摘要 :
Building a high-assurance, secure operating system for memory constrained systems, such as smart cards, introduces many challenges. The increasing power of smart cards has made their use feasible in applications such as electronic...
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Building a high-assurance, secure operating system for memory constrained systems, such as smart cards, introduces many challenges. The increasing power of smart cards has made their use feasible in applications such as electronic passports, military and public sector identification cards, and cell-phone based financial and entertainment applications. Such applications require a secure environment, which can only be provided with sufficient hardware and a secure operating system. We argue that smart cards pose additional security challenges when compared to traditional computer platforms. We discuss our design for a secure smart card operating system, named Caernarvon, and show that it addresses these challenges, which include secure application download, protection of cryptographic functions from malicious applications, resolution of covert channels, and assurance of both security and data integrity in the face of arbitrary power losses.
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摘要 :
Building a high-assurance, secure operating system for memory constrained systems, such as smart cards, introduces many challenges. The increasing power of smart cards has made their use feasible in applications such as electronic...
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Building a high-assurance, secure operating system for memory constrained systems, such as smart cards, introduces many challenges. The increasing power of smart cards has made their use feasible in applications such as electronic passports, military and public sector identification cards, and cell-phone based financial and entertainment applications. Such applications require a secure environment, which can only be provided with sufficient hardware and a secure operating system. We argue that smart cards pose additional security challenges when compared to traditional computer platforms. We discuss our design for a secure smart card operating system, named Caernarvon, and show that it addresses these challenges, which include secure application download, protection of cryptographic functions from malicious applications, resolution of covert channels, and assurance of both security and data integrity in the face of arbitrary power losses.
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摘要 :
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transf...
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In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn H?rnligrat at 3500 m a.s.l. since 08/2018.
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摘要 :
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transf...
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In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.
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摘要 :
We describe the design of an acoustic emission (AE) measurement assembly for reliable acquisition of a multi-year time-series in steep alpine rock-walls. The motivation for collection of these data is to enhance understanding of f...
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We describe the design of an acoustic emission (AE) measurement assembly for reliable acquisition of a multi-year time-series in steep alpine rock-walls. The motivation for collection of these data is to enhance understanding of freezing-induced rock damage. Because measurements in natural rock slopes are challenging, this study investigates technical options suitable to capture AE signals from differing depths while incurring minimal signal loss between the rock and the sensor. We first outline the requirements for the measurement assembly and present two generic solutions to be evaluated and refined. We then present candidate materials for building components of the assembly and experimentally estimate their attenuation coefficients and signal loss at the rock-sensor contact. Based on these results, we present the final design chosen for the measurement assembly and briefly report initial experiences from a field deployment site at 3500 m a.s.l. at Jungfraujoch, Switzerland.
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摘要 :
We describe the design of an acoustic emission (AE) measurement assembly for reliable acquisition of a multi-year time-series in steep alpine rock-walls. The motivation for collection of these data is to enhance understanding of f...
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We describe the design of an acoustic emission (AE) measurement assembly for reliable acquisition of a multi-year time-series in steep alpine rock-walls. The motivation for collection of these data is to enhance understanding of freezing-induced rock damage. Because measurements in natural rock slopes are challenging, this study investigates technical options suitable to capture AE signals from differing depths while incurring minimal signal loss between the rock and the sensor. We first outline the requirements for the measurement assembly and present two generic solutions to be evaluated and refined. We then present candidate materials for building components of the assembly and experimentally estimate their attenuation coefficients and signal loss at the rock-sensor contact. Based on these results, we present the final design chosen for the measurement assembly and briefly report initial experiences from a field deployment site at 3500 m a.s.l. at Jungfraujoch, Switzerland.
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