Resonant metal-mesh bandpass filters for the far infrared

The spectral performance of freestanding resonant metal-mesh bandpass filters operating with center frequencies ranging from 585 GHz to 2.1 THz is presented. These filters are made up of a 12-μm-thick copper film with an array of cross-shaped apertures that fill a circular area with a 50-mm diameter. The filters exhibit power transmission in the range 97-100% at their respective center frequencies and stop-band rejection in excess of 18 dB. The theoretically predicted nondiffracting properties of the meshes are experimentally verified through high-resolution beam mapping. Scalability of the filter spectra with mesh dimensions is demonstrated over a wide spectral range. Several modeling methods are considered, and results from the models are shown.     

Method to derive ocean absorption coefficients from remote-sensing reflectance

A method to derive in-water absorption coefficients from total remote-sensing reflectance (ratio of the upwelling radiance to the downwelling irradiance above the surface) analytically is presented. For measurements made in the Gulf of Mexico and Monterey Bay, with concentrations of chlorophyll-a ranging from 0.07 to 50 mg/m(3), comparisons are made for the total absorption coefficients derived with the suggested method and those derived with diffuse attenuation coefficients. For these coastal to open-ocean waters, including regions of upwelling and the Loop Current, the results are as follows: at 440 nm the difference between the two methods is 13.0% (r(2) = 0.96) for total absorption coefficients ranging from 0.02 to 2.0 m(-1); at 488 nm the difference is 14.5% (r(2) = 0.97); and at 550 nm the difference is 13.6% (r(2) = 0.96). The results indicate that the method presented works very well for retrieval of in-water absorption coefficients exclusively from remotely measured signals, and that this method has a wide range of potential applications in oceanic remote sensing.     

iView: a feature clustering framework for suggesting informative views in volume visualization

The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.     

Virtual worlds as knowledge management platform – a practice‐perspective

Virtual worlds, as electronic environments where individuals can interact in a realistic manner in form of avatars, are increasingly used by gamers, consumers and employees. Therefore, they provide opportunities for reinventing business processes. Especially, effective knowledge management (KM) requires the use of appropriate information and communication technology (ICT) as well as social interaction. Emerging virtual worlds enable new ways to support knowledge and knowing processes because these virtual environments consider social aspects that are necessary for knowledge creating and knowledge sharing processes. Thus, collaboration in virtual worlds resembles real‐life activities. In this paper, we shed light on the use of Second Life (SL) as a KM platform in a real‐life setting. To explore the potential and current usage of virtual worlds for knowledge and knowing activities, we conducted a qualitative study at IBM. We interviewed IBM employees belonging to a special workgroup called ‘Web 2.0/virtual worlds’ in order to gain experience in generating and exchanging knowledge by virtually collaborating and interacting. Our results show that virtual worlds – if they are able to overcome problems like platform stability, user interface or security issues – bear the potential to serve as a KM platform. They facilitate global and simultaneous interaction, create a common context for collaboration, combine different tools for communication and enhance knowledge and knowing processes.     

Making DRAM refresh predictable

Embedded control systems with hard real-time constraints require that deadlines are met at all times or the system may malfunction with potentially catastrophic consequences. Schedulability theory can assure deadlines for a given task set when periods and worst-case execution times (WCETs) of tasks are known. While periods are generally derived from the problem specification, a task??s code needs to be statically analyzed to derive safe and tight bounds on its WCET. Such static timing analysis abstracts from program input and considers loop bounds and architectural features, such as pipelining and caching. However, unpredictability due to dynamic memory (DRAM) refresh cannot be accounted for by such analysis, which limits its applicability to systems with static memory (SRAM). In this paper, we assess the impact of DRAM refresh on task execution times and demonstrate how predictability is adversely affected leading to unsafe hard real-time system design. We subsequently contribute a novel and effective approach to overcome this problem through software-initiated DRAM refresh. We develop (1)?a?pure software and (2)?a?hybrid hardware/software refresh scheme. Both schemes provide predictable timings and fully replace the classical hardware auto-refresh. We discuss implementation details based on this design for multiple concrete embedded platforms and experimentally assess the benefits of different schemes on these platforms. We further formalize the integration of variable latency memory references into a data-flow framework suitable for static timing analysis to bound a task??s memory latencies with regard to their WCET. The resulting predictable execution behavior in the presence of DRAM refresh combined with the additional benefit of reduced access delays is unprecedented, to the best of our knowledge.