An Investigation of Single-Core and Multi-Core Computing Methods for Biosignal Processing

This paper provides a single-core and multi-core processor design for applications involving highly parallel processing and sluggish biosignal events in health surveillance systems. An instruction memory (IM), a data memory (DM), and a processor core (PC) make up the single-core design. In contrast, the multi-core architecture is made up of PCs, separate IMs for each core, a shared DM, and an interconnection cross-bar connecting  the cores and the DM. The power vs. performance compromises for a multi-lead ECG signal conditioning application that takes advantage of near threshold computing are evaluated between both designs. According to the findings, the multi-core system uses 10.4% more power for low processing demands (681 kOps/s) and 66% less power for high processing needs (50.1 MOps/s).   &nbsp

Digital-Based Analog Processing in Nanoscale CMOS ICs for IoT Applications

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Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Digital-based analog processing in nanoscale CMOS ICs for IoT applications

The Internet-of-Things (IoT) concept has been opening up a variety of applications, such as urban and environmental monitoring, smart health, surveillance, and home automation. Most of these IoT applications require more and more power/area efficient Complemen tary Metal–Oxide–Semiconductor (CMOS) systems and faster prototypes (lower time-to market), demanding special modifications in the current IoT design system bottleneck: the analog/RF interfaces. Specially after the 2000s, it is evident that there have been significant improvements in CMOS digital circuits when compared to analog building blocks. Digital circuits have been taking advantage of CMOS technology scaling in terms of speed, power consump tion, and cost, while the techniques running behind the analog signal processing are still lagging. To decrease this historical gap, there has been an increasing trend in finding alternative IC design strategies to implement typical analog functions exploiting Digital in-Concept Design Methodologies (DCDM). This idea of re-thinking analog functions in digital terms has shown that Analog ICs blocks can also avail of the feature-size shrinking and energy efficiency of new technologies. This thesis deals with the development of DCDM, demonstrating its compatibility for Ultra-Low-Voltage (ULV) and Power (ULP) IoT applications. This work proves this state ment through the proposing of new digital-based analog blocks, such as an Operational Transconductance Amplifiers (OTAs) and an ac-coupled Bio-signal Amplifier (BioAmp). As an initial contribution, for the first time, a silicon demonstration of an embryonic Digital-Based OTA (DB-OTA) published in 2013 is exhibited. The fabricated DB-OTA test chip occupies a compact area of 1,426 µm2 , operating at supply voltages (VDD) down to 300 mV, consuming only 590 pW while driving a capacitive load of 80pF. With a Total Harmonic Distortion (THD) lower than 5% for a 100mV input signal swing, its measured small-signal figure of merit (FOMS) and large-signal figure of merit (FOML) are 2,101 V −1 and 1,070, respectively. To the best of this thesis author’s knowledge, this measured power is the lowest reported to date in OTA literature, and its figures of merit are the best in sub-500mV OTAs reported to date. As the second step, mainly due to the robustness limitation of previous DB-OTA, a novel calibration-free digital-based topology is proposed, named here as Digital OTA (DIG OTA). A 180-nm DIGOTA test chip is also developed exhibiting an area below the 1000 µm2 wall, 2.4nW power under 150pF load, and a minimum VDD of 0.25 V. The proposed DIGOTA is more digital-like compared with DB-OTA since no pseudo-resistor is needed. As the last contribution, the previously proposed DIGOTA is then used as a building block to demonstrate the operation principle of power-efficient ULV and ultra-low area (ULA) fully-differential, digital-based Operational Transconductance Amplifier (OTA), suitable for microscale biosensing applications (BioDIGOTA) such as extreme low area Body Dust. Measured results in 180nm CMOS confirm that the proposed BioDIGOTA can work with a supply voltage down to 400 mV, consuming only 95 nW. The BioDIGOTA layout occupies only 0.022 mm2 of total silicon area, lowering the area by 3.22X times compared to the current state of the art while keeping reasonable system performance, such as 7.6 Noise Efficiency Factor (NEF) with 1.25 µVRMS input-referred noise over a 10 Hz bandwidth, 1.8% of THD, 62 dB of the common-mode rejection ratio (CMRR) and 55 dB of power supply rejection ratio (PSRR). After reviewing the current DCDM trend and all proposed silicon demonstrations, the thesis concludes that, despite the current analog design strategies involved during the analog block development

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic

Resource Management for Edge Computing in Internet of Things (IoT)

Die große Anzahl an Geräten im Internet der Dinge (IoT) und deren kontinuierliche Datensammlungen führen zu einem rapiden Wachstum der gesammelten Datenmenge. Die Daten komplett mittels zentraler Cloud Server zu verarbeiten ist ineffizient und zum Teil sogar unmöglich oder unnötig. Darum wird die Datenverarbeitung an den Rand des Netzwerks verschoben, was zu den Konzepten des Edge Computings geführt hat. Informationsverarbeitung nahe an der Datenquelle (z.B. auf Gateways und Edge Geräten) reduziert nicht nur die hohe Arbeitslast zentraler Server und Netzwerke, sondern verringer auch die Latenz für Echtzeitanwendungen, da die potentiell unzuverlässige Kommunikation zu Cloud Servern mit ihrer unvorhersehbaren Netzwerklatenz vermieden wird. Aktuelle IoT Architekturen verwenden Gateways, um anwendungsspezifische Verbindungen zu IoT Geräten herzustellen. In typischen Konfigurationen teilen sich mehrere IoT Edge Geräte ein IoT Gateway. Wegen der begrenzten verfügbaren Bandbreite und Rechenkapazität eines IoT Gateways muss die Servicequalität (SQ) der verbundenen IoT Edge Geräte über die Zeit angepasst werden. Nicht nur um die Anforderungen der einzelnen Nutzer der IoT Geräte zu erfüllen, sondern auch um die SQBedürfnisse der anderen IoT Edge Geräte desselben Gateways zu tolerieren. Diese Arbeit untersucht zuerst essentielle Technologien für IoT und existierende Trends. Dabei werden charakteristische Eigenschaften von IoT für die Embedded Domäne, sowie eine umfassende IoT Perspektive für Eingebettete Systeme vorgestellt. Mehrere Anwendungen aus dem Gesundheitsbereich werden untersucht und implementiert, um ein Model für deren Datenverarbeitungssoftware abzuleiten. Dieses Anwendungsmodell hilft bei der Identifikation verschiedener Betriebsmodi. IoT Systeme erwarten von den Edge Geräten, dass sie mehrere Betriebsmodi unterstützen, um sich während des Betriebs an wechselnde Szenarien anpassen zu können. Z.B. Energiesparmodi bei geringen Batteriereserven trotz gleichzeitiger Aufrechterhaltung der kritischen Funktionalität oder einen Modus, um die Servicequalität auf Wunsch des Nutzers zu erhöhen etc. Diese Modi verwenden entweder verschiedene Auslagerungsschemata (z.B. die übertragung von Rohdaten, von partiell bearbeiteten Daten, oder nur des finalen Ergebnisses) oder verschiedene Servicequalitäten. Betriebsmodi unterscheiden sich in ihren Ressourcenanforderungen sowohl auf dem Gerät (z.B. Energieverbrauch), wie auch auf dem Gateway (z.B. Kommunikationsbandbreite, Rechenleistung, Speicher etc.). Die Auswahl des besten Betriebsmodus für Edge Geräte ist eine Herausforderung in Anbetracht der begrenzten Ressourcen am Rand des Netzwerks (z.B. Bandbreite und Rechenleistung des gemeinsamen Gateways), diverser Randbedingungen der IoT Edge Geräte (z.B. Batterielaufzeit, Servicequalität etc.) und der Laufzeitvariabilität am Rand der IoT Infrastruktur. In dieser Arbeit werden schnelle und effiziente Auswahltechniken für Betriebsmodi entwickelt und präsentiert. Wenn sich IoT Geräte in der Reichweite mehrerer Gateways befinden, ist die Verwaltung der gemeinsamen Ressourcen und die Auswahl der Betriebsmodi für die IoT Geräte sogar noch komplexer. In dieser Arbeit wird ein verteilter handelsorientierter Geräteverwaltungsmechanismus für IoT Systeme mit mehreren Gateways präsentiert. Dieser Mechanismus zielt auf das kombinierte Problem des Bindens (d.h. ein Gateway für jedes IoT Gerät bestimmen) und der Allokation (d.h. die zugewiesenen Ressourcen für jedes Gerät bestimmen) ab. Beginnend mit einer initialen Konfiguration verhandeln und kommunizieren die Gateways miteinander und migrieren IoT Geräte zwischen den Gateways, wenn es den Nutzen für das Gesamtsystem erhöht. In dieser Arbeit werden auch anwendungsspezifische Optimierungen für IoT Geräte vorgestellt. Drei Anwendungen für den Gesundheitsbereich wurden realisiert und für tragbare IoT Geräte untersucht. Es wird auch eine neuartige Kompressionsmethode vorgestellt, die speziell für IoT Anwendungen geeignet ist, die Bio-Signale für Gesundheitsüberwachungen verarbeiten. Diese Technik reduziert die zu übertragende Datenmenge des IoT Gerätes, wodurch die Ressourcenauslastung auf dem Gerät und dem gemeinsamen Gateway reduziert wird. Um die vorgeschlagenen Techniken und Mechanismen zu evaluieren, wurden einige Anwendungen auf IoT Plattformen untersucht, um ihre Parameter, wie die Ausführungszeit und Ressourcennutzung, zu bestimmen. Diese Parameter wurden dann in einem Rahmenwerk verwendet, welches das IoT Netzwerk modelliert, die Interaktion zwischen Geräten und Gateway erfasst und den Kommunikationsoverhead sowie die erreichte Batterielebenszeit und Servicequalität der Geräte misst. Die Algorithmen zur Auswahl der Betriebsmodi wurden zusätzlich auf IoT Plattformen implementiert, um ihre Overheads bzgl. Ausführungszeit und Speicherverbrauch zu messen

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Multimodal Wearable Sensors for Human-Machine Interfaces

Certain areas of the body, such as the hands, eyes and organs of speech production, provide high-bandwidth information channels from the conscious mind to the outside world. The objective of this research was to develop an innovative wearable sensor device that records signals from these areas more conveniently than has previously been possible, so that they can be harnessed for communication. A novel bioelectrical and biomechanical sensing device, the wearable endogenous biosignal sensor (WEBS), was developed and tested in various communication and clinical measurement applications. One ground-breaking feature of the WEBS system is that it digitises biopotentials almost at the point of measurement. Its electrode connects directly to a high-resolution analog-to-digital converter. A second major advance is that, unlike previous active biopotential electrodes, the WEBS electrode connects to a shared data bus, allowing a large or small number of them to work together with relatively few physical interconnections. Another unique feature is its ability to switch dynamically between recording and signal source modes. An accelerometer within the device captures real-time information about its physical movement, not only facilitating the measurement of biomechanical signals of interest, but also allowing motion artefacts in the bioelectrical signal to be detected. Each of these innovative features has potentially far-reaching implications in biopotential measurement, both in clinical recording and in other applications. Weighing under 0.45 g and being remarkably low-cost, the WEBS is ideally suited for integration into disposable electrodes. Several such devices can be combined to form an inexpensive digital body sensor network, with shorter set-up time than conventional equipment, more flexible topology, and fewer physical interconnections. One phase of this study evaluated areas of the body as communication channels. The throat was selected for detailed study since it yields a range of voluntarily controllable signals, including laryngeal vibrations and gross movements associated with vocal tract articulation. A WEBS device recorded these signals and several novel methods of human-to-machine communication were demonstrated. To evaluate the performance of the WEBS system, recordings were validated against a high-end biopotential recording system for a number of biopotential signal types. To demonstrate an application for use by a clinician, the WEBS system was used to record 12‑lead electrocardiogram with augmented mechanical movement information

Channel modeling and characterization for VLC-based medical body sensor networks: trends and challenges

Optical Wireless Communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, Infrared (IR), and Ultraviolet (UV) bands. In this paper, we focus on indoor Visible Light Communication (VLC)-based Medical Body Sensor Networks (MBSNs) which allow the Light Emitting Diodes (LEDs) to communicate between on-body sensors/subdermal implants and on-body central hubs/monitoring devices while also serving as a luminaire. Since the Quality-of-Service (QoS) of the communication systems depends heavily on realistic channel modeling and characterization, this paper aims at presenting an up-to-date survey of works on channel modeling activities for MBSNs. The first part reviews existing IR-based MBSNs channel models based on which VLC channel models are derived. The second part of this review provides details on existing VLC-based MBSNs channel models according to the mobility of the MBSNs on the patient’s body. We also present a realistic channel modeling approach called site-specific ray tracing that considers the skin tissue for the MBSNs channel modeling for realistic hospital scenarios.Scientific Research Projects (BAP) (Grant Number: 20A204)Publisher's Versio