Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

Biophysical modeling of a cochlear implant system: progress on closed-loop design using a novel patient-specific evaluation platform

The modern cochlear implant is one of the most successful neural stimulation devices, which partially mimics the workings of the auditory periphery. In the last few decades it has created a paradigm shift in hearing restoration of the deaf population, which has led to more than 324,000 cochlear implant users today. Despite its great success there is great disparity in patient outcomes without clear understanding of the aetiology of this variance in implant performance. Furthermore speech recognition in adverse conditions or music appreciation is still not attainable with today's commercial technology. This motivates the research for the next generation of cochlear implants that takes advantage of recent developments in electronics, neuroscience, nanotechnology, micro-mechanics, polymer chemistry and molecular biology to deliver high fidelity sound. The main difficulties in determining the root of the problem in the cases where the cochlear implant does not perform well are two fold: first there is not a clear paradigm on how the electrical stimulation is perceived as sound by the brain, and second there is limited understanding on the plasticity effects, or learning, of the brain in response to electrical stimulation. These significant knowledge limitations impede the design of novel cochlear implant technologies, as the technical specifications that can lead to better performing implants remain undefined. The motivation of the work presented in this thesis is to compare and contrast the cochlear implant neural stimulation with the operation of the physiological healthy auditory periphery up to the level of the auditory nerve. As such design of novel cochlear implant systems can become feasible by gaining insight on the question `how well does a specific cochlear implant system approximate the healthy auditory periphery?' circumventing the necessity of complete understanding of the brain's comprehension of patterned electrical stimulation delivered from a generic cochlear implant device. A computational model, termed Digital Cochlea Stimulation and Evaluation Tool (‘DiCoStET’) has been developed to provide an objective estimate of cochlear implant performance based on neuronal activation measures, such as vector strength and average activation. A patient-specific cochlea 3D geometry is generated using a model derived by a single anatomical measurement from a patient, using non-invasive high resolution computed tomography (HRCT), and anatomically invariant human metrics and relations. Human measurements of the neuron route within the inner ear enable an innervation pattern to be modelled which joins the space from the organ of Corti to the spiral ganglion subsequently descending into the auditory nerve bundle. An electrode is inserted in the cochlea at a depth that is determined by the user of the tool. The geometric relation between the stimulation sites on the electrode and the spiral ganglion are used to estimate an activating function that will be unique for the specific patient's cochlear shape and electrode placement. This `transfer function', so to speak, between electrode and spiral ganglion serves as a `digital patient' for validating novel cochlear implant systems. The novel computational tool is intended for use by bioengineers, surgeons, audiologists and neuroscientists alike. In addition to ‘DiCoStET’ a second computational model is presented in this thesis aiming at enhancing the understanding of the physiological mechanisms of hearing, specifically the workings of the auditory synapse. The purpose of this model is to provide insight on the sound encoding mechanisms of the synapse. A hypothetical mechanism is suggested in the release of neurotransmitter vesicles that permits the auditory synapse to encode temporal patterns of sound separately from sound intensity. DiCoStET was used to examine the performance of two different types of filters used for spectral analysis in the cochlear implant system, the Gammatone type filter and the Butterworth type filter. The model outputs suggest that the Gammatone type filter performs better than the Butterworth type filter. Furthermore two stimulation strategies, the Continuous Interleaved Stimulation (CIS) and Asynchronous Interleaved Stimulation (AIS) have been compared. The estimated neuronal stimulation spatiotemporal patterns for each strategy suggest that the overall stimulation pattern is not greatly affected by the temporal sequence change. However the finer detail of neuronal activation is different between the two strategies, and when compared to healthy neuronal activation patterns the conjecture is made that the sequential stimulation of CIS hinders the transmission of sound fine structure information to the brain. The effect of the two models developed is the feasibility of collaborative work emanating from various disciplines; especially electrical engineering, auditory physiology and neuroscience for the development of novel cochlear implant systems. This is achieved by using the concept of a `digital patient' whose artificial neuronal activation is compared to a healthy scenario in a computationally efficient manner to allow practical simulation times.Open Acces

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression