DB2 Universal Database: A Case Study of a Successful User-Centered Design Program

This case study describes the application of user-centered design (UCD) principles to the development of a complex middleware software product-IBM's DB2(r) Universal Database. Motivated by trade press reviews highlighting ease-of-use problems, a multisite development team utilized IBM UCD to transform ease of use into a featured product attribute. This case study describes the initial application of UCD to DB21 and the positive business results that followed: critical acclaim, increased marketshare and user satisfaction, and increased support and adoption of UCD across the IBM database product family. Although ease-of-use objectives, target markets, and human factors practitioners' roles have evolved over 6 years and multiple versions of the product, DB2 continues to achieve positive results with UCD. In addition to the flexibility of the UCD methodology, other factors that influenced DB2's success included management support, resource commitment, cross-site communication and collaboration, detailed product specifications, and a focus on supporting user tasks.     

A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

A robust and reliable method for detecting signals of interest in multiexponential decays

The concept of rejecting the null hypothesis for definitively detecting a signal was extended to relaxation spectrum space for multiexponential reconstruction. The novel test was applied to the problem of detecting the myelin signal, which is believed to have a time constant below 40 ms, in T2 decays from magnetic resonance imagining of the human brain. It was demonstrated that the test allowed the detection of a signal in a relaxation spectrum by using only the information in the data, thus avoiding any potentially unreliable prior information. The test was implemented both explicitly and implicitly for simulated T2 measurements. For the explicit implementation, the null hypothesis was that a relaxation spectrum existed that had no signal below 40 ms and that was consistent with the T2 decay. The confidence level by which the null hypothesis could be rejected gave the confidence level that there was signal below the 40 ms time constant. The explicit implementation assessed the test's performance with and without prior information where the prior information was the non-negative relaxation spectrum assumption. The test was also implemented implicitly with a data conserving multiexponential reconstruction algorithm that used left invertible matrices and that has been published previously. The implicit and explicit implementations demonstrated similar characteristics in detecting the myelin signal in both the simulated and experimental T2 decays, providing additional evidence to support the close link between the two tests. When the relaxation spectrum was assumed to be non-negative, the novel test required signal to noise ratios (SNRs) approaching 1000 in the T2 decays for detection of the myelin signal with high confidence. When the relaxation spectrum was not assumed to be non-negative, the SNR requirements for a detection with high confidence increased by a factor of 25. The application of the test to a T2 decay from human white matter, measured in vivo with a SNR of 650, demonstrated a solid detection of the signal below 40 ms believed to be due to the myelin water. This study demonstrated the robustness and reliability of extending the concept of rejecting the null hypothesis to relaxation spectrum space. The study also raised serious questions about the susceptibility to false positive detection of the myelin signal of the multiexponential reconstruction algorithms currently in use.     

Report from the Wellcome Trust/EBA “Perspectives in Clinical Proteomics” retreat--a strategy to implement next-generation proteomic analyses to the clinic for patient benefit: pathway to translation

Proteomics has long been thought to hold the promise of producing results of clinical utility which will influence patient treatment and outcomes. A recent Wellcome Trust/EBI meeting and retreat--“Perspectives in Clinical Proteomics”--brought together experts from a broad range of stakeholder groups with an interest in ensuring proteomics achieves this aim. This viewpoint presents views derived from these forums, proposing a pathway for the development of next-generation proteomic analyses in the clinical setting from selection of candidates through to their validation and ultimate demonstration of utility through health technology assessments. Although not meant to be all encompassing, important elements for proteomics researchers to consider are presented.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.