1,088 research outputs found

    A high-flux cold atom source based on a nano-structured atom chip

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    Modern physics is challenged by existential questions about the most fundamental interactions of matter. While three of the four known fundamental forces are modeled in the grand unified theory [1], gravity seems to be incompatible in its current formulation. Many physicists search to unify them, but often the invented models violate well-tested assumptions such as the Einstein Equivalence Principle, a cornerstone of General Relativity. Despite macroscopic tests of this principle have already been carried out to high precision [2–4], quantum tests exploiting matter-wave interferometry [5–7] may provide complementary information [8] with even higher precision [9–11]. These yield their ultimate performance with Bose-Einstein condensates (BECs) over long evolution times as conventionally achieved by free-fall in space [12]. As such, a new generation of high performance BEC sources is required with strict budgets on size, weight and power demands. Efforts to miniaturize these sources have been pursued with promising results using atom chips [13–15], but further miniaturization of these setups is necessary. In an attempt to simplify the usage of atom chips, the following thesis describes the development of a nano-structured atom chip that allows for single-beam magneto-optical trapping. The chip is implemented in a dedicated atom chip test facility that has been planned, built and characterized in the scope of this thesis. The facility features a state-of-the-art master oscillator power amplifier laser system, compact control electronics [13,15–17] and a high-flux 2D+-MOT as an atomic source. Despite the simplified setup, magneto-optical trapping of 1.1 × 10^9 Rubidium atoms was achieved within 1 s which is comparable to other atom chip setups and well above previous achievements with grating MOTs [18–23]. Illuminating the grating with a beam profile from a custom-built top-hat beam expander was instrumental to achieve balanced laser cooling in a large volume above the grating. This allowed to cool 4.7 × 10^8 atoms to 13 µK and transfer 2.4 × 10^8 atoms into a large-volume Ioffe-Pritchard type magnetic chip trap, demonstrating the required mode-matching between the laser cooled atoms and the magnetic trap. The trapped atoms were then used to characterize the magnetic field environment of the test facility using radio frequency spectroscopy gauging the surrounding magnetic bias coils. These results demonstrate the feasibility of using a nano structured atom chip to build a single-beam BEC source which could become the foundation of future high-performance quantum sensors on ground and in space

    BDS GNSS for Earth Observation

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    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Optimising multimodal fusion for biometric identification systems

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    Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023

    Parameter identification in networks of dynamical systems

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    Mathematical models of real systems allow to simulate their behavior in conditions that are not easily or affordably reproducible in real life. Defining accurate models, however, is far from trivial and there is no one-size-fits-all solution. This thesis focuses on parameter identification in models of networks of dynamical systems, considering three case studies that fall under this umbrella: two of them are related to neural networks and one to power grids. The first case study is concerned with central pattern generators, i.e. small neural networks involved in animal locomotion. In this case, a design strategy for optimal tuning of biologically-plausible model parameters is developed, resulting in network models able to reproduce key characteristics of animal locomotion. The second case study is in the context of brain networks. In this case, a method to derive the weights of the connections between brain areas is proposed, utilizing both imaging data and nonlinear dynamics principles. The third and last case study deals with a method for the estimation of the inertia constant, a key parameter in determining the frequency stability in power grids. In this case, the method is customized to different challenging scenarios involving renewable energy sources, resulting in accurate estimations of this parameter

    Magnetic Material Modelling of Electrical Machines

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    The need for electromechanical energy conversion that takes place in electric motors, generators, and actuators is an important aspect associated with current development. The efficiency and effectiveness of the conversion process depends on both the design of the devices and the materials used in those devices. In this context, this book addresses important aspects of electrical machines, namely their materials, design, and optimization. It is essential for the design process of electrical machines to be carried out through extensive numerical field computations. Thus, the reprint also focuses on the accuracy of these computations, as well as the quality of the material models that are adopted. Another aspect of interest is the modeling of properties such as hysteresis, alternating and rotating losses and demagnetization. In addition, the characterization of materials and their dependence on mechanical quantities such as stresses and temperature are also considered. The reprint also addresses another aspect that needs to be considered for the development of the optimal global system in some applications, which is the case of drives that are associated with electrical machines

    Dynamic Nanophotonic Structures Leveraging Chalcogenide Phase-Change Materials

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    Chip-scale nanophotonic devices have the potential to enable next-generation imaging, computing, communication, and engineered quantum systems with very stringent performance requirements on size, power, integrability, stability, and bandwidth. The emergence of meta-optic devices with deep subwavelength features has enabled the formation of ultra-thin flat optical structures to replace bulky conventional counterparts in free-space applications. Nevertheless, progress in meta-optics has been slowed due to the passive nature of existing devices and the urgent need for a reliable, fast, low-power, and robust reconfiguration mechanism. In this research, I devised a new material and device platform to resolve this challenge. Through detailed theoretical design, nanofabrication, and experimental demonstration, I demonstrated the unique features of my proposed platform as an essential building block of truly scalable adaptive flat optics for the active manipulation of optical wavefronts. One of the key attributes of this research is the integration of CMOS-compatible materials for the fabrication of passive devices with phase-change materials that provide the largest known modulation of the index of refraction upon stimulation with an optical or electrical signal. A unique selection of phase-change materials for operation in the near-infrared and visible wavelengths has been made, followed by developing the optimum deposition and fabrication processes for the realization of nanophotonics devices that integrate these functional materials with semiconductor and plasmonic materials. A major breakthrough in this process was the design and realization of integrated electrical stimulation circuitry with far better performance compared to existing solutions. Using this platform, I experimentally demonstrated the first electrically tunable meta-optic structure for fast optical switching with a high contrast ratio and dynamic wavefront scanning with a large steering angle. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. In an independent work, I demonstrated, for the first time, a nonvolatile meta-optic structure for high-resolution, wide-gamut, and high-contrast microdisplays with added polarization controllability and the possibility of implementation on a flexible substrate. Further features of this metaphotonic display include: 1) full addressability at the microscale pixel via fast electrical pulses; 2) super-resolution pixels with controllable brightness and contrast; and 3) a wide range of colors with high saturation and purity. Lastly, for the first time, I realized a hybrid photonic-plasmonic meta-optic platform with active control over the spatial, spectral, and temporal properties of an optical wavefront. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. These demonstrations are now being pursued in different directions for novel systems for imaging, sensing, computing, and quantum applications, just to name a few.Ph.D

    Accelerating Audio Data Analysis with In-Network Computing

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    Digital transformation will experience massive connections and massive data handling. This will imply a growing demand for computing in communication networks due to network softwarization. Moreover, digital transformation will host very sensitive verticals, requiring high end-to-end reliability and low latency. Accordingly, the emerging concept “in-network computing” has been arising. This means integrating the network communications with computing and also performing computations on the transport path of the network. This can be used to deliver actionable information directly to end users instead of raw data. However, this change of paradigm to in-network computing raises disruptive challenges to the current communication networks. In-network computing (i) expects the network to host general-purpose softwarized network functions and (ii) encourages the packet payload to be modified. Yet, today’s networks are designed to focus on packet forwarding functions, and packet payloads should not be touched in the forwarding path, under the current end-to-end transport mechanisms. This dissertation presents fullstack in-network computing solutions, jointly designed from network and computing perspectives to accelerate data analysis applications, specifically for acoustic data analysis. In the computing domain, two design paradigms of computational logic, namely progressive computing and traffic filtering, are proposed in this dissertation for data reconstruction and feature extraction tasks. Two widely used practical use cases, Blind Source Separation (BSS) and anomaly detection, are selected to demonstrate the design of computing modules for data reconstruction and feature extraction tasks in the in-network computing scheme, respectively. Following these two design paradigms of progressive computing and traffic filtering, this dissertation designs two computing modules: progressive ICA (pICA) and You only hear once (Yoho) for BSS and anomaly detection, respectively. These lightweight computing modules can cooperatively perform computational tasks along the forwarding path. In this way, computational virtual functions can be introduced into the network, addressing the first challenge mentioned above, namely that the network should be able to host general-purpose softwarized network functions. In this dissertation, quantitative simulations have shown that the computing time of pICA and Yoho in in-network computing scenarios is significantly reduced, since pICA and Yoho are performed, simultaneously with the data forwarding. At the same time, pICA guarantees the same computing accuracy, and Yoho’s computing accuracy is improved. Furthermore, this dissertation proposes a stateful transport module in the network domain to support in-network computing under the end-to-end transport architecture. The stateful transport module extends the IP packet header, so that network packets carry message-related metadata (message-based packaging). Additionally, the forwarding layer of the network device is optimized to be able to process the packet payload based on the computational state (state-based transport component). The second challenge posed by in-network computing has been tackled by supporting the modification of packet payloads. The two computational modules mentioned above and the stateful transport module form the designed in-network computing solutions. By merging pICA and Yoho with the stateful transport module, respectively, two emulation systems, i.e., in-network pICA and in-network Yoho, have been implemented in the Communication Networks Emulator (ComNetsEmu). Through quantitative emulations, the experimental results showed that in-network pICA accelerates the overall service time of BSS by up to 32.18%. On the other hand, using in-network Yoho accelerates the overall service time of anomaly detection by a maximum of 30.51%. These are promising results for the design and actual realization of future communication networks
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