Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Developing wireless measurement system for building deployed capacitive sensors with optimized RF front end circuit

In this paper, a prototype of miniaturized, low power, bi-directional wireless sensor node for wireless sensor networks (WSN) was designed for doors and windows building monitoring. The capacitive pressure sensors have been developed particularly for such application, where packaging size and minimization of the power requirements of the sensors are the major drivers. The capacitive pressure sensors have been fabricated using a 2.4 mum thick strain compensated heavily boron doped SiGeB diaphragm is presented. In order to integrate the sensors with the wireless module, the sensor dice was wire bonded onto TO package using chip on board (COB) technology. The telemetric link and its capabilities to send information for longer range have been significantly improved using a new design and optimization process. The simulation tool employed for this work was the Designerreg tool from Ansoft Corporation.

Wireless sensor node design for heterogeneous networks

Two complementary wireless sensor nodes for building two-tiered heterogeneous networks are presented. A larger node with a 25 mm by 25 mm size acts as the backbone of the network, and can handle complex data processing. A smaller, cheaper node with a 10 mm by 10 mm size can perform simpler sensor-interfacing tasks. The 25mm node is based on previous work that has been done in the Tyndall National Institute that created a modular wireless sensor node. In this work, a new 25mm module is developed operating in the 433/868 MHz frequency bands, with a range of 3.8 km. The 10mm node is highly miniaturised, while retaining a high level of modularity. It has been designed to support very energy efficient operation for applications with low duty cycles, with a sleep current of 3.3 μA. Both nodes use commercially available components and have low manufacturing costs to allow the construction of large networks. In addition, interface boards for communicating with nodes have been developed for both the 25mm and 10mm nodes. These interface boards provide a USB connection, and support recharging of a Li-ion battery from the USB power supply. This paper discusses the design goals, the design methods, and the resulting implementation.

Design of miniaturized wireless sensor mote and actuator for building monitoring and control

In this paper, a wireless sensor network mote hardware design and implementation are introduced for building deployment application. The core of the mote design is based on the 8 bit AVR microcontroller, Atmega1281 and 2.4 GHz wireless communication chip, CC2420. The module PCB fabrication is using the stackable technology providing powerful configuration capability. Three main layers of size 25 mm2 are structured to form the mote; these are RF, sensor and power layers. The sensors were selected carefully to meet both the building monitoring and design requirements. Beside the sensing capability, actuation and interfacing to external meters/sensors are provided to perform different management control and data recording tasks. Experiments show that the developed mote works effectively in giving stable data acquisition and owns good communication and power performance.

Remote electricity actuation and monitoring mote

This work presents the design and evaluation of the REAM (Remote Electricity Actuation and Monitoring) node based around the modular Tyndall Mote platform. The REAM node enables the user to remotely actuate power to a mains power extension board while sampling the current, voltage, power and power factor of the attached load. The node contains a current transformer interfaced to an Energy Metering IC which continuously samples current and voltage. These values are periodically read from the part by a PIC24 microcontroller, which calculates the RMS current and voltage, power factor and overall power. The resultant values can then be queried wirelessly employing the Tyndall 802.15.4 compliant wireless module.

Antenna tuning for wearable wireless sensors

When miniaturized wireless sensors are placed on or close to the human body, they can experience a significant loss inperformance due to antenna detuning, resulting in degradationof wireless performance as well as decreased battery lifetime.Several antenna tuning technologies have been proposed formobile wireless devices but devices suitable for widespread integration have yet to emerge. This paper highlights the possible advantages of antenna tuning for wearable wireless sensors and presents the design and characterization of a prototype 433MHz tuner module.

Practical wireless sensor networks power consumption metrics for building energy management applications

The power consumption of wireless sensor networks (WSN) module is an important practical concern in building energy management (BEM) system deployments. A set of metrics are created to assess the power profiles of WSN in real world condition. The aim of this work is to understand and eventually eliminate the uncertainties in WSN power consumption during long term deployments and the compatibility with existing and emerging energy harvesting technologies. This paper investigates the key metrics in data processing, wireless data transmission, data sensing and duty cycle parameter to understand the system power profile from a practical deployment prospective. Based on the proposed analysis, the impacts of individual metric on power consumption in a typical BEM application are presented and the subsequent low power solutions are investigated.

Gröbner basis techniques for certain problems in coding and systems theory

There is much common ground between the areas of coding theory and systems theory. Fitzpatrick has shown that a Göbner basis approach leads to efficient algorithms in the decoding of Reed-Solomon codes and in scalar interpolation and partial realization. This thesis simultaneously generalizes and simplifies that approach and presents applications to discrete-time modeling, multivariable interpolation and list decoding. Gröbner basis theory has come into its own in the context of software and algorithm development. By generalizing the concept of polynomial degree, term orders are provided for multivariable polynomial rings and free modules over polynomial rings. The orders are not, in general, unique and this adds, in no small way, to the power and flexibility of the technique. As well as being generating sets for ideals or modules, Gröbner bases always contain a element which is minimal with respect tot the corresponding term order. Central to this thesis is a general algorithm, valid for any term order, that produces a Gröbner basis for the solution module (or ideal) of elements satisfying a sequence of generalized congruences. These congruences, based on shifts and homomorphisms, are applicable to a wide variety of problems, including key equations and interpolations. At the core of the algorithm is an incremental step. Iterating this step lends a recursive/iterative character to the algorithm. As a consequence, not all of the input to the algorithm need be available from the start and different "paths" can be taken to reach the final solution. The existence of a suitable chain of modules satisfying the criteria of the incremental step is a prerequisite for applying the algorithm.

Modular average case analysis: Language implementation and extension

Motivated by accurate average-case analysis, MOdular Quantitative Analysis (MOQA) is developed at the Centre for Efficiency Oriented Languages (CEOL). In essence, MOQA allows the programmer to determine the average running time of a broad class of programmes directly from the code in a (semi-)automated way. The MOQA approach has the property of randomness preservation which means that applying any operation to a random structure, results in an output isomorphic to one or more random structures, which is key to systematic timing. Based on original MOQA research, we discuss the design and implementation of a new domain specific scripting language based on randomness preserving operations and random structures. It is designed to facilitate compositional timing by systematically tracking the distributions of inputs and outputs. The notion of a labelled partial order (LPO) is the basic data type in the language. The programmer uses built-in MOQA operations together with restricted control flow statements to design MOQA programs. This MOQA language is formally specified both syntactically and semantically in this thesis. A practical language interpreter implementation is provided and discussed. By analysing new algorithms and data restructuring operations, we demonstrate the wide applicability of the MOQA approach. Also we extend MOQA theory to a number of other domains besides average-case analysis. We show the strong connection between MOQA and parallel computing, reversible computing and data entropy analysis.

Biodiscovery of natural products from microbes associated with Irish coastal sponges

Marine sponges have been an abundant source of new metabolites in recent years. The symbiotic association between the bacteria and the sponge has enabled scientists to access the bacterial diversity present within the bacterial/sponge ecosystem. This study has focussed on accessing the bacterial diversity in two Irish coastal marine sponges, namely Amphilectus fucorum and Eurypon major. A novel species from the genus Aquimarina has been isolated from the sponge Amphilectus fucorum. The study has also resulted in the identification of an α–Proteobacteria, Pseudovibrio sp. as a potential producer of antibiotics. Thus a targeted based approach to specifically cultivate Pseudovibrio sp. may prove useful for the development of new metabolites from this particular genus. Bacterial isolates from the marine sponge Haliclona simulans were screened for anti–fungal activity and one isolate namely Streptomyces sp. SM8 displayed activity against all five fungal strains tested. The strain was also tested for anti–bacterial activity and it showed activity against both against B. subtilis and P. aeruginosa. Hence a combinatorial approach involving both biochemical and genomic approaches were employed in an attempt to identify the bioactive compounds with these activities which were being produced by this strain. Culture broths from Streptomyces sp. SM8 were extracted and purified by various techniques such as reverse–phase HPLC, MPLC and ash chromatography. Anti–bacterial activity was observed in a fraction which contained a hydroxylated saturated fatty acid and also another compound with a m/z 227 but further structural elucidation of these compounds proved unsuccessful. The anti–fungal fractions from SM8 were shown to contain antimycin–like compounds, with some of these compounds having different retention times from that of an antimycin standard. A high–throughput assay was developed to screen for novel calcineurin inhibitors using yeast as a model system and three putative bacterial extracts were found to be positive using this screen. One of these extracts from SM8 was subsequently analysed using NMR and the calcineurin inhibition activity was con rmed to belong to a butenolide type compound. A H. simulans metagenomic library was also screened using the novel calcineurin inhibitor high–throughput assay system and eight clones displaying putative calcineurin inhibitory activity were detected. The clone which displayed the best inhibitory activity was subsequently sequenced and following the use of other genetic based approaches it became clear that the inhibition was being caused by a hypothetical protein with similarity to a hypothetical Na+/Ca2+ exchanger protein. The Streptomyces sp. SM8 genome was sequenced from a fragment library using Roche 454 pyrosequencing technology to identify potential secondary metabolism clusters. The draft genome was annotated by IMG/ER using the Prodigal pipeline. The Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AMPN00000000. The genome contains genes which appear to encode for several polyketide synthases (PKS), non–ribosomal peptide synthetases (NRPS), terpene and siderophore biosynthesis and ribosomal peptides. Transcriptional analyses led to the identification of three hybrid clusters of which one is predicted to be involved in the synthesis of antimycin, while the functions of the others are as yet unknown. Two NRPS clusters were also identified, of which one may be involved in gramicidin biosynthesis and the function of the other is unknown. A Streptomyces sp. SM8 NRPS antC gene knockout was constructed and extracts from the strain were shown to possess a mild anti–fungal activity when compared to the SM8 wild–type. Subsequent LCMS analysis of antC mutant extracts confirmed the absence of the antimycin in the extract proving that the observed anti–fungal activity may involve metabolite(s) other than antimycin. Anti–bacterial activity in the antC gene knockout strain against P. aeruginosa was reduced when compared to the SM8 wild–type indicating that antimycin may be contributing to the observed anti–bacterial activity in addition to the metabolite(s) already identified during the chemical analyses. This is the first report of antimycins exhibiting anti–bacterial activity against P. aeruginosa. One of the hybrid clusters potentially involved in secondary metabolism in SM8 that displayed high and consistent levels of gene–expression in RNA studies was analysed in an attempt to identify the metabolite being produced by the pathway. A number of unusual features were observed following bioinformatics analysis of the gene sequence of the cluster, including a formylation domain within the NRPS cluster which may add a formyl group to the growing chain. Another unusual feature is the lack of AT domains on two of the PKS modules. Other unusual features observed in this cluster is the lack of a KR domain in module 3 of the cluster and an aminotransferase domain in module 4 for which no clear role has been hypothesised.

Work experience in transition year: a mixed methods study of subject and career choices

Transition Year (TY) has been a feature of the Irish Education landscape for 39 years. Work experience (WE) has become a key component of TY. WE is defined as a module of between five and fifteen days duration where students engage in a work placement in the broader community. It places a major emphasis on building relationships between schools and their external communities and concomitantly between students and their potential future employers. Yet, the idea that participation in a TY work experience programme could facilitate an increased awareness of potential careers has drawn little attention from the research community. This research examines the influence WE has on the subsequent subjects choices made by students along with the effects of that experience on the students’ identities and emerging vocational identities. Socio-cultural Learning Theory and Occupational Choice Theory frame the overall study. A mixed methods approach to data collection was adopted through the administration of 323 quantitative questionnaires and 32 individual semi-structured interviews in three secondary schools. The analysis of the data was conducted using a grounded theory approach. The findings from the research show that WE makes a significant contribution to the students’ sense of agency in their own lives. It facilitates the otherwise complex process of subject choice, motivates students to work harder in their senior cycle, introduces them to the concepts of active, experience-based and self-directed learning, while boosting their self-confidence and nurturing the emergence of their personal and vocational identities. This research is a gateway to further study in this field. It also has wide reaching implications for students, teachers, school authorities, parents and policy makers regarding teaching and learning in our schools and the value of learning beyond the walls of the classroom.