A fully integrated 1‐in. AMOLED display using current feedback based on a five‐mask LTPS CMOS process

Abstract— In the past, a five‐mask LTPS CMOS process requiring only one single ion‐doping step was used. Based on that process, all necessary components for the realization of a fully integrated AMOLED display using a 3T1C current‐feedback pixel circuit has recently been developed. The integrated data driver is based on a newly developed LTPS operational amplifier, which does not require any compensation for Vth or mobility variations. Only one operational amplifier per column is used to perform digital‐to‐analog conversion as well as current control. In order to achieve high‐precision analog behavior, the operational amplifier is embedded in a switched capacitor network. In addition to circuit verification by simulation and analytic analysis, a 1‐in. fully integrated AMOLED demonstrator was successfully built. To the best of the authors' knowledge, this is the first implementation of a fully integrated AMOLED display with current feedback.     

Physics-Based Person Tracking Using the Anthropomorphic Walker

We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degree-of-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and spring-like forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset.     

Design and implementation of the Node Identity Internetworking Architecture

The Internet Protocol (IP) has been proven very flexible, being able to accommodate all kinds of link technologies and supporting a broad range of applications. The basic principles of the original Internet architecture include end-to-end addressing, global routeability and a single namespace of IP addresses that unintentionally serves both as locators and host identifiers. The commercial success and widespread use of the Internet have lead to new requirements, which include Internetworking over business boundaries, mobility and multi-homing in an untrusted environment. Our approach to satisfy these new requirements is to introduce a new Internetworking layer, the node identity layer. Such a layer runs on top of the different versions of IP, but could also run directly on top of other kinds of network technologies, such as MPLS and 2G/3G PDP contexts. This approach enables connectivity across different communication technologies, supports mobility, multi-homing, and security from ground up. This paper describes the Node Identity Architecture in detail and discusses the experiences from implementing and running a prototype.     

A biased random‐key genetic algorithm for OSPF and DEFT routing to minimize network congestion

Interior gateway routing protocols like Open Shortest Path First (OSPF) and Distributed Exponentially Weighted Flow Splitting (DEFT) send flow through forward links toward the destination node. OSPF routes only on shortest‐weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem (WSP). In this paper, we present a biased random‐key genetic algorithm for WSP using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.     

A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop

A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly suitable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.     

A simulation-based scheduling system for real-time optimization and decision making support

This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.     

Water diffusion in polymer nano-films measured with microcantilevers

We present a method to measure the absorption of water molecules from the liquid and the vapour phase into polymer nano-films and the diffusion inside these films. Film thickness can be down to 45 nm. To demonstrate the possibilities of this method we use polymer films that are deposited on the upper side of a silicon cantilever by plasma polymerization of norbornene. When a microdrop of water is deposited onto the initially straight cantilever, the drop causes the cantilever to bend while it evaporates. Evaporation of such small water drops usually takes less than a second. An upwards bending is due to capillary forces and a downwards bending is due to the diffusion of water into the polymer film – and the consequent volume expansion (swelling) of the film. The magnitude of the capillary forces and the extent of swelling continuously change during drop evaporation. When drop evaporation is over the cantilever returns to its initial straight position. We simulate the time dependent bending with a numerical model that qualitatively agrees with the experiment. From the time dependence of cantilever bending we are able to determine the diffusion coefficient of water in the thin polymer film.     

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.     

Instructional animations can be superior to statics when learning human motor skills

Based on the assumption of a working memory processor devoted to human movement, cognitive load theory is used to explore some conditions under which animated instructions are hypothesised to be more effective for learning than equivalent static graphics. Using paper-folding tasks dealing with human movement, results from three experiments confirmed our hypothesis, indicating a superiority of animation over static graphics. These results are discussed in terms of a working memory processor that may be facilitated by our mirror-neuron system and may explain why animated instructional animations are superior to static graphics for cognitively based tasks that involve human movement.     

Guided Image Filtering for Interactive High‐quality Global Illumination

Interactive computation of global illumination is a major challenge in current computer graphics research. Global illumination heavily affects the visual quality of generated images. It is therefore a key attribute for the perception of photo‐realistic images. Path tracing is able to simulate the physical behaviour of light using Monte Carlo techniques. However, the computational burden of this technique prohibits interactive rendering times on standard commodity hardware in high‐quality. Trying to solve the Monte Carlo integration with fewer samples results in characteristic noisy images. Global illumination filtering methods take advantage of the fact that the integral for neighbouring pixels may be very similar. Averaging samples of similar characteristics in screen‐space may approximate the correct integral, but may result in visible outliers. In this paper, we present a novel path tracing pipeline based on an edge‐aware filtering method for the indirect illumination which produces visually more pleasing results without noticeable outliers. The key idea is not to filter the noisy path traced images but to use it as a guidance to filter a second image composed from characteristic scene attributes that do not contain noise by default. We show that our approach better approximates the Monte Carlo integral compared to previous methods. Since the computation is carried out completely in screen‐space it is therefore applicable to fully dynamic scenes, arbitrary lighting and allows for high‐quality path tracing at interactive frame rates on commodity hardware.