This paper proposes an approach for pedestrian tracking using dead reckoning enhanced with a mode detection using a standard smartphone. The mode represents a specific state of carrying device, and it is automatically detected while a person is walking. This paper presents a new approach, which extends and enhances previous methods by identifying in real-time three typical modes of carrying the device and using the identified mode to enhance tracking accuracy. The way of carrying the device in all modes is unconstrained to offer reliable person-independent tracking. Based on the identification of modes, a lightweight step-based tracking algorithm is developed with a novel step length estimation model. The tracking system is implemented on a commercial off-the-shelf smartphone equipped with a built-in inertial measurement unit with 3-D accelerometer and gyroscope. It achieves real-time tracking and localization performance with an average position accuracy of 98.91%.

Inertial Navigation System (INS) using kinematic sensors is able to provide accurate pedestrian tracking over a short distance. Information fusion of INS with wireless technology has been commonly employed to develop more robust and accurate tracking systems over longer distances. Ultra-Wideband (UWB) based system offers further improvement and achieves positioning error in order of centimetres. Existing INS and UWB integrated systems require at least three anchors at known locations for positioning in the covered area. The deployment cost of such systems is high since UWB devices are more expensive than other commonly used wireless nodes. The need to obtain the exact locations of all anchor nodes also contribute to additional cost in system deployment. In this paper, a low-cost INS and UWB integrated pedestrian tracking system is proposed using only one UWB anchor node at an unknown location, minimizing infrastructure cost and setup. The anchor location is estimated at the initial stage of tracking with the aid of INS tracking algorithm. Afterwards, the UWB ranging measurements are fused with INS data to provide robust and accurate tracking performance. The system is evaluated with pedestrian tracking experiments over long distances and achieves up to 85.75 % reduction in average positioning error when compared to an INS only solution.

IEC 61499 has been endorsed as the standard for modeling and implementing distributed industrial process measurement and control systems. The standard prescribes the use of function blocks for designing systems in a component-oriented approach. The execution model of a basic function block and the manner for event/data connections between blocks are described therein. Unfortunately, the standard does not provide exhaustive specifications for function block execution. Consequently, multiple standard-compliant implementations exhibiting different behaviors are possible. This not only defeats the purpose of having a standard but also makes verification of function block systems difficult. To overcome this, we propose synchronous semantics for function blocks and show its feasibility by translating function blocks into a subset of Esterel, a well-known synchronous language. The proposed semantics avoids causal cycles common in Esterel and is proved to be reactive and deterministic under any composition. Moreover, verification techniques developed for synchronous systems can now be applied to function blocks.

Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns provide valuable insights for decision-making in a wide range of applications. Recently proposed anomaly detection methods based on the tree isolation mechanism are very fast due to their logarithmic time complexity, making them capable of handling big data sets efficiently. However, the underlying similarity or distance measures in these methods have not been well understood. Contrary to the claims that these methods never rely on any distance measure, we find that they have close relationships with certain distance measures. This implies that the current use of this fast isolation mechanism is only limited to these distance measures and fails to generalise to other commonlyused measures. In this paper, we propose a generic framework named LSHiForest for fast tree isolation based ensemble anomaly analysis with the use of a Locality-Sensitive Hashing (LSH) forest. Being generic, the proposed framework can be instantiated with a diverse range of LSH families, and the fast isolation mechanism can be extended to any distance measures, data types and data spaces where an LSH family is defined. In particular, the instances of our framework with kernelised LSH families or learning based hashing schemes can detect complicated anomalies like local or surrounded anomalies. We also formally show that the existing tree isolation based detection methods are special cases of our framework with the corresponding distance measures. Extensive experiments on both synthetic and real-world benchmark data sets show that the framework can achieve both high time efficiency and anomaly detection quality.