Communication

Summary

Communication is usually defined as the transmission of information. The term can also refer to the message itself, or the field of inquiry studying these transmissions, also known as communication studies. The precise definition of communication is disputed. Controversial issues are whether unintentional or failed transmissions are included and whether communication does not just transmit meaning but also create it. Models of communication aim to provide a simplified overview of its main components and their interaction. Many models include the idea that a source uses a coding system to express information in the form of a message. The source uses a channel to send the message to a receiver who has to decode it in order to understand its meaning. Channels are usually discussed in terms of the senses used to perceive the message, like hearing, sight, smell, touch, and taste. Communication can be classified based on whether information is exchanged between humans, members of other spec

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https://en.wikipedia.org/wiki/Communication

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Active Debris Removal missions consist of sending a satellite in space and removing one or more debris from their current orbit. A key challenge is to obtain information about the uncooperative target. By gathering the velocity, position, and rotation of the desired object, the satellite is able to plan its trajectory and define the sequence of approach. It requires the use of a variety of sensors with often a high data rate. For this task, a dedicated payload computer is envisioned with the responsibility of processing the information from the various rendezvous sensors. This component has the goal to provide meaningful information to the main satellite computer about the targeted debris. The focus of this work is on the data processing, the number of elements, and the electrical energy with constraints due to internal communication.First, an avionic testbench was built at the EPFL Space Center to assess early hardware and software architectures. The goal is to enable Hardware-In-the-Loop testing while developing the payload computer. A communication data bus has been implemented between the Platform and the Payload On-Board Computer. Emphasis was placed on the reliability of the high-level protocol and multiple concepts for improvement were analyzed.In a second phase, the testbench has been extended to support other data buses. The aim was to develop a reliable and efficient backup or fall-back data bus in case of failure in the main link. Four data buses have been tested with extensive analyses on their resilience to error and the efficiency of their data exchange.In parallel, extensive work has been performed to develop a simulation and optimization tool. Its goal is to support the design of payload avionic architectures for ADR missions by providing trade-offs and analyses. In the first iteration, a simulator has been created with instances of various high-level elements modeled.The second iteration introduced the additional dimension of optimization. It is trying to determine the best set of instruments and algorithms to use. With this capability, numerous analyses have been conducted on the influence of various parameters linked to the general optimizer behavior. It allows us to better understand the strength and limitations of the tool.The third step regarding the tool was to start its verification. The task has been to develop an Hardware-In-the-Loop architecture to compare its behavior with the results of the optimizer. The implementation of various mock-up algorithms enables the verification of their models in the tool. These analyses guarantee the feasibility of the outputted solution.The last part was dedicated to the creation and analysis of a realistic payload avionic architecture. The goal was to test the capability of the tool with hardware elements inspired by actual components. This work has shown the procedure to efficiently use the tool in the design phase of a mission.In conclusion, the work on the communication between the Payload and the Platform On-Board Computer has brought valuable lessons and experiences to the projects. They can now be used for the establishment of the high-level protocol into ClearSpace-1 flight software. In addition, the optimizer created allows tackling the design of complex payload avionic architecture where mass saving and processing resources are crucial. It is an essential point to develop a highly efficient payload computer for an Active Debris Removal satellite.

EPFL2022

Coding for Communications and Secrecy

Mani Bastaniparizi

Shannon, in his landmark 1948 paper, developed a framework for characterizing the fundamental limits of information transmission. Among other results, he showed that reliable communication over a channel is possible at any rate below its capacity. In 2008, Arikan discovered polar codes; the only class of explicitly constructed low-complexity codes that achieve the capacity of any binary-input memoryless symmetric-output channel. Arikan's polar transform turns independent copies of a noisy channel into a collection of synthetic almost-noiseless and almost-useless channels. Polar codes are realized by sending data bits over the almost-noiseless channels and recovering them by using a low-complexity successive-cancellation (SC) decoder, at the receiver. In the first part of this thesis, we study polar codes for communications. When the underlying channel is an erasure channel, we show that almost all correlation coefficients between the erasure events of the synthetic channels decay rapidly. Hence, the sum of the erasure probabilities of the information-carrying channels is a tight estimate of the block-error probability of polar codes when used for communication over the erasure channel. We study SC list (SCL) decoding, a method for boosting the performance of short polar codes. We prove that the method has a numerically stable formulation in log-likelihood ratios. In hardware, this formulation increases the decoding throughput by 53% and reduces the decoder's size about 33%. We present empirical results on the trade-off between the length of the CRC and the performance gains in a CRC-aided version of the list decoder. We also make numerical comparisons of the performance of long polar codes under SC decoding with that of short polar codes under SCL decoding. Shannon's framework also quantifies the secrecy of communications. Wyner, in 1975, proposed a model for communications in the presence of an eavesdropper. It was shown that, at rates below the secrecy capacity, there exist reliable communication schemes in which the amount of information leaked to the eavesdropper decays exponentially in the block-length of the code. In the second part of this thesis, we study the rate of this decay. We derive the exact exponential decay rate of the ensemble-average of the information leaked to the eavesdropper in Wyner's model when a randomly constructed code is used for secure communications. For codes sampled from the ensemble of i.i.d. random codes, we show that the previously known lower bound to the exponent is exact. Our ensemble-optimal exponent for random constant-composition codes improves the lower bound extant in the literature. Finally, we show that random linear codes have the same secrecy power as i.i.d. random codes. The key to securing messages against an eavesdropper is to exploit the randomness of her communication channel so that the statistics of her observation resembles that of a pure noise process for any sent message. We study the effect of feedback on this approximation and show that it does not reduce the minimum entropy rate required to approximate a given process. However, we give examples where variable-length schemes achieve much larger exponents in this approximation in the presence of feedback than the exponents in systems without feedback. Upper-bounding the best exponent that block codes attain, we conclude that variable-length coding is necessary for achieving the improved exponents.

EPFL2017

Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

Elly Gysels

By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a classifier have to be extracted. This dissertation addresses the extraction of features in the framework of BCIs. On the one hand, new features are proposed. On the other hand, feature selection algorithms are investigated in order to select relevant features. Currently existing BCIs mostly use power estimates in some pre-defined frequency bands, which are single-channel features. Some authors report on the use of multichannel features, but interactions between specific brain regions have not yet been studied. We propose to use the synchronization feature Phase Locking Value (PLV) for the classification of spontaneous EEG recorded during different mental tasks. It is fast to compute and can be applied to relatively short time windows, two important assets for BCI applications. In a first instance, average synchronization values are considered. Tests on offline data show that significant classification accuracies can be obtained by the sole use of PLV. This demonstrates the relevance of synchronization features for the classification of EEG in this context. We found that PLV and power features do not clearly outperform each other, but their combination often leads to significantly improved results and never significantly deteriorates the classification accuracies obtained by the separate subsets. In the next step, feature selection algorithms are investigated in order to select the most interesting features. We show that Genetic Algorithms (GAs) as well as SVM-based recursive feature elimination (SVM-rfe) select physiologically meaningful features. As they are slow (computation times on the order of days and hours respectively) and thus cannot practically be used for BCIs, a modified version of the Fast Correlation-Based Filter (FCBF) is proposed. In this study, FCBF generalizes well and achieves good classification accuracies with very few features. The correspondence of the selected EEG signals with neurophysiological evidence is even stronger than for GAs and SVM-rfe. In addition, this algorithm is fast (computation time on the order of minutes) and so it can be applied between two recording sessions. Comparing the classification results obtained with broadband and narrowband power and PLV features selected by FCBF learns that power features are preferably computed in the narrower 8–12Hz frequency band and that for PLV features, the 8–30Hz frequency band is the better one. Furthermore, FCBF is used to evaluate a set of features comprising, on the one hand, the PLV for all possible electrode pairs, PLV averages, the cluster participation index and power features (band power and statistical mean frequency (SMF)), computed from the EEG signals. On the other hand, power (total power and SMF) and synchronization features derived from the empirical mode decomposition of these signals were included. They all proved useful. It is recommended to initially consider power features and only the PLV averages, because the resulting set of features is substantially smaller than when using PLV for all possible electrode pairs. Automatic detection of artifacts and rejection of the corresponding data window for selecting the features and training the classifier, results in, if any, an increase of classification accuracy that is generally not greater than 1%, for our data. Application of global and local PCA-based denoising techniques never yielded improved results for two of the five subjects we analyzed. If it yields better results for the other 3 subjects, the increase never exceeds 1.84%. Application of the proposed methods to blind data of the third international BCI competition held in 2005 gave significant classification accuracies. This further illustrates the relevance and the potential of the investigated techniques. We conclude that PLV may complement currently used features and improve future BCI systems. PLV is well suited for BCI applications, because of its fast computation, needed for online feedback systems. The Fast Correlation-Based Filter is a valuable tool for evaluating features and selecting a subset of relevant features.

EPFL2005