This paper describes the design, implementation and evaluation of a simulator that generates plausible data for the debugging and testing of situation-aware monitoring systems. The use of simulated data is necessary because there is very little sensor-level behavioural-data available in the literature and, moreover, these data are very specific of the set-up used to collect them, i.e. the kind of sensors, the people behavior and age, the environment characteristics, and so on. Moreover, data collection requires a long time since the phenomena we are interested in last months or years. The use of a tool like Repast has substantially sped up the design and implementation phase while still achieving the necessary time performance and quality of the data generated.

Abstract In recent years there has been a growing interest in solutions for the delivery of clinical care for the elderly because of the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called Secure and INDependent lIving (SINDI), focused on (a) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and (b) inferring from these data enough information to support caregivers in understanding patients' well-being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.

In this paper we propose an innovative approach to the problem of indoor position estimation that aims at extending tracking to a new level of “awareness” bringing to bear new ambient data and opening the possibility of “reasoning” not only on simple positioning but also on the situation at hand. In order to validate the approach, we implemented a positioning system called Situation-Aware Indoor Tracking (SAIT). The comparison of SAIT with several commercial systems highlights a promising behaviour, showing that exploiting the movement data (e.g. the users' heading and speed) for updating the PF motion models used in the tracking engine together with situation assessment techniques can improve the accuracy of tracking up to 42% in comparison with a Wi-Fi based system.

This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning component, which is based on answer set programming (ASP), in performing three main reasoning tasks: (i) continuous contextualization of the physical, mental and social state of a patient, (ii) prediction of possibly risky situations and (iii) identification of plausible causes for the worsening of a patient's health. Starting from different data sources (sensor data, test results, inference results) the reasoning component applies expressive logic rules aimed at correct interpretation of incomplete or inconsistent contextual information, and evaluates correlation rules expressed by clinicians. The expressive power of ASP allows efficient enough reasoning to support prevention, while declarativity simplifies rule-specification and allows automatic encoding of knowledge. Preliminary evaluations show that the combination of an ASP-based reasoning component and a WSN is a good solution for creating a home-based healthcare system.

Today, mobile learning has become the heir of electronic learning. M-learning provides a new learning experience, even though it introduces new research challenges and opens issues which need to be faced. This paper proposes a new platform based on the concepts of push services, instant messaging, and SIP (Session Initiation Protocol). The platform addresses the actual conceptual and technological limitations of existing m-learning solutions and it supports our new learning paradigm anytime + anywhere + just for me.

This paper is a case study of user involvement in the requirements specification for project ISLE: Interactive Spoken Language Education. Developers of Spoken Language Dialogue Systems should involve users from the outset, particularly if the aim is to develop novel solutions for a generic target application area or market. As well as target end-users, SLDS developers should identify and consult ‘meta-level’ domain experts with expertise in human-to-human dialogue in the target domain. In our case, English language teachers and publishers provided generic knowledge of learners' dialogue preferences; other applications have analogous domain language experts. These domain language experts can help to pin down a domain-specific sublanguage which fits the constraints of current speech recognition technology: linguistically-naive end-users may expect unconstrained conversational English, but in practice, dialogue interactions have to be constrained in vocabulary and syntax. User consultation also highlighted a need to consider how to integrate speech input and output with other modes of interaction and processing; in our case the input speech signal is processed by speech recogniser, stress and mispronunciation detectors, and output responses are text and graphics as well as speech. This suggests a need to revisit the definition of ‘dialogue’: other SLDS developers should also consider the merits of multimodality as an adjunct to pure spoken language dialogue, particularly given that current systems are not capable of accurately handling unconstrained English.

We describe a sub-vector clustering technique to reduce the memory size and computational cost of continuous density hidden Markov models (CHMMs). Acoustic models in modern large-vocabulary, continuous speech recognition systems are typically CHMMs. Systems with 100,000 Gaussian distributions of 40-60 dimensions are common, needing several tens of MB of memory. Computing HMM state likelihoods is several tens of times slower than real time. We show that by clustering and quantizing the Gaussian distributions a few dimensions at a time, both computation and memory costs can be reduced several fold without significant loss of recognition accuracy. On the 1994 Wall Street Journal 20K test set, this technique reduced the acoustic model size by a factor of 9-10, and HMM state output likelihood computation time by a factor of 4-5. 1.

PLUS is a multiprocessor architecture tailored to the fast execution of a single multithreaded process; its goal is to accelerate the execution of CPU-bound applications. PLUS supports shared memory and efficient synchronization. Memory access latency is reduced by non-demand replication of pages with hardware-supported coherence between replicated pages. The architecture has been simulated in detail and the paper presents some of the key measurements that have been used to substantiate our architectural decisions. The current implementation of PLUS is also described.

BEAM is a hardware accelerator that has been designed and built for real-time execution of the SPHINX speaker-independent, continuous-speech recognition system and similar systems. SPHINX on BEAM is able to recognize sentences from a 1000-word vocabulary and a perplexity-60 grammar in about 1.3 times real time. BEAM does not use any custom integrated circuits. The architecture of the accelerator is described. Performance data are given and compared with those for other architectures. It is concluded that BEAM demonstrates how general-purpose technology can be used to build systems that are substantially faster than general-purpose systems.>

A system called Agora was designed and implemented that supports the development of multilanguage parallel applications for heterogeneous machines. Agora hinges on two ideas: the first one is that shared memory can be a suitable abstraction to program concurrent, multilanguage modules running on heterogeneous machines. The second idea is that a shared memory abstraction can be efficiently supported across different computer architectures that are not connected by a physical shared memory, e.g., local area network workstations or ensemble machines. Agora has been in use for more than a year. The authors describe the Agora shared memory and its software implementation on both tightly and loosely coupled architectures. Measurements of the current implementation are also included.>

We have designed and implemented a software facility, called Agora, that supports the development of parallel applications written in multiple languages. At the core of Agora there is a mechanism that allows concurrent computations to share data structures independently of the computer architecture they are executed on. Concurrent computations exchange control information by using a pattern-directed technique. This paper describes the Agora shared memory and its software implementation on both tightly and loosely-coupled architectures.

This paper is an overview of the software environment (called AGORA) that is being developed at Carnegie-Mellon University to support the devlopment of connected, speaker independent, speech recognition systems. This includes the design, implemenentation, testing and fast (parallel) execution of speech recognition programs composed of a large number of independent algorithms. Systems described within AGORA can be executed on a number of different multiprocessor configurations.

This paper describes two custom architectures tailored to a speech recognition beam search algorithm. Both architectures have been simulated using real data and the results of the simulation are presented. The paper also describes the design process of the custom architectures and presents a number of ideas on the automatic design of custom systems for data dependent computations.

The goal of the paper is to present some of the design characteristics and performance of a special purpose custom machine that has the potential of improving the speed of the implementation of a beam search algorithm by two orders of magnitude when compared with a general purpose architecture implementation. The paper also describes the architecture of a general purpose self-timed device that can be used in implementing such a machine.

The design of a custom VLSI circuit that has the potential of achieving a high throughput when executing one of the most computationally expensive modules of a speech recognition system is presented. The architecture of the device is described in terms, of a set of self-timed building blocks that implement arithmetic operations, storage and input/output functions. The formalism used to describe the architecture makes it easy to define variations of the basic structure in order to deal with recognition systems that use different heuristics and search different kinds of databases. A tool that helps the designer investigate the area/speed trade-offs is also described.

The behavior of many speech recognition systems, and of some Artificial Intelligence programs, can he modeled as a data driven computation in which the "instructions" are complex but inexpensive operations (e.g. the evaluation of the likelihood of a partial sentence hypothesis), and the "data-flow graph" is derived directly from the knowledge representation (e.g. the phoneme-level network in a Harpy system). The architecture of a machine that exploits this characteristic is presented together with the results of the simulation of one possible implementation.

The Cost effectiveness of various search methods used in experimental and practical discrete utterance speech recognition systems is a very critical factor for the usefulness of such systems. The advantages of some cost effective search techniques, e.g. branch and bound search, branch and bound search with pruning and beam search, have been previoulsy reported. In this paper we analyze the properties that affect the practical usefulness of these algorithms when task characteristics and machine architecture are considered.