Koherentse fluktuatsiooni nefelomeetria rakendamine laboratoorses praktikas

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolev doktoritöö on pühendatud uue optilise meetodi – koherentse fluktuatsiooni nefelomeetria (i.k. coherent fluctuation nephelometry, CFN) – uurimisele ja praktilisele rakendamisele. See on uus lähenemine lahuste hägususe mõõtmiseks, kasutades selleks hajunud valguse mõõtmist. Sel lähenemisel on mitmeid eeliseid võrreldes tavapärase nefelomeetrilise meetodiga, mille tundlikkust piirab hajunud valguse foon, mis pärineb eelkõige küvetilt, aga ka kõikidelt süsteemi teistelt optilistelt osadelt. CFN põhineb hajunud valguse ajalise fluktuatsiooni mõõtmisel, nii et kasulik signaal pärineb ainult liikuvatelt osakestelt küveti vedelikus ning süsteemi mitteliikuvad osad ei mõjuta peaaegu üldse signaali. See võimaldab saavutada paremat tundlikkust, lihtsustada seadme konstruktsiooni ning kasutada ühekordseid madala optilise kvaliteediga küvette. Nefelomeetria on laialdaselt kasutusel teaduslikes ning laboratoorsetes rakendustes (nt meditsiinilaborites) hindamaks proovide hägusust ning monitoorimaks protsesse, mille käigus hägusus muutub. Suure tundlikkuse saavutamine nõuab seadme keerulisemaks muutmist ning kõrge optilise kvaliteediga küvettide kasutamist, mis peaksid olema kliinilistes laborites ühekordseks kasutamiseks mõeldud. CFN meetodi eelised võimaldavad konstrueerida efektiivsemaid seadmeid, millega olulisi probleeme meditsiinilabori praktikas lahendada. Doktoritöö peamine ülesanne oli uurida CFN meetodit, et teha kindlaks optilise tee optimaalsed parameetrid ning arendada välja CFN-analüsaatorite prototüübid. Teine oluline ülesanne oli rakendada CFN meetodit, et lahendada olulisi probleeme kliiniliste laborite praktikas ning valida välja meetodi põhiline rahendusala. CFN meetodit rakendati, et analüüsida erinevaid bioloogilisi proove ning osakeste suspensioone väikese ning suure hägususega. Põhitulemused saavutati immunoglutinatsiooni reaktsioonide ning mikroorganismide kasvukõverate salvestamise korral. CFN meetodi toimimise modelleerimine ning teoreetiline analüüs koos eksperimentaaltöö tulemustega võimaldasid arendada mitmekanalilise mikrobioloogilise analüsaatori eesmärgiga seda rakendada kliinilises mikrobioloogia laboris. Need seadmed hõlmavad endas nii CFN kui turbidimeetria meetodeid, et suurendada seadmete dünaamilist mõõteulatust. Analüsaatorites pole mitte mingeid mehaanilisi süsteeme küvettide positsioneerimiseks; CFN meetodi lihtsus võimaldab iga küveti jaoks kasutada eraldi valgusallikat ning fotodetektoreid, vähendades nii seadme keerulisust ning suurendades töökindlust. CFN prototüüpe kasutati edukalt kliinilistes laborites, et lahendada kaht olulist probleemi: uriiniproovide kiir-skriining (sõelumine) ning antibiootikumide mikroobtundlikkuse kiir-testimine. Kokku tehti rohkem kui 900 mõõtmist, mille võrdluskatsed teiste mikrobioloogiliste meetoditega näitasid head kokkulangevust. Arendatud CFN analüsaatorid näidati olevat efektiivsed kliinilises mikrobioloogia laboris.The thesis is devoted to investigation and application of the new optical method called coherent fluctuating nephelometry (CFN). It is the new approach to turbidity measurement by means of scattered light detection. It has several advantages in comparison with conventional nephelometry method, which has limited sensitivity due to parasitic stray light, scattered by all optical parts of the device, first of all by the optical cuvette. CFN is based on detecting of fluctuations of scattered light, so the signal is formed only by particles moving in the liquid in the cuvette, and light scattered by nonmoving parts of the device almost does not influence the signal. That allows to achieve higher sensitivity, to make the device simpler and to use disposable cuvettes of low optical quality. Nephelometry is widely used in scientific and laboratory applications (e.g. medical laboratory practice) for turbidity estimation and recording the processes causing turbidity changes. High sensitivity achievement requires complicating of the device and using cuvettes of high optical quality, which must be disposable in clinical laboratory practice. The advantages of CFN method allows to construct more efficient devices to solve important problems in medical laboratory practice. The main task was to investigate CFN method to determine optimal parameters of optical path to develop prototypes of CFN-analyzers. The other task was to apply CFN method for solving important problems in clinical laboratory practice and to choose main field of applicability. CFN was applied to analyze different biological samples and particles suspensions of low and high turbidity. The main results were achieved for immunoagglutination reactions and for recording of microorganisms growth curves. The result of modeling and theoretical analysis of CFN operation together with the results of experimental work allowed to develop multichannel microbiological analyzers for application in clinical microbiology laboratory. These devices combine CFN and turbidimetry methods to broad the dynamics range. Analyzers do not use any mechanical system of cuvettes positioning; the simplicity of CFN allows to use own light source and photodetectors for each cuvette, decreasing device complexity and increasing its reliability. CFN prototypes were successfully used in clinical microbiology laboratories to solve two important problems: fast urine screening and rapid antibiotic susceptibility testing. Altogether more than 900 test were done, the comparison with other microbiological methods showed good agreement. The developed CFN-analyzers were shown to be effective in clinical laboratory microbiology

Number Systems for Deep Neural Network Architectures: A Survey

Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlightedComment: 28 page

Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored

Estimation of Input Variable as Initial Condition of a Chaos Based Analogue to Digital Converter

A realization of an analogue-to-digital converter(ADC) with improved conversion accuracy,using the chaotic behaviour of the tent map,is presented. In this approach, the analogue input signal to be measured, termed as the initial condition is applied to a chaotic map, and the symbolic dynamics resulting from the map evolution, is used to determine the initial condition in digital form. The unimodal piecewise linear tent map (TM) has been used for this purpose, because of its property of generating uniform distribution of points and robust chaos. Through electronic implementation of the TMit is practically impossible to produce an ‘ideal’ TM behaviour with parameter values in the full range [0,1]. Due to component imprecision and various other factors, a non-ideal map with reduced height is observed. For such a map, converting the equivalent symbolic trajectory generated by TM iterations return erroneous results as the partitioning of the phase space embodied in the finite symbolic dynamics no longer has unique correspondence with the initial condition. Two algorithmic solutions have been proposed to minimise the errors associated with a practical system. For one, it has been established that for a reduced-height map the partitioning will not remain of equal size. Considering that the height of the tent map used for this purpose is known from an independent but related research, a technique of partitioning the state space unevenly, depending on the map height has been proposed and has been shown that if the correct partitioning is used, the resulting symbolic dynamics again map uniquely to the initial condition. Alternatively, it has been shown that the degree of deviation of the iterate values can be determined based on the parameter value, which in turn can be adjusted for depending on the symbolic sequence generated by the initial condition to determine the correct decimal equivalent values. The both the approaches proved to be highly effective in obtaining a digital outcome corresponding to the initial condition using 8 symbolic iterations of the map in hardware domain, with the second approach outperforming the first in terms of accuracy, while the first method can easily be pipelined alongside generating the iterates and thus improve the speed. This development is promising because, in contrast to the commercially available ADCs, it places lower demand on the hardware resource and can be effectively implemented to give a real-time operation