8,420 research outputs found

    The Mars Science Laboratory record of optical depth measurements via solar imaging

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    Acknowledgments We are grateful to the teams that developed, landed, and operated Curiosity on Mars, allowing for the present study. The research was conducted partly at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). MTL was supported via sub-contract 18-1187 from Malin Space Science Systems, Inc. SDG was supported by the MSL Participating Scientist program. JMB was supported by MSL Participating Scientist Grant 80NSSC22K0657. AV-R was supported by the Comunidad de Madrid Project S2018/NMT-4291 (TEC2SPACE-CM). M-PZ was supported by grant PID2019-104205GB-C21 funded by MCIN/AEI/10.13039/501100011033. JM-T was supported by UK Space Agency projects ST/W00190X/1 and ST/V00610X/1.Peer reviewedPostprin

    Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications

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    This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration

    An investigation of entorhinal spatial representations in self-localisation behaviours

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    Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space. Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour. Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure. Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Securing Additive Manufacturing Systems from Cyber and Intellectual Property Attacks

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    Additive Manufacturing (AM), also known as 3D printing, refers to a collection of manufacturing processes where materials are joined together layer by layer to make objects directly from 3D models. Due to many advantages of AM, such as rapid prototyping, massive customization, material saving, and flexibility of designs, there is a trend for AM to replace traditional manufacturing processes. However, AM highly relies on computers to work. As AM systems are gaining popularity in many critical industry sectors, there is an increased risk of cyberattacks on AM systems. To protect AM systems from cyberattacks that aim to sabotage the AM systems, Intrusion Detection Systems (IDSs) can be used. In recent years, researchers proposed a series of IDSs that work by leveraging side-channel signals. A side-channel signal is typically a physical signal that is correlated with the state of the AM system, such as the acoustic sound or the electromagnetic wave emitted by a 3D printer in a printing process. Because of the correlation between a side-channel signal and the state of a 3D printer, it is possible to perform intrusion detection by analyzing the side-channel signal. In fact, most existing IDSs leveraging side-channel signals in AM systems function by comparing an observed side-channel signal against a reference side-channel signal. However, we found that these IDSs are not practical due to a lack of synchronization. Many IDSs in the literature do not contain details on how to align two (or more) side-channel signals at their starting moments and their stopping moments. In addition, we found that there is time noise in AM processes. When the same G-code file is executed on the same 3D printer multiple times, the printing processes will have slightly different timing. Because of time noise, a direct comparison between two signals point by point or window by window will not make sense. To overcome this problem, we propose to use dynamic synchronization to find corresponding points between two signals in real time. To demonstrate the necessity of dynamic synchronization, we performed a total of 302 benign printing processes and a total of 200 malicious printing processes with two printers. Our experiment results show that existing IDSs leveraging side-channel signals in AM systems can only achieve an accuracy from 0.50 to 0.88, whereas our IDS with dynamic synchronization can reach an accuracy of 0.99. Other than cyberattacks to sabotage AM systems, there are also cyberattacks to steal intellectual property in AM systems. For example, there are acoustic side-channel attacks on AM systems which can recover the printing path by analyzing the acoustic sound by a printer in a printing process. However, we found that the acoustic side-channel attack is hard to perform due to challenges such as integration drift and non-unique solution. In this thesis, we explore the optical side-channel attack, which is much easier to perform than the acoustic side-channel attack. The optical side-channel signal is basically the video of a printing process. We use a modified deep neural network, ResNet50, to recognize the coordinates of the printhead in each frame in the video. To defend against the optical side-channel attack, we propose the optical noise injection method. We use an optical projector to artificially inject crafted optical noise onto the printing area in an attempt to confuse the attacker and make it harder to recover the printing path. We found that existing noise generation algorithms, such as replaying, random blobs, white noise, and full power, can effortlessly defeat a naive attacker who is not aware of the existence of the injected noise. However, an advanced attacker who knows about the injected noise and incorporates images with injected noise in the training dataset can defeat all of the existing noise generation algorithms. To defend against such an advanced attacker, we propose three novel noise generation algorithms: channel uniformization, state uniformization, and state randomization. Our experiment results show that noise generated via state randomization can successfully defeat the advanced attacker.Ph.D

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    The Forward Physics Facility at the High-Luminosity LHC

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    WearPut : Designing Dexterous Wearable Input based on the Characteristics of Human Finger Motions

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    Department of Biomedical Engineering (Human Factors Engineering)Powerful microchips for computing and networking allow a wide range of wearable devices to be miniaturized with high fidelity and availability. In particular, the commercially successful smartwatches placed on the wrist drive market growth by sharing the role of smartphones and health management. The emerging Head Mounted Displays (HMDs) for Augmented Reality (AR) and Virtual Reality (VR) also impact various application areas in video games, education, simulation, and productivity tools. However, these powerful wearables have challenges in interaction with the inevitably limited space for input and output due to the specialized form factors for fitting the body parts. To complement the constrained interaction experience, many wearable devices still rely on other large form factor devices (e.g., smartphones or hand-held controllers). Despite their usefulness, the additional devices for interaction can constrain the viability of wearable devices in many usage scenarios by tethering users' hands to the physical devices. This thesis argues that developing novel Human-Computer interaction techniques for the specialized wearable form factors is vital for wearables to be reliable standalone products. This thesis seeks to address the issue of constrained interaction experience with novel interaction techniques by exploring finger motions during input for the specialized form factors of wearable devices. The several characteristics of the finger input motions are promising to enable increases in the expressiveness of input on the physically limited input space of wearable devices. First, the input techniques with fingers are prevalent on many large form factor devices (e.g., touchscreen or physical keyboard) due to fast and accurate performance and high familiarity. Second, many commercial wearable products provide built-in sensors (e.g., touchscreen or hand tracking system) to detect finger motions. This enables the implementation of novel interaction systems without any additional sensors or devices. Third, the specialized form factors of wearable devices can create unique input contexts while the fingers approach their locations, shapes, and components. Finally, the dexterity of fingers with a distinctive appearance, high degrees of freedom, and high sensitivity of joint angle perception have the potential to widen the range of input available with various movement features on the surface and in the air. Accordingly, the general claim of this thesis is that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. This thesis demonstrates the general claim by providing evidence in various wearable scenarios with smartwatches and HMDs. First, this thesis explored the comfort range of static and dynamic touch input with angles on the touchscreen of smartwatches. The results showed the specific comfort ranges on variations in fingers, finger regions, and poses due to the unique input context that the touching hand approaches a small and fixed touchscreen with a limited range of angles. Then, finger region-aware systems that recognize the flat and side of the finger were constructed based on the contact areas on the touchscreen to enhance the expressiveness of angle-based touch input. In the second scenario, this thesis revealed distinctive touch profiles of different fingers caused by the unique input context for the touchscreen of smartwatches. The results led to the implementation of finger identification systems for distinguishing two or three fingers. Two virtual keyboards with 12 and 16 keys showed the feasibility of touch-based finger identification that enables increases in the expressiveness of touch input techniques. In addition, this thesis supports the general claim with a range of wearable scenarios by exploring the finger input motions in the air. In the third scenario, this thesis investigated the motions of in-air finger stroking during unconstrained in-air typing for HMDs. The results of the observation study revealed details of in-air finger motions during fast sequential input, such as strategies, kinematics, correlated movements, inter-fingerstroke relationship, and individual in-air keys. The in-depth analysis led to a practical guideline for developing robust in-air typing systems with finger stroking. Lastly, this thesis examined the viable locations of in-air thumb touch input to the virtual targets above the palm. It was confirmed that fast and accurate sequential thumb touch can be achieved at a total of 8 key locations with the built-in hand tracking system in a commercial HMD. Final typing studies with a novel in-air thumb typing system verified increases in the expressiveness of virtual target selection on HMDs. This thesis argues that the objective and subjective results and novel interaction techniques in various wearable scenarios support the general claim that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. Finally, this thesis concludes with thesis contributions, design considerations, and the scope of future research works, for future researchers and developers to implement robust finger-based interaction systems on various types of wearable devices.ope

    Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

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    Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived through dedicated preprocessing, or integrated into end-to-end learning. Independent of their origin, for the vast majority of contemporary HAR, those representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches have been explored - primarily motivated by the desire to minimize computational requirements, but also with a view on applications beyond mere recognition, such as, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting representations with detrimental effects on downstream tasks. Times have changed and in this paper we propose a return to discretized representations. We adopt and apply recent advancements in Vector Quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, resulting in recognition performance that is generally on par with their contemporary, continuous counterparts - sometimes surpassing them. Therefore, this work presents a proof-of-concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR

    Natural stimuli for mice: environment statistics and behavioral responses

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