[ES] Los altos costes energéticos, las constantes medidas regulatorias aplicadas por las administraciones para mantener bajos los costes sanitarios, así como los cambios en la normativa sanitaria que se han introducido en los últimos años, han tenido un impacto significativo en la industria farmacéutica y sanitaria. El paradigma Industria 4.0 engloba cambios en el modelo productivo tradicional de la industria farmacéutica con la inclusión de tecnologías que van más allá de la automatización tradicional. El objetivo principal es lograr medicamentos más rentables mediante la incorporación óptima de tecnologías como la analítica avanzada. El proceso de fabricación de las industrias farmacéuticas tiene diferentes etapas (mezclado, secado, compactado, recubrimiento, envasado, etc.) donde una de las etapas más costosas energéticamente es el proceso de secado. El objetivo durante este proceso es extraer el contenido de líquidos como el agua mediante la inyección de aire caliente y seco en el sistema. Este tiempo de secado normalmente está predeterminado y depende del volumen y el tipo de unidades de producto farmacéutico que se deben deshidratar. Por otro lado, la fase de precalentamiento puede variar dependiendo de varios parámetros como la experiencia del operador. Por lo tanto, es posible asumir que una optimización de este proceso a través de analítica avanzada es posible y puede tener un efecto significativo en la reducción de costes en todo el proceso de fabricación. Debido al alto coste de la maquinaria involucrada en el proceso de producción de medicamentos, es una práctica común en la industria farmacéutica tratar de maximizar la vida útil de estas máquinas que no están equipados con los últimos sensores. Así pues, es posible implementar un modelo de aprendizaje automático que utilice plataformas de analítica avanzada, como la computación en la nube, para analizar los posibles ahorros en el consumo de energía. Esta tesis está enfocada en mejorar el consumo de energía en el proceso de precalentamiento de un secador de lecho fluido, mediante la definición e implementación de una plataforma de computación en la nube IIOT (Industrial Internet of Things)-Cloud, para alojar y ejecutar un algoritmo de aprendizaje automático basado en el modelo Catboost, para predecir cuándo es el momento óptimo para detener el proceso y reducir su duración y, en consecuencia, su consumo energético. Los resultados experimentales muestran que es posible reducir el proceso de precalentamiento en un 45% de su duración en tiempo y, en consecuencia, reducir el consumo de energía hasta 2.8 MWh por año. / [CAT] Els elevats costos energètics, les constants mesures reguladores aplicades per les administracions per mantenir uns costos assistencials baixos, així com els canvis en la normativa sanitària que s'han introduït en els darrers anys, han tingut un impacte important en el sector farmacèutic i sanitari. El paradigma de la indústria 4.0 engloba els canvis en el model de producció tradicional de la indústria farmacèutica amb la inclusió de tecnologies que van més enllà de l'automatització tradicional. L'objectiu principal és aconseguir fàrmacs més rendibles mitjançant la incorporació òptima de tecnologies com l'analítica avançada. El procés de fabricació de les indústries farmacèutiques té diferents etapes (mescla, assecat, compactació, recobriment, envasat, etc.) on una de les etapes més costoses energèticament és el procés d'assecat. L'objectiu d'aquest procés és extreure el contingut de líquids com l'aigua injectant aire calent i sec al sistema. Aquest temps de procediment d'assecat normalment està predeterminat i depèn del volum i del tipus d'unitats de producte farmacèutic que cal deshidratar. D'altra banda, la fase de preescalfament pot variar en funció de diversos paràmetres com l'experiència de l'operador. Per tant, podem assumir que una optimització d'aquest procés mitjançant analítiques avançades és possible i pot tenir un efecte significatiu de reducció de costos en tot el procés de fabricació. A causa de l'elevat cost de la maquinària implicada en el procés de producció de fàrmacs, és una pràctica habitual a la indústria farmacèutica intentar maximitzar la vida útil d'aquestes màquines que no estan equipats amb els darrers sensors. Així, es pot implementar un model d'aprenentatge automàtic que utilitza plataformes de analítiques avançades com la computació en núvol, per analitzar l'estalvi potencial del consum d'energia. Aquesta tesis està enfocada a millorar el consum d'energia en el procés de preescalfament d'un assecador de llit fluid, mitjançant la definició i implementació d'una plataforma IIOT (Industrial Internet of Things)-Cloud computing, per allotjar i executar un algorisme d'aprenentatge automàtic basat en el modelatge Catboost, per predir quan és el moment òptim per aturar el procés i reduir-ne la durada, i en conseqüència el seu consum energètic. Els resultats de l'experiment mostren que és possible reduir el procés de preescalfament en un 45% de la seva durada en temps i, en conseqüència, reduir el consum d'energia fins a 2.8 MWh anuals. / [EN] High energy costs, the constant regulatory measures applied by administrations to maintain low healthcare costs, and the changes in healthcare regulations introduced in recent years have all significantly impacted the pharmaceutical and healthcare industry. The industry 4.0 paradigm encompasses changes in the traditional production model of the pharmaceutical industry with the inclusion of technologies beyond traditional automation. The primary goal is to achieve more cost-efficient drugs through the optimal incorporation of technologies such as advanced analytics. The manufacturing process of the pharmaceutical industry has different stages (mixing, drying, compacting, coating, packaging, etc..), and one of the most energy-expensive stages is the drying process. This process aims to extract the liquid content, such as water, by injecting warm and dry air into the system. This drying procedure time usually is predetermined and depends on the volume and the kind of units of a pharmaceutical product that must be dehydrated. On the other hand, the preheating phase can vary depending on various parameters, such as the operator's experience. It is, therefore, safe to assume that optimization of this process through advanced analytics is possible and can have a significant cost-reducing effect on the whole manufacturing process. Due to the high cost of the machinery involved in the drug production process, it is common practice in the pharmaceutical industry to try to maximize the useful life of these machines, which are not equipped with the latest sensors. Thus, a machine learning model using advanced analytics platforms, such as cloud computing, can be implemented to analyze potential energy consumption savings. This thesis is focused on improving the energy consumption in the preheating process of a fluid bed dryer by defining and implementing an IIOT (Industrial Internet of Things) Cloud computing platform. This architecture will host and run a machine learning algorithm based on Catboost modeling to predict when the optimum time is reached to stop the process, reduce its duration, and consequently its energy consumption. Experimental results show that it is possible to reduce the preheating process by 45% of its time duration, consequently reducing energy consumption by up to 2.8 MWh per year. / Barriga Rodríguez, R. (2023). Optimization of Fluid Bed Dryer Energy Consumption for Pharmaceutical Drug Processes through Machine Learning and Cloud Computing Technologies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/195847

In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes (MDPs) with finite state-space under the average cost criterion. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and averaged for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, a memory efficient implementation using a feature-vector representation of the state-space and TD (0) learning along the faster timescale is discussed. A three-timescale simulation based algorithm for solution of infinite horizon discounted-cost MDPs via the Value Iteration approach is also proposed. An approximation of the Dynamic Programming operator T is applied to the value function iterates. A sketch of convergence explaining the dynamics of the algorithm using associated ODEs is presented. Numerical experiments on rate based flow control on a bottleneck node using a continuous-time queueing model are presented using the proposed algorithms. Next, in Chapter 3, we develop three simulation-based algorithms for finite-horizon MDPs (FHMDPs). The first algorithm is developed for finite state and compact action spaces while the other two are for finite state and finite action spaces. Convergence analysis is briefly sketched. We then concentrate on methods to mitigate the curse of dimensionality that affects FH-MDPs severely, as there is one probability transition matrix per stage. Two parametrized actor-critic algorithms for FHMDPs with compact action sets are proposed, the ‘critic’ in both algorithms learning the policy gradient. We show w.p1convergence to a set with the necessary condition for constrained optima. Further, a third algorithm for stochastic control of stopping time processes is presented. Numerical experiments with the proposed finite-horizon algorithms are shown for a problem of flow control in communication networks. Towards stochastic optimization, in Chapter 4, we propose five algorithms which are variants of SPSA. The original one measurement SPSA uses an estimate of the gradient of objective function L containing an additional bias term not seen in two-measurement SPSA. We propose a one-measurement algorithm that eliminates this bias, and has asymptotic convergence properties making for easier comparison with the two-measurement SPSA. The algorithm, under certain conditions, outperforms both forms of SPSA with the only overhead being the storage of a single measurement. We also propose a similar algorithm that uses perturbations obtained from normalized Hadamard matrices. The convergence w.p.1 of both algorithms is established. We extend measurement reuse to design three second-order SPSA algorithms, sketch the convergence analysis and present simulation results on an illustrative minimization problem. We then propose several stochastic approximation implementations for related algorithms in flow-control of communication networks, beginning with a discrete-time implementation of Kelly’s primal flow-control algorithm. Convergence with probability1 is shown, even in the presence of communication delays and stochastic effects seen in link congestion indications. Two relevant enhancements are then pursued :a) an implementation of the primal algorithm using second-order information, and b) an implementation where edge-routers rectify misbehaving flows. Also, discrete-time implementations of Kelly’s dual algorithm and primal-dual algorithm are proposed. Simulation results a) verifying the proposed algorithms and, b) comparing stability properties with an algorithm in the literature are presented.

In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche.

Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results.

The purpose of this thesis is the proposal of the issue of education software for learning algorithms. Work approach to the problem, both from a theoretical point of view, of education and learning to new things, so and more important from a practical point of view for introduction to the process algorithmization. It also discusses the problem of learning algorithms in Czech schools and describes the resulting program, which was created along with this thesis. Contribution of this thesis is the summary of the requirements for the software and implementing software of this type, where is possible to demonstrate the problems and requirements of the draft programs of this kind. This thesis also describes the reactions of teachers to created application.

[pt] Com o rápido crescimento da Internet das Coisas (IoT), surgiram novas tecnologias de comunicação sem fio para atender aos requisitos de longo alcance, baixo custo e baixo consumo de energia exigidos pelos aplicativos de IoT. Nesse contexto, surgiram as redes de longa distância de baixa potência (LPWANs), as quais oferecem diferentes soluções que atendem aos requisitos dos aplicativos de IoT mencionados anteriormente. Entre as soluções LPWAN existentes, o LoRaWAN tem-se destacado por receber atenção significativa da indústria e da academia nos últimos anos. Embora o LoRaWAN ofereça uma combinação atraente de transmissões de dados de longo alcance e baixo consumo de energia, ele ainda enfrenta vários desafios em termos de confiabilidade e escalabilidade. No entanto, devido a sua natureza de código aberto e à flexibilidade do esquema de modulação no qual ele se baseia (Long Range (LoRa) permite o ajuste de fatores de espalhamento e a potência de transmissão), o LoRaWAN também oferece importantes possibilidades de melhorias. Esta dissertação aproveita a adequação dos algoritmos de Aprendizagem por Reforço (RL) para resolver tarefas de tomada de decisão e os utiliza para ajustar dinamicamente os parâmetros de transmissão dos dispositivos finais LoRaWAN. O sistema proposto, chamado RL-LoRa, mostra melhorias significativas em termos de confiabilidade e escalabilidade quando comparado ao LoRaWAN. Especificamente, diminui a taxa de erro de pacote (PER) média do LoRaWAN em 15 porcento, o que pode aumentar ainda mais a escalabilidade da rede. / [en] With the massive growth of the Internet of Things (IoT), novel wireless communication technologies have emerged to address the long-range, lowcost, and low-power consumption requirements of the IoT applications. In this context, the Low Power Wide Area Networks (LPWANs) have appeared, offering different solutions that meet the IoT applications requirements mentioned before. Among the existing LPWAN solutions, LoRaWAN has stood out for receiving significant attention from both industry and academia in recent years. Although LoRaWAN offers a compelling combination of long-range and low-power consumption data transmissions, it still faces several challenges in terms of reliability and scalability. However, due to its open-source nature and the flexibility of the modulation scheme it is based on (Long Range (LoRa) modulation allows the adjustment of spreading factors and transmit power), LoRaWAN also offers important possibilities for improvements. This thesis takes advantage of the appropriateness of the Reinforcement Learning (RL) algorithms for solving decision-making tasks, and use them to dynamically adjust the transmission parameters of LoRaWAN end devices. The proposed system, called RL-LoRa, shows significant improvements in terms of reliability and scalability when compared with LoRaWAN. Specifically, it decreases the average Packet Error Ratio (PER) of LoRaWAN by 15 percent, which can further increase the network scalability.

The researcher developed an autonomous driving simulation by training an end-to-end policy model using deep reinforcement learning algorithms in the Gym-duckietown virtual environment. The control strategy of the model was designed for the lane-following task. Several reinforcement learning algorithms were implemented and the SAC algorithm was chosen to train a non-end-to-end model with the information provided by the environment such as speed as input values, as well as an end-to-end model with images captured by the agent's front camera as input. In this paper, the researcher compared the advantages and disadvantages of the two models using kinetic parameters in the environment and conducted a series of experiments on the control strategy of the end-to-end model to explore the effects of different environmental parameters or reward functions on the models.:CHAPTER 1 INTRODUCTION 1 1.1 AUTONOMOUS DRIVING OVERVIEW 1 1.2 RESEARCH QUESTIONS AND METHODS 3 1.2.1 Research Questions 3 1.2.2 Research Methods 4 1.3 PAPER STRUCTURE 5 CHAPTER 2 RESEARCH BACKGROUND 7 2.1 RESEARCH STATUS 7 2.2 THEORETICAL BASIS 8 2.2.1 Machine Learning 8 2.2.2 Deep Learning 9 2.2.3 Reinforcement Learning 11 2.2.4 Deep Reinforcement Learning 14 CHAPTER 3 METHOD 15 3.1 SIMULATION PLATFORM 16 3.2 CONTROL TASK 17 3.3 OBSERVATION SPACE 18 3.3.1 Information as Observation (Non-end-to-end) 19 3.3.2 Images as Observation (End-to-end) 20 3.4 ACTION SPACE 22 3.5 ALGORITHM 23 3.5.1 Mathematical Foundations 23 3.5.2 Policy Iteration 25 3.6 POLICY ARCHITECTURE 25 3.6.1 Network Architecture for Non-end-to-end Model 26 3.6.2 Network Architecture for End-to-end Model 28 3.7 REWARD SHAPING 29 3.7.1 Calculation of Speed-based Reward Function 30 3.7.2 Calculation of the reward function based on the position of the agent relative to the right lane 31 CHAPTER 4 TRAINING PROCESS 33 4.1 TRAINING PROCESS OF NON-END-TO-END MODEL 34 4.2 TRAINING PROCESS OF END-TO-END MODEL 35 CHAPTER 5 RESULT 38 CHAPTER 6 TEST AND EVALUATION 41 6.1 EVALUATION OF END-TO-END MODEL 43 6.1.1 Speed Tests in Two Scenarios 43 6.1.2 Lateral Deviation between the Agent and the Right Lane’s Centerline 44 6.1.3 Orientation Deviation between the Agent and the Right Lane’s Centerline 45 6.2 COMPARISON OF THE END-TO-END MODEL TO TWO BASELINES IN SIMULATION 46 6.2.1 Comparison with Non-end-to-end Baseline 47 6.2.2 Comparison with PD Baseline 51 6.3 TEST THE EFFECT OF DIFFERENT WEIGHTS ASSIGNMENTS ON THE END-TO-END MODEL 53 CHAPTER 7 CONCLUSION 57 / Der Forscher entwickelte eine autonome Fahrsimulation, indem er ein End-to-End-Regelungsmodell mit Hilfe von Deep Reinforcement Learning-Algorithmen in der virtuellen Umgebung von Gym-duckietown trainierte. Die Kontrollstrategie des Modells wurde für die Aufgabe des Spurhaltens entwickelt. Es wurden mehrere Verstärkungslernalgorithmen implementiert, und der SAC-Algorithmus wurde ausgewählt, um ein Nicht-End-to-End-Modell mit den von der Umgebung bereitgestellten Informationen wie Geschwindigkeit als Eingabewerte sowie ein End-to-End-Modell mit den von der Frontkamera des Agenten aufgenommenen Bildern als Eingabe zu trainieren. In diesem Beitrag verglich der Forscher die Vor- und Nachteile der beiden Modelle unter Verwendung kinetischer Parameter in der Umgebung und führte eine Reihe von Experimenten zur Kontrollstrategie des End-to-End-Modells durch, um die Auswirkungen verschiedener Umgebungsparameter oder Belohnungsfunktionen auf die Modelle zu untersuchen.:CHAPTER 1 INTRODUCTION 1 1.1 AUTONOMOUS DRIVING OVERVIEW 1 1.2 RESEARCH QUESTIONS AND METHODS 3 1.2.1 Research Questions 3 1.2.2 Research Methods 4 1.3 PAPER STRUCTURE 5 CHAPTER 2 RESEARCH BACKGROUND 7 2.1 RESEARCH STATUS 7 2.2 THEORETICAL BASIS 8 2.2.1 Machine Learning 8 2.2.2 Deep Learning 9 2.2.3 Reinforcement Learning 11 2.2.4 Deep Reinforcement Learning 14 CHAPTER 3 METHOD 15 3.1 SIMULATION PLATFORM 16 3.2 CONTROL TASK 17 3.3 OBSERVATION SPACE 18 3.3.1 Information as Observation (Non-end-to-end) 19 3.3.2 Images as Observation (End-to-end) 20 3.4 ACTION SPACE 22 3.5 ALGORITHM 23 3.5.1 Mathematical Foundations 23 3.5.2 Policy Iteration 25 3.6 POLICY ARCHITECTURE 25 3.6.1 Network Architecture for Non-end-to-end Model 26 3.6.2 Network Architecture for End-to-end Model 28 3.7 REWARD SHAPING 29 3.7.1 Calculation of Speed-based Reward Function 30 3.7.2 Calculation of the reward function based on the position of the agent relative to the right lane 31 CHAPTER 4 TRAINING PROCESS 33 4.1 TRAINING PROCESS OF NON-END-TO-END MODEL 34 4.2 TRAINING PROCESS OF END-TO-END MODEL 35 CHAPTER 5 RESULT 38 CHAPTER 6 TEST AND EVALUATION 41 6.1 EVALUATION OF END-TO-END MODEL 43 6.1.1 Speed Tests in Two Scenarios 43 6.1.2 Lateral Deviation between the Agent and the Right Lane’s Centerline 44 6.1.3 Orientation Deviation between the Agent and the Right Lane’s Centerline 45 6.2 COMPARISON OF THE END-TO-END MODEL TO TWO BASELINES IN SIMULATION 46 6.2.1 Comparison with Non-end-to-end Baseline 47 6.2.2 Comparison with PD Baseline 51 6.3 TEST THE EFFECT OF DIFFERENT WEIGHTS ASSIGNMENTS ON THE END-TO-END MODEL 53 CHAPTER 7 CONCLUSION 57

Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. This thesis focuses on exploring the application of Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN) and Graph Neural Network (GNN), to address this problem. Traditional methods of estimating IR drop using commercial tools are time consuming, especially for complex designs with millions of transistors. To overcome that, ML algorithms are investigated for their ability to provide fast and accurate IR drop estimation. This thesis utilizes electrical, timing and physical features of the ASIC design as input to train the ML models. The scalability of the selected features allows for their effective application across various ASIC designs with very few adjustments. Experimental results demonstrate the advantages of ML models over commercial tools, offering significant improvements in prediction speed. Notably, GNNs, such as Graph Convolutional Network (GCN) models showed promising performance with low prediction errors in voltage drop estimation. The incorporation of graph-structures models opens new fields of research for accurate IR drop prediction. The conclusions drawn emphasize the effectiveness of ML algorithms in accurately estimating IR drop, thereby optimizing ASIC design efficiency. The application of ML models enables faster predictions and noticeably reducing calculation time. This contributes to enhancing energy efficiency and minimizing environmental impact through optimised power circuits. Future work can focus on exploring the scalability of the models by training on a smaller portion of the circuit and extrapolating predictions to the entire design seems promising for more efficient and accurate IR drop estimation in complex ASIC designs. These advantages present new opportunities in the field and extend the capabilities of ML algorithms in the task of IR drop prediction. / Noggrann uppskattning av spänningsfallet (IR-fall), i ASIC är en kritisk utmaning som påverkar deras prestanda och strömförbrukning. När tekniken går framåt och formstorlekarna krymper, blir det allt svårare att förutsäga IR-fall snabbt och exakt. Denna avhandling fokuserar på att utforska tillämpningen av ML-algoritmer, inklusive XGBoost, CNN och GNN, för att lösa detta problem. Traditionella metoder för att uppskatta IR-fall med kommersiella verktyg är tidskrävande, särskilt för komplexa konstruktioner med miljontals transistorer. För att övervinna det undersöks ML-algoritmer för deras förmåga att ge snabb och exakt IR-falluppskattning. Denna avhandling använder elektriska, timing och fysiska egenskaper hos ASIC-designen som input för att träna ML-modellerna. Skalbarheten hos de valda funktionerna möjliggör deras effektiva tillämpning över olika ASIC-designer med mycket få justeringar. Experimentella resultat visar fördelarna med ML-modeller jämfört med kommersiella verktyg, och erbjuder betydande förbättringar i förutsägelsehastighet. Noterbart är att GNNs, såsom GCN-modeller, visade lovande prestanda med låga prediktionsfel vid uppskattning av spänningsfall. Införandet av grafstrukturmodeller öppnar nya forskningsfält för exakt IRfallförutsägelse. De slutsatser som dras betonar effektiviteten hos MLalgoritmer för att noggrant uppskatta IR-fall, och därigenom optimera ASICdesigneffektiviteten. Tillämpningen av ML-modeller möjliggör snabbare förutsägelser och märkbart minskad beräkningstid. Detta bidrar till att förbättra energieffektiviteten och minimera miljöpåverkan genom optimerade kraftkretsar. Framtida arbete kan fokusera på att utforska skalbarheten hos modellerna genom att träna på en mindre del av kretsen och att extrapolera förutsägelser till hela designen verkar lovande för mer effektiv och exakt IR-falluppskattning i komplexa ASIC-designer. Dessa fördelar ger nya möjligheter inom området och utökar kapaciteten hos ML-algoritmer i uppgiften att förutsäga IR-fall.

This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.