Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients

Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes have been developed, including very few elderly diabetic patients. Several of these projects specifically concerned elderly subjects (n = 4). Mainly, these projects and studies show that telemonitoring diabetes results in improved blood glucose control—a significant reduction in HbA1c, improved patient ownership of the disease, greater patient adherence to therapeutic and hygiene-dietary measures, positive impact on comorbidities (hypertension, weight, dyslipidemia), improved quality of life for patients, and at least good patient receptivity and accountability. To date, the magnitude of its effects remains debatable, especially with the variation in patients’ characteristics (e.g., background, ability for self-management, medical condition), sample selection, and approach for treatment of control groups. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development

Mobile Probe for Cellular Network Coverage and Quality Measurement

This article describes a proposal of the probe application used for 2G–4G mobile network coverage and speech quality measurement. It is based on Android platform, most commonly used operation system for mobile phones. Measured results are visualized in the form of a map using GPS location. There are few tests available focusing on the applications that are most often used: test of the network coverage, speed of the websites loading, data rate test and voice quality test. The results are analyzed directly in the application and are also available over the web interface form. Individual results can be exported to standard output .csv, .json and .xml formats for further analysis

High-Power Hall Propulsion Development at NASA Glenn Research Center

The NASA Office of the Chief Technologist Game Changing Division is sponsoring the development and testing of enabling technologies to achieve efficient and reliable human space exploration. High-power solar electric propulsion has been proposed by NASA's Human Exploration Framework Team as an option to achieve these ambitious missions to near Earth objects. NASA Glenn Research Center (NASA Glenn) is leading the development of mission concepts for a solar electric propulsion Technical Demonstration Mission. The mission concepts are highlighted in this paper but are detailed in a companion paper. There are also multiple projects that are developing technologies to support a demonstration mission and are also extensible to NASA's goals of human space exploration. Specifically, the In-Space Propulsion technology development project at NASA Glenn has a number of tasks related to high-power Hall thrusters including performance evaluation of existing Hall thrusters; performing detailed internal discharge chamber, near-field, and far-field plasma measurements; performing detailed physics-based modeling with the NASA Jet Propulsion Laboratory's Hall2De code; performing thermal and structural modeling; and developing high-power efficient discharge modules for power processing. This paper summarizes the various technology development tasks and progress made to dat

Resonance lamp absorption technique for simultaneous determination of the OH concentration and temperature at 10 spatial positions in combustion environments

A rugged, easy to implement, line-of-sight absorption instrument which utilizes a low pressure water vapor microwave discharge cell as the light source, has been developed to make simultaneous measurements of the OH concentration and temperature at 10 spatial positions. The design, theory, and capability of the instrument are discussed. Results of the measurements obtained on a methane/air flat flame burner are compared with those obtained using a single-frequency, tunable dye laser system

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development