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The ARINC-653 standard architecture for flight software specifies an application executive (APEX) which provides an application programming interface and defines a hierarchical framework which provides health management for error detection and recovery.In every partition of the architecture, however, asynchronously concurrent processes or threads may include concurrency bugs such as unintended race conditions which are common and difficult to remove by testing. A race condition toward a shared data, or data race, is a pair of unsynchronized instructions that access a shared variable with at least one write access. Data races threaten the reliability of sharedmemory programs seriously and latently, because they result in unintended nondeterministic executions of the programs.To heal data race during executions of ARINC-653 flight software, this paper instruments on-the-fly race detection into the target program and incorporates on-the-fly race healing into the health management of the ARINC-653 architecture. The race detection signals to the health monitor using the corresponding APEX call, if a data race is detected. The health monitor then responds by invoking an aperiodic, user-defined, error handling process that is assigned the highest possible priority. This special process uses an APEX call to identify and then heals the occurrence of race condition as an application error, one of seven error types defined by ARINC-653. This race-healing process allows the target programs to be assured at run-time that the execution result of the healed program could have been in the original program and therefore no new functional bug has been introduced. This paper evaluates efficiencies of the on-the-fly mechanisms to argue that they are practical to be configured under the ARINC-653 partitions.

Data races represent the most notorious class of concurrency bugs in multithreaded programs. To detect data races precisely and efficiently during the execution of multithreaded programs, the epoch-based FastTracktechnique has been employed. However, FastTrackhas time and space complexities that depend on the maximum parallelism of the program to partially maintain expensive data structures, such as vector clocks. This paper presents an efficient algorithm, callediFT, that uses only the epochs of the access histories. Unlike FastTrack, our algorithm requiresO(1)operations to maintain an access history and locate data races, without any switching between epochs and vector clocks. We implement this algorithm on top of the Pin binary instrumentation framework and compare it with other on-the-fly detection algorithms, including FastTrack, which uses a state-of-the-art happens-before analysis algorithm. Empirical results using the PARSEC benchmark show thatiFT reduces the average runtime and memory overhead to 84% and 37%, respectively, of those of FastTrack.

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This paper presents a biosensor-based cattle health monitoring system capable of collecting bio-signals of farm animals in an effective way. For the presented monitoring system. We design an integrated monitoring device consisting of a sensing module to measure bio-signals of cattle such as the heartbeat, the breath rate and the momentum, as well as a Zigbee module designed to transmit the biometric data based on Wireless Sensor Network (WSN).

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