The hardware security issues are emerging in crypto-algorithms of embedded portable Internet-of-Things-Devices (IoTD). The communication protocols/standards including MQTT (Message Queuing Telemetry Transport) are enf...
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The hardware security issues are emerging in crypto-algorithms of embedded portable Internet-of-Things-Devices (IoTD). The communication protocols/standards including MQTT (Message Queuing Telemetry Transport) are enforcing additional cares in device-to-system design perspectives. Due to computation-capacity limitations (CCLs) in battery-operated IoTD, heavy-duty crypto-algorithms are prohibited. This results in compromised hardware using lightweight algorithms. In this study, a new implementation schema for hierarchically-connected IoTD for indoor applications is proposed. This schema allows the IoT network to utilise strong-crypto-algorithms (i.e. RSA) instead of lightweight algorithms (i.e. attribute-based encryption (ABE)). Therefore, without increasing the consumption power or complexity, the security in the IoT network increases. This method brings about a new low CCL RSA with two-folded power-aware implementation. Furthermore, without complexity overhead, the proposed method is more secure than the conventional implementation due to the inherent countermeasure against the side-channel attacks. The presented schema is implemented on a target IoT network, utilising in XC7A100T-FPGA as IoT nodes. Furthermore, both the conventional and the proposed RSA-2048 have been implemented in Spartan6-LX75 on a SAKURA-GW board. The results show that the proposed method has reduced the RSA execution time and consumption power of IoTD at about 50 and 60%, respectively. The most noticeable drawback of the current implementation is an overhead in the range of 30-53% on block-random access memory (RAM) usage.
A demonstrator has been developed to illustrate the performance of a lightweight fingerprint recognition algorithm based on the feature QFingerMap16, which is extracted from a window of the directional image centered ...
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ISBN:
(纸本)9781538635346
A demonstrator has been developed to illustrate the performance of a lightweight fingerprint recognition algorithm based on the feature QFingerMap16, which is extracted from a window of the directional image centered at the convex core of the fingerprint. The algorithm has been implemented into a low-power ARM Cortex-M3 microcontroller included in a Texas Instruments LaunchPad CC2650 evaluation kit. It has been also implemented in a Raspberry Pi 2 so as to show the results obtained at the successive steps of the recognition process with the aid of a Graphical User Interface (GUI). The algorithm offers a good tradeoff between power consumption and recognition accuracy, being suitable for authentication on wearables.
An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we ...
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An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we present the design and implementation of a robust and computationally lightweight algorithm for a real-time vision system, capable of detecting and tracking vehicle taillights, recognizing common alert signals using a vehicle-mounted embedded smart camera, and counting the cars passing on both sides of the vehicle. The system is low-power and processes scenes entirely on the microprocessor of an embedded smart camera. In contrast to most existing work that addresses either daytime or nighttime detection, the presented system provides the ability to track vehicle taillights and detect alert signals regardless of lighting conditions. The mobile vision system has been tested in actual traffic scenes and the results obtained demonstrate the performance and the lightweight nature of the algorithm.
Considering the substantial population affected by some form of color-vision deficiency (CVD), reliable traffic control signal head light detection is an important problem for driver-assistance systems. While a large ...
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Considering the substantial population affected by some form of color-vision deficiency (CVD), reliable traffic control signal head light detection is an important problem for driver-assistance systems. While a large number of technologies can be used to localize traffic lights, without drastic changes in infrastructure, only visual information can be used in identifying the status of the light. In addition, traffic light detection is not currently integrated into any driver-assistance systems, making driving for individuals with CVD (where permitted) dangerous to other drivers, pedestrians, and themselves. This paper presents a robust, traffic-standards-based, and computationally efficient method for detecting the status of the traffic lights without relying on Global Positioning System, lidar, radar information, or prior (map-based) knowledge. To the extent of our knowledge, this is the first work to use official Institute of Transportation Engineers (U.S.) and British Standards Institute (European Union) standards for defining traffic light colors, as well as integrating a number of fail-safe mechanisms designed to prevent erroneous detection. The algorithm can be easily ported over to an embedded smart camera platform and used as a windshield-mounted driver-assistance device by individuals with CVD. The system can accurately identify the status of the light at 400 ft away from the intersection, reliably detecting solid, faulty, arrow, and high-visibility signal lights. Over 50 h of video (over 2000 intersections) were tested with the system, containing intersections with one to four traffic lights, governing different lanes of traffic, with 97.5% accuracy of solid light detection.
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