Adaptive Interactive Learning: a Novel Approach to Training Brain-Computer Interface Systems

Aju-arvuti liides (AAL) on süsteem, mis võimaldab infovahetust inimese aju ja arvuti vahel. Kasutades erinevaid neuropildistuste tehnikaid aju aktiivsust salvestatakse ja saadetakse arvutisse, kus signaal töödeldakse masinõpe meetoditega. AALi põhieesmärk on anda inimesele võimalust juhtida välisseadet kasutades mõttejõudu. Inimese mõtteseisundite eristame on raske ülesanne, mis ei ole lahendatav ainult masinõpe kasutamisega. Vastuvõetav klassifitseerimise täpsuse tase on saavutatav pärast pikajalist õpetamise protsessi, mille jooksul inimene õpib kuidas ta peab tekitama sobivad mõtteseisundid, ning arvuti loob mudeli, mis oskab neid eristada. Käesolevas töös me esitame uut lähenemist AAL süsteemi õpetamise protsessi jaoks. See põhineb inimese ja arvuti koostoimimise ideel, mille jooksul mõlemad osapooled adapteerivad oma käitumist vastavalt sellele, millist tagasisided nad saavad suhtlemise ajal. Pakutud viisi vastandiks on võetud traditsiooniline lähenemine, kus katseisik ei saa tagasisidet õppeprotsessi edukusest selle käigus. Teine uudsus traditsioonilise meetodiga võrreldes on juhendamata õppealgoritmi kasutamine (iseorganiseeriv kaart, SOM) meie süsteemi tuumana. Algne iseorganiseeruva kaardi algoritm on täiendatud niimoodi, et ta esindab tõenäosusliku ennustamise mudelit, mis oskab klassifitseerida aju signaali, anda tagasisidet katseisikule ning vajadusel kohandada mudelit reaalajas. Tuginedes läbiviidud eksperimentide tulemustel e järeldame, et interaktiivne lähenemine süsteemi õpetamiseks omab hulk eelisi traditsioonilise meetodiga võrreldes.A Brain-Computer Interface is a system which allows communication between a human and a computer. Using various neuroimaging techniques the brain activity is recorded and transmitted to the computer, where the signal is analyzed with the help of machine learning methods. The ultimate goal of BCI is to empower the human with the ability to control the external device with the power of thought. However, distinguishing mental states of a human is a challenging task and standard machine learning alone is not enough to solve the problem. Acceptable level of performance can be achieved after a long training process, during which the human learns how to produce suitable mental states and the machine creates a model, which is able to classify the signal. In this thesis we proposed a conceptually new approach to the process of training a BCI system. It relies on the idea of the interaction between the test subject and the machine and the ability of those two agents to adapt their behavior accordingly to the information they receive during the learning process. The approach is proposed as a counterpart to the traditional BCI training, where the test subject does not receive any feedback. Another novelty in comparison to the traditional approach is using an unsupervised learning algorithm (SOM) as the core of the learning system. The original concept of self-organizing maps is amended to represent a probabilistic predictive model, which can be used to classify the brain signal, provide feedback and adapt the model in real time. Based on the results of the conducted experiments we conclude that adaptive learning process has the multiple major advantages over the traditional one

Optimising Structured P2P Networks for Complex Queries

With network enabled consumer devices becoming increasingly popular, the number of connected devices and available services is growing considerably - with the number of connected devices es- timated to surpass 15 billion devices by 2015. In this increasingly large and dynamic environment it is important that users have a comprehensive, yet efficient, mechanism to discover services. Many existing wide-area service discovery mechanisms are centralised and do not scale to large numbers of users. Additionally, centralised services suffer from issues such as a single point of failure, high maintenance costs, and difficulty of management. As such, this Thesis seeks a Peer to Peer (P2P) approach. Distributed Hash Tables (DHTs) are well known for their high scalability, financially low barrier of entry, and ability to self manage. They can be used to provide not just a platform on which peers can offer and consume services, but also as a means for users to discover such services. Traditionally DHTs provide a distributed key-value store, with no search functionality. In recent years many P2P systems have been proposed providing support for a sub-set of complex query types, such as keyword search, range queries, and semantic search. This Thesis presents a novel algorithm for performing any type of complex query, from keyword search, to complex regular expressions, to full-text search, over any structured P2P overlay. This is achieved by efficiently broadcasting the search query, allowing each peer to process the query locally, and then efficiently routing responses back to the originating peer. Through experimentation, this technique is shown to be successful when the network is stable, however performance degrades under high levels of network churn. To address the issue of network churn, this Thesis proposes a number of enhancements which can be made to existing P2P overlays in order to improve the performance of both the existing DHT and the proposed algorithm. Through two case studies these enhancements are shown to improve not only the performance of the proposed algorithm under churn, but also the performance of traditional lookup operations in these networks

Study to design and develop remote manipulator system

Modeling of human performance in remote manipulation tasks is reported by automated procedures using computers to analyze and count motions during a manipulation task. Performance is monitored by an on-line computer capable of measuring the joint angles of both master and slave and in some cases the trajectory and velocity of the hand itself. In this way the operator's strategies with different transmission delays, displays, tasks, and manipulators can be analyzed in detail for comparison. Some progress is described in obtaining a set of standard tasks and difficulty measures for evaluating manipulator performance

Design Strategies for Adaptive Social Composition: Collaborative Sound Environments

In order to develop successful collaborative music systems a variety of subtle interactions need to be identified and integrated. Gesture capture, motion tracking, real-time synthesis, environmental parameters and ubiquitous technologies can each be effectively used for developing innovative approaches to instrument design, sound installations, interactive music and generative systems. Current solutions tend to prioritise one or more of these approaches, refining a particular interface technology, software design or compositional approach developed for a specific composition, performer or installation environment. Within this diverse field a group of novel controllers, described as ‘Tangible Interfaces’ have been developed. These are intended for use by novices and in many cases follow a simple model of interaction controlling synthesis parameters through simple user actions. Other approaches offer sophisticated compositional frameworks, but many of these are idiosyncratic and highly personalised. As such they are difficult to engage with and ineffective for groups of novices. The objective of this research is to develop effective design strategies for implementing collaborative sound environments using key terms and vocabulary drawn from the available literature. This is articulated by combining an empathic design process with controlled sound perception and interaction experiments. The identified design strategies have been applied to the development of a new collaborative digital instrument. A range of technical and compositional approaches was considered to define this process, which can be described as Adaptive Social Composition. Dan Livingston

Data replication and update propagation in XML P2P data management systems

XML P2P data management systems are P2P systems that use XML as the underlying data format shared between peers in the network. These systems aim to bring the benefits of XML and P2P systems to the distributed data management field. However, P2P systems are known for their lack of central control and high degree of autonomy. Peers may leave the network at any time at will, increasing the risk of data loss. Despite this, most research in XML P2P systems focus on novel and efficient XML indexing and retrieval techniques. Mechanisms for ensuring data availability in XML P2P systems has received comparatively little attention. This project attempts to address this issue. We design an XML P2P data management framework to improve data availability. This framework includes mechanisms for wide-spread data replication, replica location and update propagation. It allows XML documents to be broken down into fragments. By doing so, we aim to reduce the cost of replicating data by distributing smaller XML fragments throughout the network rather than entire documents. To tackle the data replication problem, we propose a suite of selection and placement algorithms that may be interchanged to form a particular replication strategy. To support the placement of replicas anywhere in the network, we use a Fragment Location Catalogue, a global index that maintains the locations of replicas. We also propose a lazy update propagation algorithm to propagate updates to replicas. Experiments show that the data replication algorithms improve data availability in our experimental network environment. We also find that breaking XML documents into smaller pieces and replicating those instead of whole XML documents considerably reduces the replication cost, but at the price of some loss in data availability. For the update propagation tests, we find that the probability that queries return up-to-date results increases, but improvements to the algorithm are necessary to handle environments with high update rates

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion