Smart chemical sensor application of ZnO nanowires grown on CMOS compatible SOI microheater platform

Smart chemical sensor based on CMOS(complementary metal-oxide- semiconductor) compatible SOI(silicon on insulator) microheater platform was realized by facilitating ZnO nanowires growth on the small membrane at the relatively low temperature. Our SOI microheater platform can be operated at the very low power consumption with novel metal oxide sensing materials, like ZnO or SnO2 nanostructured materials which demand relatively high sensing temperature. In addition, our sol-gel growth method of ZnO nanowires on the SOI membrane was found to be very effective compared with ink-jetting or CVD growth techniques. These combined techniques give us the possibility of smart chemical sensor technology easily merged into the conventional semiconductor IC application. The physical properties of ZnO nanowire network grown by the solution-based method and its chemical sensing property also were reported in this paper.

Estimation of fishery resources by sonar surveys

The methodology adopted by the Pelagic Fishery Project, Cochin for surveying the surface fish schools with sonar equipment, is described. The estimates of the biomass arrived at from the surveys conducted during 1975-78 are presented.

Auto-calibration of a camera system using Image Alignment

Calibration of a camera system is a necessary step in any stereo metric process. It correlates all cameras to a common coordinate system by measuring the intrinsic and extrinsic parameters of each camera. Currently, manual calibration of a camera system is the only way to achieve calibration in civil engineering operations that require stereo metric processes (photogrammetry, videogrammetry, vision based asset tracking, etc). This type of calibration however is time-consuming and labor-intensive. Furthermore, in civil engineering operations, camera systems are exposed to open, busy sites. In these conditions, the position of presumably stationary cameras can easily be changed due to external factors such as wind, vibrations or due to an unintentional push/touch from personnel on site. In such cases manual calibration must be repeated. In order to address this issue, several self-calibration algorithms have been proposed. These algorithms use Projective Geometry, Absolute Conic and Kruppa Equations and variations of these to produce processes that achieve calibration. However, most of these methods do not consider all constraints of a camera system such as camera intrinsic constraints, scene constraints, camera motion or varying camera intrinsic properties. This paper presents a novel method that takes all constraints into consideration to auto-calibrate cameras using an image alignment algorithm originally meant for vision based tracking. In this method, image frames are taken from cameras. These frames are used to calculate the fundamental matrix that gives epipolar constraints. Intrinsic and extrinsic properties of cameras are acquired from this calculation. Test results are presented in this paper with recommendations for further improvement.

700V smart trench IGBT with monolithic over-voltage and over-current protecting functions

An advanced 700V Smart Trench IGBT with monolithically integrated over-voltage and over-current protecting circuits is presented in this paper. The proposed Smart IGBT comprises a sense IGBT, a low voltage lateral n-channel MOSFET (M 1), an avalanche diode (D av), and poly-crystalline Zener diodes (ZD) and resistor (R poly). Mix-mode transient simulations with MEDICI have proven the functionalities of the protecting circuits when the device is operating under abnormal conditions, such as Unclamped Inductive Switching (UIS) and Short Circuit (SC) condition. A Trench IGBT process is used to fabricate this device with total 11 masks including one metal mask only. The characterizations of the fabricated device exhibit the clamping capability of the avalanche diode and voltage pull-down ability of the MOSFET. © 2012 IEEE.

Comparison of Camera Motion Estimation Methods for 3D Reconstruction of Infrastructure

Camera motion estimation is one of the most significant steps for structure-from-motion (SFM) with a monocular camera. The normalized 8-point, the 7-point, and the 5-point algorithms are normally adopted to perform the estimation, each of which has distinct performance characteristics. Given unique needs and challenges associated to civil infrastructure SFM scenarios, selection of the proper algorithm directly impacts the structure reconstruction results. In this paper, a comparison study of the aforementioned algorithms is conducted to identify the most suitable algorithm, in terms of accuracy and reliability, for reconstructing civil infrastructure. The free variables tested are baseline, depth, and motion. A concrete girder bridge was selected as the "test-bed" to reconstruct using an off-the-shelf camera capturing imagery from all possible positions that maximally the bridge's features and geometry. The feature points in the images were extracted and matched via the SURF descriptor. Finally, camera motions are estimated based on the corresponding image points by applying the aforementioned algorithms, and the results evaluated.

Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.

Generating the Sparse Point Cloud of a Civil Infrastructure Scene Using a Single Video Camera under Practical Constraints

Automating the model generation process of infrastructure can substantially reduce the modeling time and cost. This paper presents a method to generate a sparse point cloud of an infrastructure scene using a single video camera under practical constraints. It is the first step towards establishing an automatic framework for object-oriented as-built modeling. Motion blur and key frame selection criteria are considered. Structure from motion and bundle adjustment are explored. The method is demonstrated in a case study where the scene of a reinforced concrete bridge is videotaped, reconstructed, and metrically validated. The result indicates the applicability, efficiency, and accuracy of the proposed method.

Three-Dimensional Tracking of Construction Resources using an On-Site Camera System

Vision trackers have been proposed as a promising alternative for tracking at large-scale, congested construction sites. They provide the location of a large number of entities in a camera view across frames. However, vision trackers provide only two-dimensional (2D) pixel coordinates, which are not adequate for construction applications. This paper proposes and validates a method that overcomes this limitation by employing stereo cameras and converting 2D pixel coordinates to three-dimensional (3D) metric coordinates. The proposed method consists of four steps: camera calibration, camera pose estimation, 2D tracking, and triangulation. Given that the method employs fixed, calibrated stereo cameras with a long baseline, appropriate algorithms are selected for each step. Once the first two steps reveal camera system parameters, the third step determines 2D pixel coordinates of entities in subsequent frames. The 2D coordinates are triangulated on the basis of the camera system parameters to obtain 3D coordinates. The methodology presented in this paper has been implemented and tested with data collected from a construction site. The results demonstrate the suitability of this method for on-site tracking purposes.

ℓasso MPC: Smart regulation of over-actuated systems

In this paper, a novel MPC strategy is proposed, and referred to as asso MPC. The new paradigm features an 1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. This cost choice is motivated by the successful development of LASSO theory in signal processing and machine learning. In the latter fields, sum-of-norms regularisation have shown a strong capability to provide robust and sparse solutions for system identification and feature selection. In this paper, a discrete-time dual-mode asso MPC is formulated, and its stability is proven by application of standard MPC arguments. The controller is then tested for the problem of ship course keeping and roll reduction with rudder and fins, in a directional stochastic sea. Simulations show the asso MPC to inherit positive features from its corresponding regressor: extreme reduction of decision variables' magnitude, namely, actuators' magnitude (or variations), with a finite energy error, being particularly promising for over-actuated systems. © 2012 AACC American Automatic Control Council).

An integrated service-device-technology roadmap for smart city development

Firms and other organizations use Technology Roadmapping (TRM) extensively as a framework for supporting research and development of future technologies and products that could sustain a competitive advantage. While the importance of technology strategy has received more attention in recent years, few research studies have examined how roadmapping processes are used to explore the potential convergence of products and services that may be developed in the future. The aim of this paper is to introduce an integrated roadmapping process for services, devices and technologies capable of implementing a smart city development R&D project in Korea. The paper applies a QFD (Quality Function Deployment) method to establish interconnections between services and devices, and between devices and technologies. The method is illustrated by a detailed case study, which shows how different types of roadmap can be coordinated with each other to produce a clear representation of the technological changes and uncertainties associated with the strategic planning of complex innovations. © 2012 Elsevier Inc.