Robust Pitch Detection by Narrow Band Spectrum Analysis

This paper proposes a new technique for detecting pitch patterns which is useful for automatic speech recognition, by using a narrow band spectrum analysis. The motivation of this approach is that humans perceive some kind of pitch in whispers where no fundamental frequencies can be observed, while most of the pitch determination algorithm (PDA) fails to detect such perceptual pitch. The narrow band spectrum analysis enable us to find pitch structure distributed locally in frequency domain. Incorporating this technique into PDA's is realized to applying the technique to the lag window based PDA. Experimental results show that pitch detection performance could be improved by 4% for voiced sounds and 8% for voiceless sounds

Relativistic Cyclotron Radiation Detection of Tritium Decay Electrons as a New Technique for Measuring the Neutrino Mass

The shape of the beta decay energy distribution is sensitive to the mass of the electron neutrino. Attempts to measure the endpoint shape of tritium decay have so far seen no distortion from the zero-mass form, thus placing an upper limit of m_nu_beta < 2.3 eV. Here we show that a new type of electron energy spectroscopy could improve future measurements of this spectrum and therefore of the neutrino mass. We propose to detect the coherent cyclotron radiation emitted by an energetic electron in a magnetic field. For mildly relativistic electrons, like those in tritium decay, the relativistic shift of the cyclotron frequency allows us to extract the electron energy from the emitted radiation. We present calculations for the energy resolution, noise limits, high-rate measurement capability, and systematic errors expected in such an experiment.Comment: 4 pages, 2 figure

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

The most ancient spiral galaxy: a 2.6-Gyr-old disk with a tranquil velocity field

We report an integral-field spectroscopic (IFS) observation of a gravitationally lensed spiral galaxy A1689B11 at redshift $z=2.54$. It is the most ancient spiral galaxy discovered to date and the second kinematically confirmed spiral at $z\gtrsim2$. Thanks to gravitational lensing, this is also by far the deepest IFS observation with the highest spatial resolution ($\sim$ 400 pc) on a spiral galaxy at a cosmic time when the Hubble sequence is about to emerge. After correcting for a lensing magnification of 7.2 $\pm$ 0.8, this primitive spiral disk has an intrinsic star formation rate of 22 $\pm$ 2 $M_{\odot}$ yr$^{-1}$, a stellar mass of 10$^{9.8 \pm 0.3}$$M_{\odot}$ and a half-light radius of $r_{1/2}=2.6 \pm 0.7$ kpc, typical of a main-sequence star-forming (SF) galaxy at $z\sim2$. However, the H\alpha\ kinematics show a surprisingly tranquil velocity field with an ordered rotation ($V_{\rm c}$ = 200 $\pm$ 12 km/s) and uniformly small velocity dispersions ($V_{\rm \sigma, mean}$ = 23 $\pm$ 4 km/s and $V_{\rm \sigma, outer-disk}$ = 15 $\pm$ 2 km/s). The low gas velocity dispersion is similar to local spiral galaxies and is consistent with the classic density wave theory where spiral arms form in dynamically cold and thin disks. We speculate that A1689B11 belongs to a population of rare spiral galaxies at $z\gtrsim2$ that mark the formation epoch of thin disks. Future observations with JWST will greatly increase the sample of these rare galaxies and unveil the earliest onset of spiral arms.Comment: 18 pages, 13 figures, 1 table; accepted for publication in Ap

Estimation of Severity of Speech Disability through Speech Envelope

In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC