HC classification and vice-versa. Experiments with fine-tuning weights of 3-class (ALS/PD/HC) classifier for 2-class classification (PD/HC or ALS/HC) gives an absolute improvement of 2% classification accuracy in SPON task when compared with randomly initialized 2-class classifier.

Non-speech sounds (cough, wheeze) are typically known to perform better than speech sounds for asthmatic and healthy subject classification. In this work, we use sustained phonations of speech sounds, namely, /α:/, /i:/, /u:/, /eI/, /ou/, /s/, and /z/ from 47 asthmatic and 48 healthy controls. We consider INTERSPEECH 2013 Computational Paralinguistics Challenge baseline (ISCB) acoustic features for the classification task as they provide a rich set of characteristics of the speech sounds. Mel-frequency cepstral coefficients (MFCC) are used as the baseline features. The classification accuracy using ISCB improves over MFCC for all voiced speech sounds with the highest classification accuracy of 75.4% (18.28% better than baseline) for /ou/. The exhale achieves the highest classification accuracy of 77.8% (4.2% better than baseline). Comparable accuracies using speech sound /ou/ and non-speech exhale indicate the benefit of the rich acoustic features from ISCB. An analysis of 21 ISCB features groups using forward feature group selection shows that loudness and MFCC groups contribute the most in the case of /ou/, with interquartile range between 2ndand 3rdquartile of loudness feature being the best discriminator feature.

Moore's Law has driven the semiconductor revolution enabling over four decades of scaling in frequency, size, complexity, and power. However, the limits of physics are preventing further scaling of speed, forcing a paradigm shift towards multicore computing and parallelization. In effect, the system is taking over the role that the single CPU was playing: high-speed signals running through chips but also packages and boards connect ever more complex systems. High-speed signals making their way through the entire system cause new challenges in the design of computing hardware. Inductance, phase shifts and velocity of light effects, material resonances, and wave behavior become not only prevalent but need to be calculated accurately and rapidly to enable short design cycle times. In essence, to continue scaling with Moore's Law requires the incorporation of Maxwell's equations in the design process. Incorporating Maxwell's equations into the design flow is only possible through the combined power that new algorithms, parallelization and high-speed computing provide. At the same time, incorporation of Maxwell-based models into circuit and system-level simulation presents a massive accuracy, passivity, and scalability challenge. In this tutorial, we navigate through the often confusing terminology and concepts behind field solvers, show how advances in field solvers enable integration into EDA flows, present novel methods for model generation and passivity assurance in large systems, and demonstrate the power of cloud computing in enabling the next generation of scalable Maxwell solvers and the next generation of Moore's Law scaling of systems. We intend to show the truly symbiotic growing relationship between Maxwell and Moore!

The partial element equivalent circuit (PEEC) approach has been used in different forms for the computation of equivalent circuit elements for quasi-static and full-wave electromagnetic models. In this paper, we focus on the topic of large scale inductance computations. For many problems as part of PEEC modeling, partial inductances need to be computed to model interactions between a large numbers of objects. These computations can be very time and memory consuming. To date, several techniques have been devised to reduce the memory and time required to compute the partial inductance entities, as well as the time required to use them in a circuit analysis compute step. Some of the existing methods use hierarchical compression while some others are based on issues like properties of the inverse of the partial inductance matrix. However, because of inherent limitations, most of these methods are less suitable for PEEC applications. In this paper, we present an approach which is based on the compression of the partial inductance matrix utilizing the QR decomposition of the far coefficients submatrices. The QR-decomposed form is represented as a compressed SPICE-compatible circuit. This yields an efficient and mathematically consistent approach for reducing the storage and time requirements

Simulation of distributed electromagnetic effects of electrically large structures is no longer a luxury but a necessity in the accurate prediction of modern day circuit performance. In this regard, integral equation based methods have steadily gained in popularity but suffer from the time and memory bottlenecks arising from the resultant dense matrix. Fast linear complexity solvers have been introduced in the past but with the growing complexity of circuit layouts parallel implementations are the only viable options in addressing practical circuit layouts. In this paper, we present a parallel implementation of the low-rank compression based fast solver with linear cost reduction capacity with respect to the number of processors. The main problems in parallelizing a hierarchical algorithm are discussed and the advantages of the implemented scheme are highlighted. The new solver enables the simulation of full-chip problems consisting of millions of unknowns with acceptable accuracy and modest time and memory requirements.

Boundary element methods are being successfully used for modeling parasitic effects in cutting-edge circuit design. The dense system matrix generated therein presents a time and memory bottleneck. Fast iterative solver techniques, developed to address the problem, suffer from convergence issues which become pronounced for large number of right hand sides as is the case for massively coupled systems. In this paper an iteration free solution scheme is presented. The dense matrix is rendered sparse by applying multilevel multipole expansions, and the resultant sparse matrix is solved by a traditional sparse matrix solver. The accuracy and time and memory requirements for the solver are compared against the regular methods. The advantage of the presented method over the corresponding iterative scheme is also demonstrated.

Parasitic parameter extraction is a crucial issue in Integrated Circuit design. Integral equation based solvers, which guarantee high accuracy, suffer from a time and memory bottleneck arising from the dense matrices generated. .

Fast parasitic extraction is an integral part of high-speed microelectronic simulation at the package and on-chip level. Integral equation methods and related fast solvers for the iterative solution of the resulting dense matrix systems have enabled linear time complexity and memory usage. However, these methods tend to have large disparities between setup and matrix-vector product times that affect their efficiency when applied to multiple excitation problems, i.e., problems with a large number of nets. For example, FastCap, which is based on the fast multipole method, has a significantly faster setup time than the multilevel QR decomposition-based IES/sup 3/, but relatively slow matrix-vector products. In this paper, we present a novel oct-tree-based QR compression technique for fast iterative solution. The regular cube structure of the fast multipole method and the QR compression scheme for interaction submatrices as in IES/sup 3/ are combined to achieve a predetermined compressible matrix-block structure and, consequently, superior memory, setup, and solve time efficiencies.