To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness, i.e., vulnerability indexes, and timing correctness, i.e., the deadline misses. This paper considers the optimization of the Reliability-Timing (RT) penalty, defined as a linear combination of the vulnerability indexes (reliability penalties) and the deadline misses. We propose a multi-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions, for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. Based on the reliability and execution time profiling of these versions, our reliability-driven system software employs dynamic version selections to dynamically select a suitable version of a function according to the execution behavior of the previous functions. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions properly. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution. The reliability analysis and evaluation is performed on a reliability-aware processor simulator.

Crowding at the entrances of large events may lead to critical and life-threatening situations, particularly when people start pushing each other to reach the event faster. Automatic and timely identification of pushing behavior would help organizers and security forces to intervene early and mitigate dangerous situations. In this paper, we propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances. The proposed framework initially modifies and trains the EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it integrates the adapted model with an accurate and fast pre-trained deep optical flow model with the color wheel method to analyze video streams and identify pushing patches in real-time. Moreover, the framework uses live capturing technology and a cloud-based environment to collect video streams of crowds in real-time and provide early-stage results. A novel dataset is generated based on five real-world experiments and their associated ground truth data to train the adapted EfficientNetV2B0 model. The experimental setups simulated a crowded event entrance, while the ground truths for each video experiment was generated manually by social psychologists. Several experiments on the videos and the generated dataset are carried out to evaluate the accuracy and annotation delay time of the proposed framework. The experimental results show that the proposed framework identified pushing behaviors with an accuracy rate of 87% within a reasonable delay time.

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness (i.e., vulnerability indexes) and timing correctness (i.e., deadline misses). This paper considers the optimization of the reliability-timing (RT) penalty, defined as a linear combination of the vulnerability and deadline misses. We propose a cross-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. As the execution time of a function is not fixed, the selection of the versions depends upon the execution behavior of the previous functions. Based on the reliability and execution time profiling of these versions, our reliability-driven system software decides the prioritization of the functions for determining their execution order and employs dynamic version selection to dynamically select a suitable version of a function. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution.

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of $99.6\%$ in an average time of 250 ms.

Computation offloading has been adopted to improve the performance of embedded systems by offloading the computation of some tasks, especially computation-intensive tasks, to servers or clouds. This paper explores computation offloading for real-time embedded systems to decide which tasks should be offloaded to get the results in time. Such a problem is NP-complete even for frame-based real-time tasks with the same period and relative deadline. We develop a pseudo-polynomial-time algorithm for deriving feasible schedules, if they exist.