A machine vision extension for the Ruby programming language

Dynamically typed scripting languages have become popular in recent years. Although interpreted languages allow for substantial reduction of software development time, they are often rejected due to performance concerns. In this paper we present an extension for the programming language Ruby, called HornetsEye, which facilitates the development of real-time machine vision algorithms within Ruby. Apart from providing integration of crucial libraries for input and output, HornetsEye provides fast native implementations (compiled code) for a generic set of array operators. Different array operators were compared with equivalent implementations in C++. Not only was it possible to achieve comparable real-time performance, but also to exceed the efficiency of the C++ implementation in several cases. Implementations of several algorithms were given to demonstrate how the array operators can be used to create concise implementations.</p

Vision Based Pay-as-you-throw system: motion detection for garbage and recycle - based on faster RCNN

This project implemented the motion detection and tracking, the goal of project is detection, tracking and counting the different types garbage. The Faster Region Convolutional Neural Network (Faster RCNN or FRCNN) is used as classi�er and detector. Because the count for garbage or recycle is the �nal result, therefore, the tracker is developed based Kalman �filte

PENGAPLIKASIAN SENSOR WARNA PADA NAVIGASI LINE TRACKING ROBOT SAMPAH BERBASIS MIKROKONTROLER

Static garbage boxes, which cannot move to the location where people want to dispose the garbage, is one of the reasons why people tend to litter.  This static garbage box tends not to save energy and time of people who will dispose the garbage. In this research, a concept of handling waste using garbage robots is offered.  This garbage robot is designed to move towards the location of people who want to dispose of trash.  This robot can run automatically.  Another advantage of using this garbage robot is the navigation of the robot that uses line tracking so that the robot's way to the garbage location can be arranged.  Garbage locations are distinguished by color, while robot motion systems are based on line sensors as line track detectors.  Using the robot that can go to the location, people who will dispose of the garbage can save more energy and timeAbstrak  Kotak sampah statis, yang belum bisa bergerak menuju tempat orang yang ingin membuang ‎sampah, merupakan salah satu sebab orang cenderung membuang sampah ‎sembarangan.  Hal ini diakrenakan kotak sampah statis tidak menghemat ‎energi dan waktu orang yang akan membuang sampah. Pada penelitian ini, ditawarkan sebuah konsep penanggulangan sampah ‎menggunakan robot sampah.  Robot sampah ini dirancang dapat bergerak menuju lokasi ‎orang yang ingin membuang sampah. Robot ini dapat berjalan secara otomatis. ‎Keuntungan lain dari penggunaan robot sampah ini adalah navigasi yang ‎menggunakan line tracking sehingga jalannya robot menuju ‎lokasi sampah dapat tertata. Lokasi sampah dibedakan berdasarkan warna, sedangkan sistem gerak robot berbasis sensor garis sebagai pendeteksi line track. Dengan adanya robot sampah yang bisa menuju lokasi, orang yang akan membuang sampah dapat lebih menghemat ‎energi dan waktu.  ‎ Kata kunci : Robot sampah, Sensor warna, Line Tracking

Incremental copying garbage collection for WAM-based Prolog systems

The design and implementation of an incremental copying heap garbage collector for WAM-based Prolog systems is presented. Its heap layout consists of a number of equal-sized blocks. Other changes to the standard WAM allow these blocks to be garbage collected independently. The independent collection of heap blocks forms the basis of an incremental collecting algorithm which employs copying without marking (contrary to the more frequently used mark&copy or mark&slide algorithms in the context of Prolog). Compared to standard semi-space copying collectors, this approach to heap garbage collection lowers in many cases the memory usage and reduces pause times. The algorithm also allows for a wide variety of garbage collection policies including generational ones. The algorithm is implemented and evaluated in the context of hProlog.Comment: 33 pages, 22 figures, 5 tables. To appear in Theory and Practice of Logic Programming (TPLP

Tracking Bacterial Pollution Sources in Hampton Harbor

Fecal-borne microorganisms impact many shellfish-growing waters in coastal New Hampshire. Watersheds are often subject to fecal contamination by a variety of sources and efforts to improve water quality are often limited because of lack of information on which contaminant sources are most significant. Ribotyping and other microbial source tracking methods are useful new tools for providing information on the sources of fecal-borne bacterial contaminants in surface waters. New Hampshire has areas of abundant oyster (Crassostrea virginica) and clam (Mya arenaria) resources, the latter being most important in Hampton Harbor. In this study, Escherichia coli isolates (bacteria colonies) were obtained from water samples collected from ten sites in Hampton Harbor year-round during both dry and wet conditions. A library of known E. coli isolates was created from twenty different potential source species in the New Hampshire coastal watershed, including humans, livestock, pets, wildlife and avian species. The ribosomal RNA DNA of E. coli isolates was analyzed using ribotyping in which the patterns of ribosomal DNA were detected using chemiluminescence, then optimized and analyzed using GelCompar II software. A total of 249 isolates from the twenty known source species were used as a reference to identify sources for 390 unknown isolates from water samples taken from August 2000 through October 2001. Banding patterns for water samples and source species isolates were considered to be the same if there was 80% or greater similarity between patterns. Overall, sources for 62% of the isolates were identified

Beltway: Getting Around Garbage Collection Gridlock

We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors