MiPOS - the Mote Indoor Positioning System

In the past few years, there have been huge research efforts into ubiquitous and context aware platforms that offer a user a custom level of service based on some known local parameters. The utility of such systems is greatly enhanced if a physical locational area can be determined. Recently, hybrid devices have been developed combining low power micro controllers with short range FM radio transceivers. Some location identification work has been carried out with these systems such as the Matrix Pencil approximation technique[8],however most of these all provide information for an ideal square area with no RF obstructions.Here we present MiPOS, a scalable locationing system based on the MICA mote[11] family of devices.The design goal of MiPOS is to provide a low-power, scalable, distributed locationing system suited to an indoor (office) environment.During the presentation of this paper we will highlight solutions in the areas of security, radio and network management and power awareness for a hybrid context aware wearable locationing device

Concurrent Design of Embedded Control Software

Embedded software design for mechatronic systems is becoming an increasingly time-consuming and error-prone task. In order to cope with the heterogeneity and complexity, a systematic model-driven design approach is needed, where several parts of the system can be designed concurrently. There is however a trade-off between concurrency efficiency and integration efficiency. In this paper, we present a case study on the development of the embedded control software for a real-world mechatronic system in order to evaluate how we can integrate concurrent and largely independent designed embedded system software parts in an efficient way. The case study was executed using our embedded control system design methodology which employs a concurrent systematic model-based design approach that ensures a concurrent design process, while it still allows a fast integration phase by using automatic code synthesis. The result was a predictable concurrently designed embedded software realization with a short integration time

Beyond: collapsible tools and gestures for computational design

Since the invention of the personal computer, digital media has remained separate from the physical world, blocked by a rigid screen. In this paper, we present Beyond, an interface for 3-D design where users can directly manipulate digital media with physically retractable tools and hand gestures. When pushed onto the screen, these tools physically collapse and project themselves onto the screen, letting users perceive as if they were inserting the tools into the digital space beyond the screen. The aim of Beyond is to make the digital 3-D design process straightforward, and more accessible to general users by extending physical affordances to the digital space beyond the computer screen

Analyzing brain activity in understanding cultural and language interaction for depression and anxiety

Human brain has always been considered as a black box and is the source of all emotions. Analyzing cultural and language role through human emotion by looking at the brain activity can thus help us understand depression and stress better. This paper focuses on understanding and analyzing undergraduate students’ emotions with different background and culture after completing their semester final examination. Brain wave signals were captured using EEG device and analyzed through proposing an affective computation model. EEG signal was collected from 8 healthy subjects from different states of Malaysia with different dialects where each subject was emotionally induced with audio and video emotion stimuli using the International Affective Pictures and System (IAPS). Features were extracted from the captured EEG signals using Kernel Density Estimation (KDE), which was then categorized into four basic emotions of happy, calm, sad and fear using the Multi-layer Perceptron (MLP). Results of the study show potential of using such analysis in understanding stress, anxiety and depression

Broadcast Gossip Based Distributed Hypothesis Testing in Wireless Sensor Networks

We consider the scenario that N sensors collaborate to observe a single event. The sensors are distributed and can only exchange messages through a network to reach a consensus about the observed event. In this paper, we propose a very robust and simple method using broadcast gossip algorithm to solve the distributed hypothesis testing problem. The simulation result shows that our method has good performance and is very energy efficient comparing to existing methods

Flexible Bus Media Redundancy

DETIThis paper proposes a flexible approach to bus media redundancy in Controller Area Network (CAN) fieldbuses, both to improve the bandwidth by transmitting different traffic in different channels or to promote redundancy by transmitting the same message in more than one channel. Specifically the proposed solution is discussed in the context of Flexible Time-Triggered protocol over CAN (FTTCAN) and inherits the online scheduling flexibility of FTTCAN, enabling on-the-fly modifications of the traffic conveyed in the replicated buses. Flexible bus media redundancy is useful to fulfill application requirements in terms of additional bandwidth or to react to bus failures leading the system to a degraded operational mode, without compromising safety. The arguments for and against flexible bus media redundancy in the context of FTT-CAN are also discussed in detail

Dynamic Target Classification in Wireless Sensor Networks

Information exploitation schemes with high-accuracy and low computational cost play an important role in Wireless Sensor Networks (WSNs). This thesis studies the problem of target classification in WSNs. Specifically, due to the resource constraints and dynamic nature of WSNs, we focus on the design of the energy-efficient solutionwith high accuracy for target classification in WSNs. Feature extraction and classification are two intertwined components in pattern recognition. Our hypothesis is that for each type of target, there exists an optimal set of features in conjunction with a specific classifier, which can yield the best performance in terms of classification accuracy using least amount of computation, measured by the number of features used. Our objective is to find such an optimal combination of features and classifiers. Our study is in the context of applications deployed in a wireless sensor network (WSN) environment, composed of large number of small-size sensors with their own processing, sensing and networking capabilities powered by onboard battery supply. Due to the extremely limited resources on each sensor platform, the decision making is prune to fault, making sensor fusion a necessity. We present a concept, referred to as dynamic target classification in WSNs. The main idea is to dynamically select the optimal combination of features and classifiers based on the probability that the target to be classified might belong to a certain category. We use two data sets to validate our hypothesis and derive the optimal combination sets by minimizing a cost function. We apply the proposed algorithm to a scenario of collaborative target classification among a group of sensors which are selected using information based sensor selection rule in WSNs. Experimental results show that our approach can significantly reduce the computational time while at the same time, achieve better classification accuracy without using any fusion algorithm, compared with traditional classification approaches, making it a viable solution in practice

Emotion Recognition Using Artificial Intelligence

This paper focuses on the interplay between humans and computer system, the ability for these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these system is that it requires a large training data-sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on combination of facial expression and speech outperforms existing ones, which are based solely either on facial or on verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper the increasing significance and demand for facial recognition technology in emotion recognition is also discussed

Model Based Analysis and Test Generation for Flight Software

We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission