Precise control of reflection response in bandwidth‐enhanced planar antennas

In this work, the issues of bandwidth enhancement of planar antennas and the relevance of precise and automated response control through numerical optimization have been investigated. Using an example of a planar antenna with parasitic radiator we illustrate possible effects of even minor modifications of the antenna geometry (here, applied to the ground plane) on its reflection performance. In particular, a proper handling of geometry parameters may lead to considerable broadening of the antenna bandwidth. For the sake of computational efficiency, the adjustment of geometry parameters is carried out using surrogate‐based optimization methods exploiting coarse‐discretization EM simulations as the underlying low‐fidelity antenna model. Additionally, suitably defined penalty function allows us to precisely control the maximum in‐band reflection so that sufficient margin to accommodate possible manufacturing tolerances can be achieved. The optimized designs of the two antenna structures considered in this work exhibit over 1.75 GHz (>31%) and 2.15 GHz (>38%) bandwidth, respectively, for the center frequency of 5.6 GHz. Simulation results are validated using measurements of the fabricated prototypes. Comparison with state‐of‐the‐art designs is also provided. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:653–659, 2016.     

AmbiKraf

This paper presents, AmbiKraf, a non-emissive fabric display that subtly animates patterns on common fabrics. We use thermochromic inks and peltier semiconductor elements to achieve this technology. With this technology we have produced numerous prototypes from animated wall paintings to pixilated fabric displays. The ability of this technology to subtly and ubiquitously change the color of the fabric itself has made us able to merge different fields and technologies with AmbiKraf. In addition, with an animated room divider screen, Ambikraf merged its technology with Japanese Byobu art to tighten the gap between traditional arts and contemporary technologies. Through this AmbiKraf Byobu art installation and other installations, we discuss the impact of this technology as a ubiquitous fabric display. With focus to improvements of some limitations of the existing system, we present our future vision that enables us to merge this technology into more applications fields thus making this technology a platform for ubiquitous interactions on our daily peripherals.     

Probabilistic motion-compensated prediction in distributed video coding

Distributed video coding (DVC) constitutes an original coding framework to meet the stringent requirements imposed by uplink-oriented and low-power mobile video applications. The quality of the side information available to the decoder and the efficiency of the employed channel codes are primary factors determining the success of a DVC system. This contribution introduces two novel techniques for probabilistic motion compensation in order to generate side information at the Wyner-Ziv decoder. The employed DVC scheme uses a base layer, serving as a hash to facilitate overlapped block motion estimation at the decoder side. On top of the base layer, a supplementary Wyner-Ziv layer is coded in the DCT domain. Both proposed probabilistic motion compensation techniques are driven by the actual correlation channel statistics and reuse information contained in the hash. Experimental results report significant rate savings caused by the novel side information generation methods compared to previous techniques. Moreover, the compression performance of the presented DVC architecture, featuring the proposed side-information generation techniques, delivers state-of-the-art compression performance.     

High temperature field effect hydrogen and hydrocarbon gas sensors based on SiC MOS devices

The growing need for reliable, efficient, high temperature hydrogen and hydrocarbon monitoring has fueled research into novel structures for gas sensing. Metal oxide semiconductor (MOS) devices employing a catalytic metal layer have emerged as one of the leading sensing platforms for such applications, owing to their high sensitivity and inherent capability for signal amplification. The limited operating temperature of such devices employing silicon as the semiconductor has led research efforts to focus on replacing them with devices based on silicon carbide (SiC). More recently, MOS devices having different oxide layers exhibiting improved sensing performance have emerged. Considering the amount of research that has been carried out in this area in recent times, it is important to elucidate the new findings and the gas interaction mechanisms that have been ascribed to such devices, and bring together several theories proposed by different research groups. In this paper we first highlight the needs which have driven research into SiC based field effect hydrogen and hydrocarbon sensors, illustrate the various structures being investigated, and describe the device evolution and current status. We provide several sensing examples of devices that make use of different oxide layers and demonstrate how their electrical properties change in the presence of the gases, as well as presenting the hydrogen gas interaction mechanisms of these sensors.     

Uncertainty quantification and apportionment in air quality models using the polynomial chaos method

Current air quality models generate deterministic forecasts by assuming perfect model, perfectly known parameters, and exact input data. However, our knowledge of the physics is imperfect. It is of interest to extend the deterministic simulation results with “error bars” that quantify the degree of uncertainty, and analyze the impact of the uncertainty input on the simulation results. This added information provides a confidence level for the forecast results. Monte Carlo (MC) method is a popular approach for air quality model uncertainty analysis, but it converges slowly. This work discusses the polynomial chaos (PC) method that is more suitable for uncertainty quantification (UQ) in large-scale models. We propose a new approach for uncertainty apportionment (UA), i.e., we develop a PC approach to attribute the uncertainties in model results to different uncertainty inputs. The UQ and UA techniques are implemented in the Sulfur Transport Eulerian Model (STEM-III). A typical scenario of air pollution in the northeast region of the USA is considered. The UQ and UA results allow us to assess the combined effects of different input uncertainties on the forecast uncertainty. They also enable to quantify the contribution of input uncertainties to the uncertainty in the predicted ozone and PAN concentrations.     

A framework for implementing robotic process automation projects

Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.

     

Beyond loop bounds: comparing annotation languages for worst-case execution time analysis

Worst-case execution time (WCET) analysis is concerned with computing a precise-as-possible bound for the maximum time the execution of a program can take. This information is indispensable for developing safety-critical real-time systems, e. g., in the avionics and automotive fields. Starting with the initial works of Chen, Mok, Puschner, Shaw, and others in the mid and late 1980s, WCET analysis turned into a well-established and vibrant field of research and development in academia and industry. The increasing number and diversity of hardware and software platforms and the ongoing rapid technological advancement became drivers for the development of a wide array of distinct methods and tools for WCET analysis. The precision, generality, and efficiency of these methods and tools depend much on the expressiveness and usability of the annotation languages that are used to describe feasible and infeasible program paths. In this article we survey the annotation languages which we consider formative for the field. By investigating and comparing their individual strengths and limitations with respect to a set of pivotal criteria, we provide a coherent overview of the state of the art. Identifying open issues, we encourage further research. This way, our approach is orthogonal and complementary to a recent approach of Wilhelm et al. who provide a thorough survey of WCET analysis methods and tools that have been developed and used in academia and industry.     

Issues in sampling and estimating continuous-time models with stochastic disturbances

The standard continuous time state space model with stochastic disturbances contains the mathematical abstraction of continuous time white noise. To work with well defined, discrete time observations, it is necessary to sample the model with care. The basic issues are well known, and have been discussed in the literature. However, the consequences have not quite penetrated the practice of estimation and identification. One example is that the standard model of an observation, being a snapshot of the current state plus noise independent of the state, cannot be reconciled with this picture. Another is that estimation and identification of time continuous models require a more careful treatment of the sampling formulas. We discuss and illustrate these issues in the current contribution. An application of particular practical importance is the estimation of models based on irregularly sampled observations.     

Animatronic shader lamps avatars

Applications such as telepresence and training involve the display of real or synthetic humans to multiple viewers. When attempting to render the humans with conventional displays, non-verbal cues such as head pose, gaze direction, body posture, and facial expression are difficult to convey correctly to all viewers. In addition, a framed image of a human conveys only a limited physical sense of presence—primarily through the display’s location. While progress continues on articulated robots that mimic humans, the focus has been on the motion and behavior of the robots rather than on their appearance. We introduce a new approach for robotic avatars of real people: the use of cameras and projectors to capture and map both the dynamic motion and the appearance of a real person onto a humanoid animatronic model. We call these devices animatronic Shader Lamps Avatars (SLA). We present a proof-of-concept prototype comprised of a camera, a tracking system, a digital projector, and a life-sized styrofoam head mounted on a pan-tilt unit. The system captures imagery of a moving, talking user and maps the appearance and motion onto the animatronic SLA, delivering a dynamic, real-time representation of the user to multiple viewers.