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The VLSI implementation complexity of a low density parity check (LDPC) decoder is largely influenced by the interconnect and the storage requirements. This dissertation presents the decoder architectures for regular and irregular LDPC codes that provide substantial gains over existing academic and commercial implementations. Several structured properties of LDPC codes and decoding algorithms are observed and are used to construct hardware implementation with reduced processing complexity. The proposed architectures utilize an on-the-fly computation paradigm which permits scheduling of the computations in a way that the memory requirements and re-computations are reduced. Using this paradigm, the run-time configurable and multi-rate VLSI architectures for the rate compatible array LDPC codes and irregular block LDPC codes are designed. Rate compatible array codes are considered for DSL applications. Irregular block LDPC codes are proposed for IEEE 802.16e, IEEE 802.11n, and IEEE 802.20. When compared with a recent implementation of an 802.11n LDPC decoder, the proposed decoder reduces the logic complexity by 6.45x and memory complexity by 2x for a given data throughput. When compared to the latest reported multi-rate decoders, this decoder design has an area efficiency of around 5.5x and energy efficiency of 2.6x for a given data throughput. The numbers are normalized for a 180nm CMOS process. Properly designed array codes have low error floors and meet the requirements of magnetic channel and other applications which need several Gbps of data throughput. A high throughput and fixed code architecture for array LDPC codes has been designed. No modification to the code is performed as this can result in high error floors. This parallel decoder architecture has no routing congestion and is scalable for longer block lengths. When compared to the latest fixed code parallel decoders in the literature, this design has an area efficiency of around 36x and an energy efficiency of 3x for a given data throughput. Again, the numbers are normalized for a 180nm CMOS process. In summary, the design and analysis details of the proposed architectures are described in this dissertation. The results from the extensive simulation and VHDL verification on FPGA and ASIC design platforms are also presented.

As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required modelbased design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder-decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder-decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder-decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageingindependent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.

Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. In this work, we attempted to develop Diagnostic Captioning systems, based on novel Deep Learning approaches, to investigate to what extent Neural Networks are capable of performing medical image tagging, as well as automatically generating a diagnostic text from a set of medical images. Towards this objective, the first step is concept detection, which boils down to predicting the relevant tags for X-RAY images, whereas the ultimate goal is caption generation. To this end, we further participated in ImageCLEFmedical 2022 evaluation campaign, addressing both the concept detection and the caption prediction tasks by developing baselines based on Deep Neural Networks; including image encoders, classifiers and text generators; in order to get a quantitative measure of my proposed architectures’ performance [28]. My contribution to the evaluation campaign, as part of this work and on behalf of NeuralDynamicsLab¹ group at KTH Royal Institute of Technology, within the school of Electrical Engineering and Computer Science, ranked 4th in the former and 5th in the latter task [55, 68] among 12 groups included within the top-10 best performing submissions in both tasks. / Diagnostisk textning avser automatisk generering från en diagnostisk text från en uppsättning medicinska bilder av en patient som samlats in under en undersökning och den kan hjälpa oerfarna läkare och radiologer, minska kliniska fel eller hjälpa erfarna yrkesmän att producera diagnostiska rapporter snabbare [59]. Därför kan verktyg som skulle hjälpa läkare och radiologer att producera rapporter av högre kvalitet på kortare tid vara av stort intresse för medicinska bildbehandlingsavdelningar, såväl som leda till inverkan på forskning om djupinlärning, vilket gör den domänen särskilt intressant för personer som är involverade i den biomedicinska industrin och djupinlärningsforskare. I detta arbete var mitt huvudmål att utveckla system för diagnostisk textning, med hjälp av nya tillvägagångssätt som används inom djupinlärning, för att undersöka i vilken utsträckning automatisk generering av en diagnostisk text från en uppsättning medi-cinska bilder är möjlig. Mot detta mål är det första steget konceptdetektering som går ut på att förutsäga relevanta taggar för röntgenbilder, medan slutmålet är bildtextgenerering. Jag deltog i ImageCLEF Medical 2022-utvärderingskampanjen, där jag deltog med att ta itu med både konceptdetektering och bildtextförutsägelse för att få ett kvantitativt mått på prestandan för mina föreslagna arkitekturer [28]. Mitt bidrag, där jag representerade forskargruppen NeuralDynamicsLab² , där jag arbetade som ledande forskningsingenjör, placerade sig på 4:e plats i den förra och 5:e i den senare uppgiften [55, 68] bland 12 grupper som ingår bland de 10 bästa bidragen i båda uppgifterna.