Adversarial network coding studies the transmission of data over networks affected by adversarial noise. In this realm, the noise is modeled by an omniscient adversary who is restricted to corrupting a proper subset of the network edges. In 2018, Ravagnani and Kschischang established a combinatorial framework for adversarial networks. The study was recently furthered by Beemer, Kilic and Ravagnani, with particular focus on the one-shot capacity: a measure of the maximum number of symbols that can be transmitted in a single use of the network without errors. In this thesis, both bounds and capacity-achieving schemes are provided for families of adversarial networks in multiple transmission rounds. We also demonstrate scenarios where we transmit more information using a network multiple times for communication versus using the network once. Some results in this thesis are joint work with Giuseppe Cotardo (Virginia Tech), Gretchen Matthews (Virginia Tech) and Alberto Ravagnani (Eindhoven University of Technology). / Master of Science / We study how to best transfer data across a communication network even if there is adversarial interference using network coding. Network coding is used in video streaming, autonomous vehicles, 5G and NextG communications, satellite networks, and Internet of Things (IoT) devices among other applications. It is the process that encodes data before sending it and decodes it upon receipt. It brings advantages such as increased network efficiency, improved reliability, reduced redundancy, enhanced resilience, and energy savings. We seek to enhance this valuable technique by determining optimal ways in which to utilize network coding schemes. We explore scenarios in which an adversary has partial access to a network. To examine the maximum data that can be communicated over one use of a network, we require the intermediate parts of the network process the information before forwarding it in a process called network decoding. In this thesis, we focus on characterizing when using a network multiple times for communication increases the amount of information that is received regardless of the worst-case adversarial attack, building on prior work that shows how underlying structure influences capacity. We design efficient methods for specific networks, to communicate at capacity.

Film is a medium which is mainly used for entertainment. It is a medium that is constantly expanding, and beside from it being a relaxing activity it can also be used for educational purposes. This is an aspect of film that should be valued, since it has the possibility to engage, educate and move its viewers. This is a study which aim is to analyze how employees within the care of disabled in Umeå have perceived educational movies. The movies that the staff has watched are a part of a basic training program, which all employees are required to fulfill. It is a total of three movies that have been watched by the workers. To examine their opinions six qualitative interviews were held with members of the staff. This study has therefor analyzed the employees’ views of the movies, rather than the movies themselves. The interviews were later analyzed in relation to the study’s theories, which include reception analyses, encoding/decoding and learning within an organization.The study has shown that the educational movies had both positive and negative aspects. When it comes to the movies' content, the informants had mostly positive things to say. They liked the information, the way the movies were made and have in some aspects incorporated what they learnt from the movies in their everyday life. When it comes to the experience of watching the movies, the informants have a more negative view. This mostly comes from the fact that they were disturbed while watching the movies, but also tired since they watched the movies late in the afternoon. These factors made it hard for the employees to concentrate while watching the movies.The study have shown that both the movies' content and the experience of watching the movies play two key roles in how the employees have perceived them. It is important that both parts satisfy the staff, in order for the organization to get their message across.

Near lossless source coding with side information only at the decoder, was first considered by Slepian and Wolf in 1970s, and rediscovered recently due to applications such as sensor network and distributed video coding. Suppose X is a source and Y is the side information. The coding scheme proposed by Slepian and Wolf, called SW coding, in which information only flows from the encoder to the decoder, was shown to achieve the rate H(X|Y) asymptotically for stationary ergodic source pairs, but not for non-ergodic case, shown by Yang and He. Recently, a new source coding paradigm called interactive encoding and decoding(IED) was proposed for near lossless coding with side information only at the decoder, where information flows in both ways, from the encoder to the decoder and vice verse. The results by Yang and He show that IED schemes are much more appealing than SW coding schemes to applications where the interaction between the encoder and the decoder is possible. However, the IED schemes proposed by Yang and He do not have an intrinsic structure that is amenable to design and implement in practice. Towards practical design, we restrict the encoding method to linear block codes, resulting in linear IED schemes. It is then shown that this restriction will not undermine the asymptotical performance of IED. Another step of practical design of IED schemes is to make the computational complexity incurred by encoding and decoding feasible. In the framework of linear IED, a scheme can be conveniently described by parity check matrices. Then we get an interesting trade-off between the density of the associated parity check matrices and the resulting symbol error probability. To implement the idea of linear IED and follow the instinct provided by the result above, Low Density Parity Check(LDPC) codes and Belief Propagation(BP) decoding are utilized. A successive LDPC code is proposed, and a new BP decoding algorithm is proposed, which applies to the case where the correlation between $Y$ and $X$ can be modeled as a finite state channel. Finally, simulation results show that linear IED schemes are indeed superior to SW coding schemes.

Near lossless source coding with side information only at the decoder, was first considered by Slepian and Wolf in 1970s, and rediscovered recently due to applications such as sensor network and distributed video coding. Suppose X is a source and Y is the side information. The coding scheme proposed by Slepian and Wolf, called SW coding, in which information only flows from the encoder to the decoder, was shown to achieve the rate H(X|Y) asymptotically for stationary ergodic source pairs, but not for non-ergodic case, shown by Yang and He. Recently, a new source coding paradigm called interactive encoding and decoding(IED) was proposed for near lossless coding with side information only at the decoder, where information flows in both ways, from the encoder to the decoder and vice verse. The results by Yang and He show that IED schemes are much more appealing than SW coding schemes to applications where the interaction between the encoder and the decoder is possible. However, the IED schemes proposed by Yang and He do not have an intrinsic structure that is amenable to design and implement in practice. Towards practical design, we restrict the encoding method to linear block codes, resulting in linear IED schemes. It is then shown that this restriction will not undermine the asymptotical performance of IED. Another step of practical design of IED schemes is to make the computational complexity incurred by encoding and decoding feasible. In the framework of linear IED, a scheme can be conveniently described by parity check matrices. Then we get an interesting trade-off between the density of the associated parity check matrices and the resulting symbol error probability. To implement the idea of linear IED and follow the instinct provided by the result above, Low Density Parity Check(LDPC) codes and Belief Propagation(BP) decoding are utilized. A successive LDPC code is proposed, and a new BP decoding algorithm is proposed, which applies to the case where the correlation between $Y$ and $X$ can be modeled as a finite state channel. Finally, simulation results show that linear IED schemes are indeed superior to SW coding schemes.

Brain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms. / text

Colours have been proven to have both a great physical and psychological impact on human beings. This essay investigates the rebranding of 37 Health Care Centers, located in Norrbotten, Sweden, and how 10 interviewees have experienced the new choice of colour on the logotypes of these Health Care Centers. The theoretical framework used in this research process, consists of theories related to rebranding, the communication process, encoding/decoding and Goethe’s Theory of Colours. To gather information about how the 10 inhabitants have experienced the new choice of colour, a semistructured form of interview was used. In addition to these interviews, three people employed by Norrbotten County Council (NLL) were interviewed, to get to know more about the rebranding process and the message behind the chosen colours of the logotype. Results show that the inhabitants had an overall positive reaction to the new colour choices, but that younger target groups lacked information about the new logotype and the rebranding. Due to these results, recommendations were made that NLL should expand the quantity of media channels to reach these younger target groups, and thereby be able to attract more patients to their Health Care Centers. NLL would also profit from investigating the inhabitants experiences related to the new colour choices themselves, but in a larger scale, to achieve greater knowledge of how to proceed with colour choices in the future.

Motivated by potential applications in wireless sensor networks, this thesis considers the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). A joint source-channel decoder is presented which can exploit the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder (JSD) for multiple sources to achieve a computational complexity that scales with the number of sources. The MUD and the JSD, then iteratively exchange extrinsic information to improve the interference cancellation. Experimental results show that, compared to a non-iterative decoder, the proposed iterative decoder is more robust against potential performance degradation due to correlated channel interference and offers better near far resistance.

The present study firstly aimed to identify children with delayed reading who were missing the component skills of decoding fluency and who also displayed antisocial behaviour in the classroom. It also aimed to replicate with them an intervention designed by Church, Nixon, Zintl and Williams (2005). The study finally aimed to explore the question of whether children who have both a reading delay and a disruptive behaviour disorder require a reinforcement scheme to maintain their engagement in learning activities. Six participants worked with same-age peer tutors on specially-designed practice activities for approximately 20 minutes a day, four times a week, for 8-18 sessions. Improvement in decoding fluency and prose reading fluency was tracked for each child throughout the intervention. Results showed that the six participants gained, on average, sixteen months on their age-equivalent score for reading rate. Decoding fluency scores increased from a pre-test average of 16 correct graphemes per minute to 32 correct graphemes per minute at the post-test measure. Reading accuracy improved by an average of five months and reading comprehension by an average of six months. The gains in reading rate are most likely due to the practice opportunities afforded by the testing procedures as decoding fluency scores did not improve enough to have had a direct impact on the learners’ prose reading ability. Implications for remedial reading interventions with children with behaviour problems are discussed.

Motivated by potential applications in wireless sensor networks, this thesis considers the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). A joint source-channel decoder is presented which can exploit the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder (JSD) for multiple sources to achieve a computational complexity that scales with the number of sources. The MUD and the JSD, then iteratively exchange extrinsic information to improve the interference cancellation. Experimental results show that, compared to a non-iterative decoder, the proposed iterative decoder is more robust against potential performance degradation due to correlated channel interference and offers better near far resistance.

Recently, lattices - a mathematical representation of infinite discrete points in the Euclidean space, have become an effective way to describe and analyze communication systems especially system those that can be modeled as linear Gaussian vector channel model. Channel codes based on lattices are preferred due to three facts: lattice codes have simple structure, the code can achieve the limits of the channel, and they can be decoded efficiently using lattice decoders which can be considered as the Closest Lattice Point Search (CLPS). Since the time lattice codes were introduced to Multiple Input Multiple Output (MIMO) channel, Sphere Decoder (SD) has been an efficient way to implement lattice decoders. Sphere decoder offers the optimal performance at the expense of high decoding complexity especially for low signal-to-noise ratios (SNR) and for high- dimensional systems. On the other hand, linear and non-linear receivers, Minimum Mean Square Error (MMSE), and MMSE Decision-Feedback Equalization (DFE), provide the lowest decoding complexity but unfortunately with poor performance. Several studies works have been conducted in the last years to address the problem of designing low complexity decoders for the MIMO channel that can achieve near optimal performance. It was found that sequential decoders using backward tree 
search can bridge the gap between SD and MMSE. The sequential decoder provides an interesting performance-complexity trade-off using a bias term. Yet, the sequential decoder still suffers from high complexity for mid-to-high SNR values. In this work, we propose a new algorithm for Bidirectional Fano sequential Decoder (BFD) in order to reduce the mid-to-high SNR complexity. Our algorithm consists of first constructing a unidirectional Sequential Decoder based on forward search using the QL decomposition. After that, BFD incorporates two searches, forward and backward, to work simultaneously till they merge and find the closest lattice point to the received signal. We show via computer simulations that BFD can reduce the mid-to-high SNR complexity for the sequential decoder without changing the bias value.