The Asia-Pacific IT and high technology sector has held up well over the past few years, accounting for a greater share of the global ICT market.A Mergent analysis shows the market capitalization of the top nine major Asia-Pacific IT companies tracked totaled US$179.45 billion, as of March 20, 2013.Although semiconductor sales in 2012 dropped by 1.13% to US$162.6 billion, the outlook is encouraging as emerging Asian economies are attractive for semiconductor consumption due to strong demand.IT companies have turned to other industries to offset losses from hardware sales and to lessen the reliance on consumer electronics due to the shift toward mobile devices.Accelerating investment is led by the Asia-Pacific, where most companies have invested more in new technologies than their European counterparts.

The electronic security industry hosts a gamut of products which are primarily used for monitoring or surveillance, intrusion detection, fire of Carbon Monoxide detection, safe access, contraband detection and others.It also entails a variety of products designed to meet the specific requirements of the homes, commercial spaces such as offices and complexes, public places such as malls and large industries and institutions.The industry draws its revenues primarily from the sales,installation and replacement markets.Additionally,some of the electronic security systems also involve monitoring expenses which are a major source of the increasing recurring monthly revenue(RMR) of the dealers and integrators.

For emerging safety-critical systems, novel design methodologies are becoming necessary to cope with early stage design validation, performance and timing prediction, and design space exploration. In this paper, we propose a tool integration technique for architectural exploration of an aircraft electric power system (EPS) controller using Ptolemy II and Metro II to satisfy requirements imposed on safety-critical system design. The functional model of a newly suggested co-simulation environment is implemented with Ptolemy II and the model for architectural exploration is realized by SystemC. To construct the co-simulation environment and combine the functional model and the architectural model, Metro II semantics is employed. We verify effectiveness and extensibility of our new approach using experiments and results with example candidates for the aircraft EPS controller.

We consider distributed memory algorithms for the all-pairs shortest paths (APSP) problem. Scaling the APSP problem to high concurrencies requires both minimizing inter-processor communication as well as maximizing temporal data locality.The 2.5D APSP algorithm, which is based on the divide-andconquer paradigm, satisfies both of these requirements: it can utilize any extra available memory to perform asymptotically less communication, and it is rich in semiring matrix multiplications,which have high temporal locality. We start by introducing a block-cyclic 2D (minimal memory) APSP algorithm. With a careful choice of block-size, this algorithm achieves known communication lower-bounds for latency and bandwidth. We extend this 2D block-cyclic algorithm to a 2.5D algorithm, which can use c extra copies of data to reduce the bandwidth cost by a factor of c1=2, compared to its 2D counterpart. However, the 2.5D algorithm increases the latency cost by c1=2. We provide a tighter lower bound on latency, which dictates that the latency overhead is necessary to reduce bandwidth along the critical path of execution. Our implementation achieves impressive performance and scaling to 24,576 cores of a Cray XE6 supercomputer by utilizing well-tuned intra-node kernels within the distributed memory algorithm.

With the arrival of the Microsoft Kinect, obtaining depth maps of interior spaces has become remarkably easy. The Kinect is equipped with an 8-bit RGB VGA resolution (640x480 pixel) video camera, and also features an IR-triangulation based depth sensor with reports of accuracy within q(z) = 2:73z2 + 0:74z 0:58[mm], with z the depth in meters. The Kinect's low cost and portability make it an attractive instrument for robotics and mapping. We have witnessed a boon of large datasets originating from such Kinect-style cameras, and an associated development in algorithms for SLAM-like tasks. While the potential for this data is vast, one immediate application is incorporating the depth data into a more robust object detector.The remainder of this report is organized as follows: Part 2 describes previous work done in the area of object recognition and depth data. Part 3 describes the dataset. Part 4 gives our approach to the problem. Part 5 gives results of our method. Part 6 provides closing remarks.

What if robots and automation systems were not limited by onboard computation, memory, or programming? This is now practical with wireless networking and rapidly expanding Internet resources. In 2010, James Kuffner at Google introduced the term “Cloud Robotics" to describe a new approach to robotics that takes advantage of the Internet as a resource for massively parallel computation and real time sharing of vast data resources. The Google autonomous driving project exemplifes this approach: the system indexes maps and images that are collected and updated by satellite, Streetview, and crowdsourcing from the network to facilitate accurate localization. Another example is Kiva Systems new approach to warehouse automation and logistics using large numbers of mobile platforms to move pallets using a local network to coordinate planforms and update tracking data. These are just two new projects that build on resources from the Cloud. Steve Cousins of Willow Garage aptly summarized the idea: “No robot is an island." Cloud Robotics recognizes the wide availability of networking, incorporates elements of open-source, open-access, and crowdsourcing to greatly extend earlier concepts of “Online Robots" and “Networked Robots". Cloud Robotics has potential to improve robot performance in at least five ways: 1) Big Data: indexing a global library of images, maps, and object data, 2) Cloud Computing: parallel grid computing on demand for statistical analysis, learning, and motion planning, 3) Open-Source / Open-Access: humans sharing code, data, algorithms, and hardware designs, 4) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes, and 5) Crowdsourcing and call centers: offline and on-demand human guidance for evaluation, learning, and error recovery. This article surveys related work as of Fall 2012.

In this paper, we present an automated technique SWATI: Synthesizing Wordlengths Automatically Using Testing and Induction, which uses a combination of Nelder-Mead optimization based testing, and induction from examples to automatically synthesize optimal fixedpoint implementation of numerical routines. The design of numerical software is commonly done using floating-point arithmetic in design-environments such as Matlab. However, these designs are often implemented using fixed-point arithmetic for speed and efficiency reasons especially in embedded systems. The fixed-point implementation reduces implementation cost, provides better performance, and reduces power consumption. The conversion from floating-point designs to fixed-point code is subject to two opposing constraints: (i) the word-width of fixed-point types must be minimized, and (ii) the outputs of the fixed-point program must be accurate. In this paper, we propose a new solution to this problem. Our technique takes the floating-point program, specified accuracy and an implementation cost model and provides the fixed-point program with specified accuracy and optimal implementation cost. We demonstrate the effectiveness of our approach on a set of examples from the domain of automated control, robotics and digital signal processing.