2,543 research outputs found
The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems
The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed
Development of visible-light-driven photocatalysts for the degradation of organic pollutants and the disinfection of microorganisms
In this research, several visible-light driven photocatalysts were developed and their photocatalytic activities were evaluated in the removal of organic pollutants. Wastewater containing pathogen carriers such as total coliforms, and E. coli was tested for disinfection using the synthesized visible-light photocatalysts. Graphitic carbon nitride (GCN or g-C3N4), a visible-light driven photocatalyst, was synthesized from different precursors. Also, different composites of GCN such GCN/Ag2CrO4, and GCN/ZnO-Cu were synthesized. The purpose of these GCN composites is to enhance the photocatalytic activity of the GCN. Several characterization techniques were used to understand the physicochemical properties of the photocatalysts. The initial photocatalytic experiments, detailed in Chapter 3, were on degrading 4-CP under a royal blue LED (450 nm) using precursor-derived GCNs and GCN composites. The results show that the GCN/0.3Ag2CrO4 performed well with over 95% degradation of 4-CP. The second set of photocatalytic experiments, detailed in Chapter 4, were on investigating the degradation of 2,4-D and MCPP, BSA protein, SARS-CoV-2 (Covid-19) spike protein, cATP, and total coliforms/E. coli using the best performing GCN/0.3Ag2CrO4 in the first photocatalytic experiments and royal blue LED. Over 85% of 2,4-D and MCPP were simultaneously degraded, 77.5% of Covid-19 spike protein was achieved, and over one log reduction of cATP, total coliforms/E. coli was achieved in wastewater treatment. In Chapter 5 (third set of experiments), new sets of photocatalysts were synthesized. GCN/0.1ZnO-Cu3% performed best with over 65% of 4-CP degradation under royal blue LED. A complete 5.5 log reduction of coliforms-containing wastewater primary influent was achieved with the same photocatalyst. In Chapter 6, the best-performing GCN/ZnO-Cu nanocomposite in observed in chapter 5 was coated on a polyvinyl chloride (PVC) substrate and the performance was evaluated under a 5000K LED (400 – 700 nm). The result shows a 2-log reduction of the coliform-containing wastewater treatment on the self-disinfecting coated surface. To the best of our knowledge, this is the first research to investigate the comprehensive use and practical application of self-disinfecting coated surfaces under commercial and industrial light (5000K LED) irradiation. All our results demonstrate that compositing GCN with metals can degrade pollutants and disinfect wastewater under visible light irradiation
GaN Transistors’ Radiated Switching Noise Source Evidenced by Hall Sensor Experiments Toward Integration
Wide bandgap Gallium Nitride (GaN) technology promises to deliver the next generation of power transistors capable of high energy density and compact design integration however, without active monitoring high failing rates are recorded due to its instability to design parameter variations. Moreover, the electromagnetic (EM) radiofrequency (RF) emissions due to GaN power switching require extra design resources. Considering the extensive research area dedicated to galvanic isolated magnetic sensors for GaN wafer monolithic integration with usage in power monitoring, this study investigates the conditions that a Hall sensor is required to meet when operating in close proximity of a GaN transistor. Through considerable experimental testing, it was determined that the sensor requires a magnetic field starting from ±1 mT when interfaced with a microcontroller. Additionally, since the GaN transistor's EM RF switching noise was one of the most monitored parameters during the experiments, it was discovered that it is proportional to the transistor's current transfer area whereas its magnitude is due to electrical current required by the load. As a result of these findings, the EM radiated switching noise may apply to all electrical switches and provide a significant advantage when designing for EM compatibility (EMC)
Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces
Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet.
Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität.
Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht.
Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application.
The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors.
The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies.
Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability
Doping of Organic Semiconductors: Effects of Crosslinking and Dopant Substituents
Doping is the process of addition of dopants to host semiconductors to improve their conductivity
and charge transport behavior. Organic solids are held together by weak van der Waals interactions
between the molecules and Coulombic attractions between the charged species. Because of these
weaker interactions in organic materials, the molecules themselves have higher mobilities within
the host material, and therefore, have a higher tendency to move. In most of the devices, the
diffusion of the dopants in device stacks is detrimental and therefore, minimizing dopant diffusion
within device interlayers is a very important topic to be consider. Considering the widespread
usage of DMBI-H derivatives for doping of organic semiconductors, this work will focus on two
aspects of doping; investigation of different approaches to address the diffusion of DMBI-H
derivatives and studying the effect of dopant substituents on charge transport behavior.
The first and second chapters of this thesis, will focus on crosslinking as new approaches for
minimizing the dopant diffusion in the solid state. Chapter 2 will discuss electrostatic crosslinking
in which the restriction of dopant ion movement by forming multiple electrostatic sites between
the multiply charged ions and ionized host segments. Chapter 3 will discuss chemical crosslinking
and chemical bond formation to decrease the diffusion of dopant and the corresponding dopant
ions. Chapter 4 will focus on a study in which the effect of a polar side chain on DMBI-H for
doping of a donor-acceptor polymer. The final chapter summarizes the findings of the thesis, puts
them in a broader perspective, and suggests future directionsPh.D
Development and testing of an FPGA-controlled switched-integrator current amplifier for use in scanning tunnelling microscopy
The scanning tunnelling microscope (STM) is a very powerful analytic tool capable of achieving atomic resolution. Unfortunately, the STM is restricted to samples that are sufficiently conductive to allow adequate tunneling current for feedback control. The amplifier used to measure the tunneling current is the critical limiting component. If the amplifier could be made more sensitive, the STM could be operated at lower tunneling currents allowing lower conductivity samples to be studied. Most amplifiers used in STM employ a resistor feedback design, which become unstable at high gain necessitating a tradeoff between gain and bandwidth. One way to circumvent that stability problem is to use a capacitor feedback design (switched
integrator), which does not exhibit the same stability problem. This comes at the expense of added complexity because the output is the integral of the current and needs to be periodically reset. In this project, a switched-integrator current amplifier is constructed and explored. It consisted of an analog switched integrator controlled by a field-programmable-gate-array (FPGA) with a 16-bit analog-to-digital converter and an 18-bit digital-to-analog converter. A viable prototype was created which allowed for the exploration of the gain, phase, and time delay of such systems. This exploration helped further characterize the important design considerations and trade-offs necessary for such a system. A design sequence is proposed that allows for optimal planning based on the desired tunneling current and system bandwidth
Algorithms for light applications: from theoretical simulations to prototyping
[eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date.
Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers.
This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices.[cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissiĂł espectral combinada, a mĂ©s d’aplicar tècniques innovadores de reconstrucciĂł espectral a un sensor colorimètric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulaciĂł espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de parĂ metres colorimètrics i no-visuals en fonts de llum multicanal. S’ha fet Ăşs del supercomputador MareNostrum i s’han trobat noves relacions entre parĂ metres colorimètrics i no visuals en conjunts de LEDs blancs comercials a travĂ©s de l’anĂ lisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimètric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucciĂł espectral. S’han generat una gran quantitat de dades que han permès realitzar simulacions i estudis estadĂstics sobre l’error comès en el procĂ©s de reconstrucciĂł espectral utilitzant diferents tècniques. Aquesta millora ha implicat un augment de la resoluciĂł espectral mesurada pel sensor, permetent obtenir una millor precisiĂł en el cĂ lcul de parĂ metres colorimètrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimètric amb l’objectiu de demostrar experimentalment el marc teòric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teòrics. Els resultats obtinguts s’han validat a travĂ©s de l’aplicaciĂł de diferents estĂ ndards de la indĂşstria o a travĂ©s de la comparativa amb dispositius comercials calibrats
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
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