Final report of the baseline data reconstruction for the Quality Assurance for Fish Marketing Project

A baseline survey for the project which had been conducted in 2009 had gaps that could not allow assessment of project performance in the outcome and impact indicators to be made. This study was, therefore, commissioned to reconstruct the baseline data, aligned to the impact and outcome indicators on the project logframe and results framework, against which project achievements could be assessed. The purpose and scope of the study was to reconstruct the baseline data and analysis describing the situation prior to QAFM Project inception, taking 2008 as the baseline year, which was aligned to the project logframe outcome and impact indicators; to collect data on current status to compare project outcome (and where possible impact) in improved fish handling sites in comparison with the baseline as well as with comparable non-improved fish landing sites as control group. The study was conducted through secondary data search from sources at NaFIRRI, DFR and ICEIDA. Field data collection was carried out using a sample survey covering 312 respondents including boat and gear owners, crew members, processors and traders at eight project and two control landing sites. Key Informant Interviews were conducted with DFOs and BMU leaders in the study districts and landing sites respectively.

Enticing general engineering undergrads into signal processing with Photo-Editing and image search

The University of Cambridge is unusual in that its Department of Engineering is a single department which covers virtually all branches of engineering under one roof. In their first two years of study, our undergrads study the full breadth of engineering topics and then have to choose a specialization area for the final two years of study. Here we describe part of a course, given towards the end of their second year, which is designed to entice these students to specialize in signal processing and information engineering topics for years 3 and 4. The course is based around a photo editor and an image search application, and it requires no prior knowledge of the z-transform or of 2-dimensional signal processing. It does assume some knowledge of 1-D convolution and basic Fourier methods and some prior exposure to Matlab. The subject of this paper, the photo editor, is written in standard Matlab m-files which are fully visible to the students and help them to see how specific algorithms are implemented in detail. © 2011 IEEE.

Fast L0-based sparse signal recovery

This paper develops an algorithm for finding sparse signals from limited observations of a linear system. We assume an adaptive Gaussian model for sparse signals. This model results in a least square problem with an iteratively reweighted L2 penalty that approximates the L0-norm. We propose a fast algorithm to solve the problem within a continuation framework. In our examples, we show that the correct sparsity map and sparsity level are gradually learnt during the iterations even when the number of observations is reduced, or when observation noise is present. In addition, with the help of sophisticated interscale signal models, the algorithm is able to recover signals to a better accuracy and with reduced number of observations than typical L1-norm and reweighted L1 norm methods. ©2010 IEEE.

Live 3D shape reconstruction, recognition and registration

We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.

An error free passive UHF RFID system using a new form of wireless signal distribution

A wide area and error free ultra high frequency (UHF) radio frequency identification (RFID) interrogation system based on the use of multiple antennas used in cooperation to provide high quality ubiquitous coverage, is presented. The system uses an intelligent distributed antenna system (DAS) whereby two or more spatially separated transmit and receive antenna pairs are used to allow greatly improved multiple tag identification performance over wide areas. The system is shown to increase the read accuracy of 115 passive UHF RFID tags to 100% from <60% over a 10m × 8m open plan office area. The returned signal strength of the tag backscatter signals is also increased by an average of 10dB and 17dB over an area of 10m 8m and 10m × 4m respectively. Furthermore, it is shown that the DAS RFID system has improved immunity to tag orientation. Finally, the new system is also shown to increase the tag read speed/rate of a population of tags compared with a conventional RFID system. © 2012 IEEE.

Progressive 3D reconstruction of infrastructure with videogrammetry

A number of methods are commonly used today to collect infrastructure's spatial data (time-of-flight, visual triangulation, etc.). However, current practice lacks a solution that is accurate, automatic, and cost-efficient at the same time. This paper presents a videogrammetric framework for acquiring spatial data of infrastructure which holds the promise to address this limitation. It uses a calibrated set of low-cost high resolution video cameras that is progressively traversed around the scene and aims to produce a dense 3D point cloud which is updated in each frame. It allows for progressive reconstruction as opposed to point-and-shoot followed by point cloud stitching. The feasibility of the framework is studied in this paper. Required steps through this process are presented and the unique challenges of each step are identified. Results specific to each step are also presented.

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.

Comparison of Image-Based and Time-of-Flight-Based Technologies for 3D Reconstruction of Infrastructure

Image-based (i.e., photo/videogrammetry) and time-of-flight-based (i.e., laser scanning) technologies are typically used to collect spatial data of infrastructure. In order to help architecture, engineering, and construction (AEC) industries make cost-effective decisions in selecting between these two technologies with respect to their settings, this paper makes an attempt to measure the accuracy, quality, time efficiency, and cost of applying image-based and time-of-flight-based technologies to conduct as-built 3D reconstruction of infrastructure. In this paper, a novel comparison method is proposed, and preliminary experiments are conducted. The results reveal that if the accuracy and quality level desired for a particular application is not high (i.e., error < 10 cm, and completeness rate > 80%), image-based technologies constitute a good alternative for time-of-flight-based technologies and significantly reduce the time and cost needed for collecting the data on site.