Organizational and direct support maintenance repair parts and special tools list (including depot maintenance repair parts and special tools) for machine gun, 7.62MM, M240 (1005-01-025-8095) and machine gun, 7.62MM, M240C (1005-01-085-4758).

Includes index.

Direct support and general support maintenance manual : armament subsystem, helicopter, 7.62-mm machine gun, 40-mm grenade launcher, M28A1, (1090-00-134-3071), (used on AH-1G helicopter).

Includes index.

Direct support and general support maintenance repair parts and special tools lists : armament subsystem, helicopter, 7.62 millimeter machine gun -- 40 millimeter grenade launcher, M28A1E1 (1090-00-081-1046) (used on AH-1Q Helicopter).

"October 1975."

Direct support, general support, and depot maintenance repair parts and special tools list : armament subsystem, helicopter, 7.62 millimeter machine gun-2.75 inch rocket launcher, M21 (1090-923-5971) (used on UH-1B and UH-1C helicopters).

"October 1970."

Operator, organizational, direct support, and general support maintenance manual including repair parts and special tool lists ... : various machine gun mounts and combinations used on tactical and armored vehicles : mounts, machine gun ... 1005-774-6836 (M548 carrier).

"February 1973."

Unit and intermediate direct support maintenance repair parts and special tools list (including depot maintenance repair parts and special tools) for machine gun, 7.62mm, M240 (1005-01-025-8095) and machine gun, 7.62mm, M240C (1005-01-085-4758) and machine gun, 7.62 mm, M240E1 (1005-01-252-4288) [microform]

"June 1988."

Organizational, direct support, and general support maintenance repair parts and special tools list ... for reeling machine, cable, engine driven, RL-207/G and RL-207A/G (NSN 3895-00-892-4583).

"August 1976."

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.

A middleware to support services delivery in a domain-specific virtual machine

The increasing use of model-driven software development has renewed emphasis on using domain-specific models during application development. More specifically, there has been emphasis on using domain-specific modeling languages (DSMLs) to capture user-specified requirements when creating applications. The current approach to realizing these applications is to translate DSML models into source code using several model-to-model and model-to-code transformations. This approach is still dependent on the underlying source code representation and only raises the level of abstraction during development. Experience has shown that developers will many times be required to manually modify the generated source code, which can be error-prone and time consuming. ^ An alternative to the aforementioned approach involves using an interpreted domain-specific modeling language (i-DSML) whose models can be directly executed using a Domain Specific Virtual Machine (DSVM). Direct execution of i-DSML models require a semantically rich platform that reduces the gap between the application models and the underlying services required to realize the application. One layer in this platform is the domain-specific middleware that is responsible for the management and delivery of services in the specific domain. ^ In this dissertation, we investigated the problem of designing the domain-specific middleware of the DSVM to facilitate the bifurcation of the semantics of the domain and the model of execution (MoE) while supporting runtime adaptation and validation. We approached our investigation by seeking solutions to the following sub-problems: (1) How can the domain-specific knowledge (DSK) semantics be separated from the MoE for a given domain? (2) How do we define a generic model of execution (GMoE) of the middleware so that it is adaptable and realizes DSK operations to support delivery of services? (3) How do we validate the realization of DSK operations at runtime? ^ Our research into the domain-specific middleware was done using an i-DSML for the user-centric communication domain, Communication Modeling Language (CML), and for microgrid energy management domain, Microgrid Modeling Language (MGridML). We have successfully developed a methodology to separate the DSK and GMoE of the middleware of a DSVM that supports specialization for a given domain, and is able to perform adaptation and validation at runtime. ^

Patterns, heuristics for architectural design support: making use of evolutionary modelling in design

Software used by architectural and industrial designers – has moved from becoming a tool for drafting, towards use in verification, simulation, project management and project sharing remotely. In more advanced models, parameters for the designed object can be adjusted so a family of variations can be produced rapidly. With advances in computer aided design technology, numerous design options can now be generated and analyzed in real time. However the use of digital tools to support design as an activity is still at an early stage and has largely been limited in functionality with regard to the design process. To date, major CAD vendors have not developed an integrated tool that is able to both leverage specialized design knowledge from various discipline domains (known as expert knowledge systems) and support the creation of design alternatives that satisfy different forms of constraints. We propose that evolutionary computing and machine learning be linked with parametric design techniques to record and respond to a designer’s own way of working and design history. It is expected that this will lead to results that impact on future work on design support systems-(ergonomics and interface) as well as implicit constraint and problem definition for problems that are difficult to quantify.

Developments in texture-based vector field visualisation techniques

Vector field visualisation is one of the classic sub-fields of scientific data visualisation. The need for effective visualisation of flow data arises in many scientific domains ranging from medical sciences to aerodynamics. Though there has been much research on the topic, the question of how to communicate flow information effectively in real, practical situations is still largely an unsolved problem. This is particularly true for complex 3D flows. In this presentation we give a brief introduction and background to vector field visualisation and comment on the effectiveness of the most common solutions. We will then give some examples of current development on texture-based techniques, and given practical examples of their use in CFD research and hydrodynamic applications.

Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12 566 individuals

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10-5). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted. © The Author 2010. Published by Oxford University Press. All rights reserved.

Machine vision toolbox for MATLAB, release 3 [Software]

The Toolbox, combined with MATLAB ® and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. For modest image sizes the processing rate can be sufficiently ``real-time'' to allow for closed-loop control. Focus of attention methods such as dynamic windowing (not provided) can be used to increase the processing rate. With input from a firewire or web camera (support provided) and output to a robot (not provided) it would be possible to implement a visual servo system entirely in MATLAB. Provides many functions that are useful in machine vision and vision-based control. Useful for photometry, photogrammetry, colorimetry. It includes over 100 functions spanning operations such as image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration and color space conversion.