In this thesis, findings in three areas of computer vision estimation are presented. First, an improvement to the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm is presented in which gyroscope data is incorporated to compensate for camera rotation. This improved algorithm is then compared with the original algorithm and shown to be more effective at tracking features in the presence of large rotational motion. Next, a deep neural network approach to depth estimation is presented. Equations are derived relating camera and feature motion to depth. The information necessary for depth estimation is given as inputs to a deep neural network, which is trained to predict depth across an entire scene. This deep neural network approach is shown to be effective at predicting the general structure of a scene. Finally, a method of passively estimating the position and velocity of constant velocity targets using only bearing and time-to-collision measurements is presented. This method is paired with a path planner to avoid tracked targets. Results are given to show the effectiveness of the method at avoiding collision while maneuvering as little as possible.

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Deep Neural Networks (DNNs) are becoming increasingly important in various aspects of human life, particularly in safety-critical areas such as autonomous driving and aerospace systems. However, soft errors including bit-flips can significantly impact the performance of these systems, leading to serious consequences. To ensure the reliability of DNNs, it is essential to guarantee their performances. Many solutions have been proposed to enhance the trustworthiness of DNNs, including traditional methods like error correcting code (ECC) that can mitigate and detect soft errors but come at a high cost of redundancy. This thesis proposes a new method of correcting soft errors in DNNs using Deep Reinforcement Learning (DRL) and Transfer Learning (TL). DRL agent can learn the knowledge of identifying the layer-wise critical weights of a DNN. To accelerate the training time, TL is used to apply this knowledge to train other layers. The primary objective of this method is to ensure acceptable performance of a DNN by mitigating the impact of errors on it while maintaining low redundancy. As a case study, we tested the proposed method approach on a multilayer perception (MLP) and ResNet-18, and our results show that our method can save around 25% redundancy compared to the baseline method ECC while achieving the same level of performance. With the same redundancy, our approach can boost system performance by up to twice that of conventional methods. By implementing TL, the training time of MLP is shortened to around 81.11%, and that of ResNet-18 is shortened to around 57.75%. / DNNs blir allt viktigare i olika aspekter av mänskligt liv, särskilt inom säkerhetskritiska områden som autonom körning och flygsystem. Mjuka fel inklusive bit-flip kan dock påverka prestandan hos dessa system avsevärt, vilket leder till allvarliga konsekvenser. För att säkerställa tillförlitligheten hos DNNs är det viktigt att garantera deras prestanda. Många lösningar har föreslagits för att förbättra tillförlitligheten för DNNs, inklusive traditionella metoder som ECC som kan mildra och upptäcka mjuka fel men som har en hög kostnad för redundans. Denna avhandling föreslår en ny metod för att korrigera mjuka fel i DNN med DRL och TL. DRL-agenten kan lära sig kunskapen om att identifiera de lagermässiga kritiska vikterna för en DNN. För att påskynda träningstiden används TL för att tillämpa denna kunskap för att träna andra lager. Det primära syftet med denna metod är att säkerställa acceptabel prestanda för en DNN genom att mildra inverkan av fel på den samtidigt som låg redundans bibehålls. Som en fallstudie testade vi den föreslagna metodmetoden på en MLP och ResNet-18, och våra resultat visar att vår metod kan spara cirka 25% redundans jämfört med baslinjemetoden ECC samtidigt som vi uppnår samma prestationsnivå. Med samma redundans kan vårt tillvägagångssätt öka systemets prestanda med upp till dubbelt så högt som för konventionella metoder. Genom att implementera TL förkortas träningstiden för MLP till cirka 81.11%, och den för ResNet-18 förkortas till cirka 57.75%.

This study focuses on improving the quality of statistical speech synthesis based on sinusoidal models. Vocoders play a crucial role during the parametrisation and reconstruction process, so we first lead an experimental comparison of a broad range of the leading vocoder types. Although our study shows that for analysis / synthesis, sinusoidal models with complex amplitudes can generate high quality of speech compared with source-filter ones, component sinusoids are correlated with each other, and the number of parameters is also high and varies in each frame, which constrains its application for statistical speech synthesis. Therefore, we first propose a perceptually based dynamic sinusoidal model (PDM) to decrease and fix the number of components typically used in the standard sinusoidal model. Then, in order to apply the proposed vocoder with an HMM-based speech synthesis system (HTS), two strategies for modelling sinusoidal parameters have been compared. In the first method (DIR parameterisation), features extracted from the fixed- and low-dimensional PDM are statistically modelled directly. In the second method (INT parameterisation), we convert both static amplitude and dynamic slope from all the harmonics of a signal, which we term the Harmonic Dynamic Model (HDM), to intermediate parameters (regularised cepstral coefficients (RDC)) for modelling. Our results show that HDM with intermediate parameters can generate comparable quality to STRAIGHT. As correlations between features in the dynamic model cannot be modelled satisfactorily by a typical HMM-based system with diagonal covariance, we have applied and tested a deep neural network (DNN) for modelling features from these two methods. To fully exploit DNN capabilities, we investigate ways to combine INT and DIR at the level of both DNN modelling and waveform generation. For DNN training, we propose to use multi-task learning to model cepstra (from INT) and log amplitudes (from DIR) as primary and secondary tasks. We conclude from our results that sinusoidal models are indeed highly suited for statistical parametric synthesis. The proposed method outperforms the state-of-the-art STRAIGHT-based equivalent when used in conjunction with DNNs. To further improve the voice quality, phase features generated from the proposed vocoder also need to be parameterised and integrated into statistical modelling. Here, an alternative statistical model referred to as the complex-valued neural network (CVNN), which treats complex coefficients as a whole, is proposed to model complex amplitude explicitly. A complex-valued back-propagation algorithm using a logarithmic minimisation criterion which includes both amplitude and phase errors is used as a learning rule. Three parameterisation methods are studied for mapping text to acoustic features: RDC / real-valued log amplitude, complex-valued amplitude with minimum phase and complex-valued amplitude with mixed phase. Our results show the potential of using CVNNs for modelling both real and complex-valued acoustic features. Overall, this thesis has established competitive alternative vocoders for speech parametrisation and reconstruction. The utilisation of proposed vocoders on various acoustic models (HMM / DNN / CVNN) clearly demonstrates that it is compelling to apply them for the parametric statistical speech synthesis.

abstract: In the era of artificial intelligent (AI), deep neural networks (DNN) have achieved accuracy on par with humans on a variety of recognition tasks. However, the high computation and storage requirement of DNN training and inference have posed challenges to deploying or locally training the DNNs on mobile and wearable devices. Energy-efficient hardware innovation from circuit to architecture level is required.In this dissertation, a smart electrocardiogram (ECG) processor is first presented for ECG-based authentication as well as cardiac monitoring. The 65nm testchip consumes 1.06 μW at 0.55 V for real-time ECG authentication achieving equal error rate of 1.7% for authentication on an in-house 645-subject database. Next, a couple of SRAM-based in-memory computing (IMC) accelerators for deep learning algorithms are presented. Two single-array macros titled XNOR-SRAM and C3SRAM are based on resistive and capacitive networks for XNOR-ACcumulation (XAC) operations, respectively. XNOR-SRAM and C3SRAM macros in 65nm CMOS achieve energy efficiency of 403 TOPS/W and 672 TOPS/W, respectively. Built on top of these two single-array macro designs, two multi-array architectures are presented. The XNOR-SRAM based architecture titled “Vesti” is designed to support configurable multibit activations and large-scale DNNs seamlessly. Vesti employs double-buffering with two groups of in-memory computing SRAMs, effectively hiding the write latency of IMC SRAMs. The Vesti accelerator in 65nm CMOS achieves energy consumption of <20 nJ for MNIST classification and <40μJ for CIFAR-10 classification at 1.0 V supply. More recently, a programmable IMC accelerator (PIMCA) integrating 108 C3SRAM macros of a total size of 3.4 Mb is proposed. The28nm prototype chip achieves system-level energy efficiency of 437/62 TOPS/W at 40 MHz, 1 V supply for DNNs with 1b/2b precision. In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

In recent years, there has been a significant push to implement Deep Neural Networks (DNNs) on edge devices, which requires power and hardware efficient circuits to carry out the intensive matrix-vector multiplication (MVM) operations. This work presents hardware efficient MVM implementation techniques using bit-serial arithmetic and a novel MSB first computation circuit. The proposed designs take advantage of the pre-trained network weight parameters, which are already known in the design stage. Thus, the partial computation results can be pre-computed and stored into look-up tables. Then the MVM results can be computed in a bit-serial manner without using multipliers. The proposed novel circuit implementation for convolution filters and rectified linear activation function used in deep neural networks conducts computation in an MSB-first bit-serial manner. It can predict earlier if the outcomes of filter computations will be negative and subsequently terminate the remaining computations to save power. The benefits of using the proposed MVM implementations techniques are demonstrated by comparing the proposed design with conventional implementation. The proposed circuit is implemented on an FPGA. It shows significant power and performance improvements compared to the conventional designs implemented on the same FPGA.

Background: The usage of AI is becoming increasingly necessary in almost every industry, including marketing and branding. AI can help managers, marketers and designers in the marketing and branding sectors to overcome realistic and practical challenges by providing data-driven results. These results could be used in making decisions. Nevertheless, implementing AI systems and the acceptance of it varies widely across different industries, with building brands is still behind.  Purpose: This research aims to develop a deeper understanding of why AI systems are not yet commonly used in the branding industry with emphasis on how it could be useful. As a result, the main opportunities and threats to the usage of AI in branding as seen by branding- and IT specialists are explored and expressed.  Method: To achieve the purpose of this study, a qualitative study was conducted. Semi-structured interviews were used as means to collect primary data and in total 15 interviews with branding and IT specialists were carried out. The data was transcribed and analyzed according to thematic analysis which emerged in four main themes.  Conclusion: The results show that AI is capable of creating brand elements, with limitations to mostly non-visual brand elements due to the lack of creativity and emotions in AI solutions. The findings indicate that the perceived possibilities of implementing AI in branding mostly are cost- and time-related since AI tends to be capable of solving tasks which are cost- and time-consuming. Furthermore, the perceived threats mainly involve i) losing a job or ii) intrude on the roles of branding professionals.

Machine learning (ML) is an area of computer science that gives computers the ability to learn data patterns without prior programming for those patterns. Using neural networks in this area is based on simulating the biological functions of neurons in brains to learn patterns in data, giving computers a predictive ability to comprehend how data can be clustered. This research investigates the possibilities of using neural networks for classifying email, i.e. working as an email case manager. A Deep Neural Network (DNN) are multiple layers of neurons connected to each other by trainable weights. The main objective of this thesis was to evaluate how the three input arguments - data size, training time and neural network structure – affects the accuracy of Deep Neural Networks pattern recognition; also an evaluation of how the DNN performs compared to the statistical ML method, Naïve Bayes, in the form of prediction accuracy and complexity; and finally the viability of the resulting DNN as a case manager. Results show an improvement of accuracy on our networks with the increase of training time and data size respectively. By testing increasingly complex network structures (larger networks of neurons with more layers) it is observed that overfitting becomes a problem with increased training time, i.e. how accuracy decrease after a certain threshold of training time. Naïve Bayes classifiers performs worse than DNN in terms of accuracy, but better in reduced complexity; making NB viable on mobile platforms. We conclude that our developed prototype may work well in tangent with existing case management systems, tested by future research.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21391号 / 情博第677号 / 新制||情||117(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 河原 達也, 教授 黒橋 禎夫, 教授 壇辻 正剛, 准教授 南條 浩輝 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23455号 / 理博第4749号 / 新制||理||1681(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 成瀬 元 助教 松岡 廣繁, 教授 生形 貴男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM