Advanced Signal Processing Solutions for Brain-Computer Interfaces: From Theory to Practice

As the field of Brain-Computer Interfaces (BCI) is rapidly evolving within both academia and industry, the necessity of improving the signal processing module of such systems becomes of significant practical and theoretical importance. Additionally, the employment of Electroencephalography (EEG) headset, which is considered as the best non-invasive modality for collecting brain signals, offers a relatively more user-friendly experience, affordability, and flexibility of design to the developers of a BCI system. Motivated by the aforementioned facts, the thesis investigates several venues through which an EEG-based BCI can more accurately interpret the users' intention. The first part of the thesis is devoted to development of theoretical approaches by which the dimensionality of the collected EEG signals can be reduced with minimum information loss. In this part, two novel frameworks are proposed based on graph signal processing theory, referred to as the GD-BCI and the GDR-BCI, where the geometrical structure of the EEG electrodes are employed to define and configure the underlying graphs. The second part of the thesis is devoted to seeking practical, yet facile-to-implement, solutions to improve the classification accuracy of BCI systems. Finally, in the last part of the thesis, inspired by the recent surge of interest in hybrid BCIs, a novel framework is proposed for cuff-less blood pressure estimation to be further coupled with an EEG-based BCI. Referred to as the WAKE-BPAT, the proposed framework simultaneously processes Electrocardiography (ECG) and Photoplethysmogram (PPG) signals via an adaptive Kalman filtering approach

Near-Space Communications: the Last Piece of 6G Space-Air-Ground-Sea Integrated Network Puzzle

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics and unique problems of NS-COM network. Afterwards, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite (StratoSat) -to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic MIMO, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, StratoSat-based radar detection and imaging, NS-COM assisted enhanced global navigation system, NS-COM assisted intelligent unmanned system and free space optical (FSO) communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.Comment: 28 pages, 8 figures, 2 table

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements

Mobile and Wireless Communications

Mobile and Wireless Communications have been one of the major revolutions of the late twentieth century. We are witnessing a very fast growth in these technologies where mobile and wireless communications have become so ubiquitous in our society and indispensable for our daily lives. The relentless demand for higher data rates with better quality of services to comply with state-of-the art applications has revolutionized the wireless communication field and led to the emergence of new technologies such as Bluetooth, WiFi, Wimax, Ultra wideband, OFDMA. Moreover, the market tendency confirms that this revolution is not ready to stop in the foreseen future. Mobile and wireless communications applications cover diverse areas including entertainment, industrialist, biomedical, medicine, safety and security, and others, which definitely are improving our daily life. Wireless communication network is a multidisciplinary field addressing different aspects raging from theoretical analysis, system architecture design, and hardware and software implementations. While different new applications are requiring higher data rates and better quality of service and prolonging the mobile battery life, new development and advanced research studies and systems and circuits designs are necessary to keep pace with the market requirements. This book covers the most advanced research and development topics in mobile and wireless communication networks. It is divided into two parts with a total of thirty-four stand-alone chapters covering various areas of wireless communications of special topics including: physical layer and network layer, access methods and scheduling, techniques and technologies, antenna and amplifier design, integrated circuit design, applications and systems. These chapters present advanced novel and cutting-edge results and development related to wireless communication offering the readers the opportunity to enrich their knowledge in specific topics as well as to explore the whole field of rapidly emerging mobile and wireless networks. We hope that this book will be useful for students, researchers and practitioners in their research studies

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Contextualized Monitoring in the Marine Environment

Marine mammal monitoring has seen improvements in the last few decades with advances made to both the monitoring hardware and post-processing computation methods. The addition of tag-based hydrophones, Fastloc GPS units, and an ever-increasing array of IMU sensors, coupled with the use of energetics proxies such as Overall Dynamic Body Acceleration (ODBA), has led to new insights into marine mammal swimming behavior that would not be possible using traditional secondary-observer methods. However, these advances have primarily been focused on and implemented in wild animal tracking, with less attention paid to the managed environment. This is a particularly important gap, as the cooperative nature of managed animals allows for research on swimming kinematics and energetics behavior with an intricacy that is difficult to achieve in the wild. While proxy-based methods are useful for relative inter-or-intra-animal comparisons, they are not robust enough for absolute energetics estimates for the animals, which can limit our understanding of their metabolic patterns. Proxies such as ODBA are based on filtered on-animal IMU data, and measure the aggregate high-pass acceleration as an estimate for the magnitude of the animal’s activity level at a given point in time. Depending on its body structure and locomotive gait, tag placement on the animal and the specific filtering techniques used can significantly impact the results. Any relation made to energetics is then strictly a mapping: a relation that may apply well to an individual or group under specific experimental conditions, but is not generalizable. To address this gap, this dissertation presents new tag-based hardware and data processing methods for persistently estimating cetacean swimming kinematics and energetics, which are functional in both managed and wild settings. Unfortunately, localization techniques for managed environments have not been thoroughly explored, so a new animal tracking method is required to spatially contextualize information on swimming behavior. State-of-the-art wild cetacean localization operates via sparse GPS updates upon animal surfacings, and can be paired with biologging-tag-based odometry for a continuous track. Such an approach is hindered by the structure and scale of managed environments: GPS suffers from increased error near and within buildings, and current odometry methods are insufficiently precise for habitat scales where locations of interest might be separated by meters, rather than kilometers (such as in the wild). There is then a need for a tracking method that uses an alternate source of absolute animal locations that can achieve the high precision necessary for meaningful results given the spatial scale. To this end, this dissertation presents a novel animal localization framework, based on tracking and sensor filtering techniques from the field of robotics that have been tailored for use in this setting. Overall, this research targets two main gaps: 1) the lack of persistent, absolute estimates of animal swimming energetics and kinematics, and 2) the lack of a robust, precise localization method for managed cetaceans. To address these gaps, the hardware and animal tracking methods developed to enable the rest of the dissertation are first defined. Next, a physics-based approach to directly monitor cetacean swimming energetics is both presented and implemented to study animal propulsion patterns under varying effort conditions. Finally, a high-fidelity 3D monitoring framework is introduced for tracking institutionally-managed cetaceans, and is applied alongside the energetics estimation method to provide a first look at the potential of spatially-contextualized animal monitoring.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169756/1/gabaldon_1.pd