Development and Evaluation of a Real-Time Framework for a Portable Assistive Hearing Device

Testing and verification of digital hearing aid devices, and the embedded software and algorithms can prove to be a challenging task especially taking into account time-to-market considerations. This thesis describes a PC based, real-time, highly configurable framework for the evaluation of audio algorithms. Implementation of audio processing algorithms on such a platform can provide hearing aid designers and manufacturers the ability to test new and existing processing techniques and collect data about their performance in real-life situations, and without the need to develop a prototype device. The platform is based on the Eurotech Catalyst development kit and the Fedora Linux OS, and it utilizes the JACK audio engine to facilitate reliable real-time performance Additionally, we demonstrate the capabilities of this platform by implementing an audio processing chain targeted at improving speech intelligibility for people suffering from auditory neuropathy. Evaluation is performed for both noisy and noise-free environments. Subjective evaluation of the results, using normal hearing listeners and an auditory neuropathy simulator, demonstrates improvement in some conditions

Advanced hardware and software approach to seismic site response investigations

Vibration measurement is an essential aspect of modern geotechnical engineering. It is particularly vital task for measuring the dynamic soil parameters, estimating seismic hazards and evaluating influence of industrial, traffic and construction vibrations on the surrounding buildings, structures and their elements. Meanwhile, commercial exploration seismic stations and data acquisition systems require significant professional knowledge and training in geophysics or vibration measurement, as well as practical skills and experience in adjusting data acquisition parameters. Furthermore, available seismological investigation and vibrometry sensors are not universally suitable for field applications in geophysical studies, soil-structure interaction investigations or structural vibrations. The frequency range suitable for seismic studies and industrial vibration measurement vary from 1 Hz to 300 Hz with sensitivity corresponding to the expected vibration level. To address these challenges, the first part of this thesis was focused on developing an innovative data acquisition system and sensors that are easy to use in a wide range of field applications. Geophysical techniques, including the Multichannel Analysis of Surface Waves (MASW) and Horizontal to Vertical Spectral Ratio (HVSR) methods, are gaining popularity in site investigations and seismic hazard characterization applications. The second part of this thesis involved conducting field studies using MASW and HVSR methods to evaluate the influence of challenging site conditions such as sloping surface topography, complicated soil stratigraphy and sloping bedrock boundaries on the results of the applied methods. The application of theoretical or numerical models of site amplification often poses a challenge under real field conditions. In the third part of the thesis, an analytical model was developed to allow for the removal of site effects from strong motion records and proposed a method for HVSR curve parameterization that resulted in an analytical expression for the amplification factor based on HVSR results

Deciphering clinical text: concept recognition in primary care text notes

Electronic patient records, containing data about the health and care of a patient, are a valuable source of information for longitudinal clinical studies. The General Practice Research Database (GPRD) has collected patient records from UK primary care practices since the late 1980s. These records contain both structured data (in the form of codes and numeric values) and free text notes. While the structured data have been used extensively in clinical studies, there are significant practical obstacles in extracting information from the free text notes. The main obstacles are data access restrictions, due to the presence of sensitive information, and the specific language of medical practitioners, which renders standard language processing tools ineffective. The aim of this research is to investigate approaches for computer analysis of free text notes. The research involved designing a primary care text corpus (the Harvey Corpus) annotated with syntactic chunks and clinically-relevant semantic entities, developing a statistical chunking model, and devising a novel method for applying machine learning for entity recognition based on chunk annotation. The tools produced would facilitate reliable information extraction from primary care patient records, needed for the development of clinically-related research. The three medical concept types targeted in this thesis could contribute to epidemiological studies by enhancing the detection of co-morbidities, and better analysing the descriptions of patient experiences and treatments. The main contributions of the research reported in this thesis are: guidelines for chunk and concept annotation of clinical text, an approach to maximising agreement between human annotators, the Harvey Corpus, a method for using a standard part-of-speech tagging model in clinical text chunking, and a novel approach to recognising clinically relevant medical concepts

Mathematical Analysis in Characterization of Carbon Nanotubes (CNTs) as possible Mosquito Repellents

Mosquitoes are a great threat to human health to date and are a subject of interdisciplinary research involving scientists from many areas. Recently much attention has been put to novel approaches to mosquito repellent products that involve the use of novel materials, such as carbon nanomaterials, where it is essential to determine their properties. This research discusses the full molecular characterization of carbon nanotubes (CNTs) produced by electrolysis in molten salts. Each CNT has its mathematical representation due to its hexagonal lattice structure. Multi-wall carbon nanotubes (MWCNTs) are considered. The focus is on determining their structural parameters: innermost and outermost diameters, chiral indices m and n, number of walls, and unit cell parameters. Corresponding frequency parts of Raman spectra of four experimentally produced CNTs are elaborated, and Python programming and Mathematica are employed for the most accurate (m,n) assignment. Determining the chirality of these samples enables the calculation of other structural properties, which are performed now, including their graph representation. The latter enables the evaluation of different distance-based topological indices (Wiener, Balaban, Sum-Balaban, Harary index, etc.) to predict some index-related properties of the molecules

Optimal Selection of Parameters for Production of Multiwall Carbon Nanotubes (MWCNTs) by Electrolysis in Molten Salts using Machine Learning

The production and use of carbon nanotubes (CNTs) have become extremely wide within the last decade. Hence, the high interest in producing non-expensive and quality CNTs has motivated many research projects. This research considers the design and development of new technology for producing MWCNTs by electrolysis in molten salts using non-stationary and stationary current regimes. The electrolysis is simple, ecological, economical, and flexible, and it offers possibilities for accurate control of various parameters, such as applied voltage, current density, or temperature. We infer the underlying relationship between the parameters and the quality of the experimentally produced MWCNTs by using explainable tree-based Machine Learning (ML) models. We train several models in a supervised manner, whereas in model covariates, we use the parameters of the MCWNTs, and as a target variable, the quality of the produced MWCNT. Domain experts label all the experimental examples in our data set. Controlling these parameters enables high-yield production and, particularly important, obtaining MWCNTs, which are up to ten times cheaper than other existing technologies

Vehicle Routing Problem with Distance Constraints aAnd Clustering Using MATLAB

The problem of designing routes for vehicles that should supply different customers with defined locations and specific demand from a single or various depots is known as the vehicle routing problem. The main objective in this case is minimizing the total cost of delivery or maximizing the profit while taking into consideration some constraints that vary from a case to another. In this paper I am going to define this problem, present a mathematical model to describe it, talk about the existing solutions to solve it, and use different tools to solve a real VRP of a company in tangier

Matrix method for large scale systems analysis

The mathematical model of a pilot operated pressure relief valve is described by a large system of nonlinear differential and algebraic equations. To analyze the stability condition of the valve, linearization of the mathematical model and getting the transfer function, i.e., the characteristic equation of the system is necessary. Obtaining the transfer function with elimination of the intermediate parameters is very complex and sometimes impossible. In this paper, a state space matrix method is used to reduce the large system of equation and to get the transfer function of the system

Data Processing Using Analytical Hierarchical Process in Real Circumstances

In the development of the industry of small and medium enterprises that are involved in one of the most specific activities such as working with products, materials it gives and seeks opportunities for optimizing the basic management activities in the work of a company. Today, many data are constantly generated that require proper processing and access to them, especially when it comes to supply chain management and business logistics. Standard processing techniques may not meet these requirements. The development of information technology and decision theory has contributed to the emergence of the application of modern-applicable methods and approaches to data processing and analysis. The way of processing data from traditional data processing tools are adapted for data processing and with the help of tools and mathematical methods that enable processing and analysis of big data that depend on several criteria such as time, resources, quality, and distance when it is about delivering work material. The identification and analysis of the problem to be solved, the determination of the possible solutions to the problem, the criteria according to which the possible solutions are evaluated, i.e., the alternatives and the choice of the best possible solution is a decision-making process. It is the choice of the best, from the most possible alternative solutions to the problem such as the problem of delivery of specific material such as leather considering the quality and price

Improvement of the Static Characteristics of Pilot Operated Pressure Relief Valves

Theoretical and experimental investigations of the static characteristics of pilot operated pressure relief valves is presented in this article. A mathematical model of pressure drop vs. flow depending of pilot operated pressure relief valves is developed. An experimental test stand was created for experimental investigation of the static characteristics and compared with each other which confirm the mathematical model. The results of solving the mathematical model and experimental investigation are presented in few diagrams. A few directions for improvement of the static characteristics are given, especially at the moment of opening of the main valve. Advantages and disadvantages of the static characteristics are discussed

Direct Economic Impact Assessment of Winter Honeybee Colony Losses in Three European Countries

Honeybees are of great importance because of their role in pollination as well as for hive products. The population of managed colonies fluctuates over time, and recent monitoring reports show different levels of colony losses in many regions and countries. The cause of this kind of loss is a combination of various factors, such as the parasitic mite Varroa destructor, viruses, pesticides, management practices, climate change, and other stress factors. Having in mind that the economic aspect of honeybee colony losses has not been estimated, a pioneer effort was made for developing a methodology that estimates the economic impact of honeybee colony losses. Winter loss data was based on 2993 answers of the COLOSS standard questionnaire survey of honeybee winter colony losses for 2016/2017. In addition, market and financial data were used for each country. In a comparative analysis, an assessment on the economic impact of colony losses in Austria, Czechia, and Macedonia was made. The estimation considered the value of the colonies and the potential production losses of the lost colonies and of surviving but weak colonies. The direct economic impact of winter honeybee colony losses in 2016/2017 in Austria was estimated to be about 32 Mio; in Czechia, 21 Mio; and in Macedonia, 3 Mio. Economic impact reflects the different value levels in the three countries, national colony populations, and the magnitude of colony losses. This study also suggests that economic losses are much higher than the subsidies, which underlines the economic importance of honeybees for the agricultural sector