Smart distance measurement module for football robot

Diplomová práce se zabývá vývojem dálkoměrného modulu určeného pro rozšíření senzorické výbavy fotbalového robotu kategorie MiroSot. Tento modul na vstupu přijímá data ze senzorické jednotky vyvinuté na Ústavu automatizace a měřicí techniky a z těchto dat extrahuje polohu míčku. Je srovnáno využití neuronové sítě a zjednodušené Houghovy transformace pro získání polohy těžiště míčku. V práci je popsána pomocná implementace funkcionality v prostředích MATLAB a C#.NET i hlavní implementace pro signálový mikrokontrolér Freescale MC56F8013. Výsledný modul splňuje nároky zadání a je plně funkční.The master's thesis concerns with the design of a distance measurement module destined for a MiroSot-category soccer robot. The module accepts data outputted by a sensor unit developed on Department of Control and Instrumentation and uses it to determine the ball position. Utilization of a neural network and a simplified Hough transform for ball finding is discussed. The thesis describes auxiliary implementations in MATLAB and C#.NET environments as well as the main implementation for digital signal controller Freescale MC56F8013. The resulting module meets requirements of the submission and is fully functional.

Computer vision reading on stickers and direct part marking on horticultural products : challenges and possible solutions

Traceability of products from production to the consumer has led to a technological advancement in product identification. There has been development from the use of traditional one-dimensional barcodes (EAN-13, Code 128, etc.) to 2D (two-dimensional) barcodes such as QR (Quick Response) and Data Matrix codes. Over the last two decades there has been an increased use of Radio Frequency Identification (RFID) and Direct Part Marking (DPM) using lasers for product identification in agriculture. However, in agriculture there are still considerable challenges to adopting barcodes, RFID and DPM technologies, unlike in industry where these technologies have been very successful. This study was divided into three main objectives. Firstly, determination of the effect of speed, dirt, moisture and bar width on barcode detection was carried out both in the laboratory and a flower producing company, Brandkamp GmbH. This study developed algorithms for automation and detection of Code 128 barcodes under rough production conditions. Secondly, investigations were carried out on the effect of low laser marking energy on barcode size, print growth, colour and contrast on decoding 2D Data Matrix codes printed directly on apples. Three different apple varieties (Golden Delicious, Kanzi and Red Jonaprince) were marked with various levels of energy and different barcode sizes. Image processing using Halcon 11.0.1 (MvTec) was used to evaluate the markings on the apples. Finally, the third objective was to evaluate both algorithms for 1D and 2D barcodes. According to the results, increasing the speed and angle of inclination of the barcode decreased barcode recognition. Also, increasing the dirt on the surface of the barcode resulted in decreasing the successful detection of those barcodes. However, there was 100% detection of the Code 128 barcode at the company’s production speed (0.15 m/s) with the proposed algorithm. Overall, the results from the company showed that the image-based system has a future prospect for automation in horticultural production systems. It overcomes the problem of using laser barcode readers. The results for apples showed that laser energy, barcode size, print growth, type of product, contrast between the markings and the colour of the products, the inertia of the laser system and the days of storage all singularly or in combination with each other influence the readability of laser Data Matrix codes and implementation on apples. There was poor detection of the Data Matrix code on Kanzi and Red Jonaprince due to the poor contrast between the markings on their skins. The proposed algorithm is currently working successfully on Golden Delicious with 100% detection for 10 days using energy 0.108 J mm-2 and a barcode size of 10 × 10 mm2. This shows that there is a future prospect of not only marking barcodes on apples but also on other agricultural products for real time production

Navigation for automatic guided vehicles using omnidirectional optical sensing

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013Automatic Guided Vehicles (AGVs) are being used more frequently in a manufacturing environment. These AGVs are navigated in many different ways, utilising multiple types of sensors for detecting the environment like distance, obstacles, and a set route. Different algorithms or methods are then used to utilise this environmental information for navigation purposes applied onto the AGV for control purposes. Developing a platform that could be easily reconfigured in alternative route applications utilising vision was one of the aims of the research. In this research such sensors detecting the environment was replaced and/or minimised by the use of a single, omnidirectional Webcam picture stream utilising an own developed mirror and Perspex tube setup. The area of interest in each frame was extracted saving on computational recourses and time. By utilising image processing, the vehicle was navigated on a predetermined route. Different edge detection methods and segmentation methods were investigated on this vision signal for route and sign navigation. Prewitt edge detection was eventually implemented, Hough transfers used for border detection and Kalman filtering for minimising border detected noise for staying on the navigated route. Reconfigurability was added to the route layout by coloured signs incorporated in the navigation process. The result was the manipulation of a number of AGV’s, each on its own designated coloured signed route. This route could be reconfigured by the operator with no programming alteration or intervention. The YCbCr colour space signal was implemented in detecting specific control signs for alternative colour route navigation. The result was used generating commands to control the AGV through serial commands sent on a laptop’s Universal Serial Bus (USB) port with a PIC microcontroller interface board controlling the motors by means of pulse width modulation (PWM). A total MATLAB® software development platform was utilised by implementing written M-files, Simulink® models, masked function blocks and .mat files for sourcing the workspace variables and generating executable files. This continuous development system lends itself to speedy evaluation and implementation of image processing options on the AGV. All the work done in the thesis was validated by simulations using actual data and by physical experimentation

On the autonomous detection of coronal mass ejections in heliospheric imager data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95523/1/jgra21843.pd

Ultrasound-guided Optical Techniques for Cancer Diagnosis: System and Algorithm Development

Worldwide, breast cancer is the most common cancer among women. In the United States alone, the American cancer society has estimated there will be 271,270 new breast cancer cases in 2019, and 42,260 lives will be lost to the disease. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) are regularly used for breast cancer diagnosis and therapy monitoring. However, they sometimes fail to diagnose breast cancer effectively. These shortcomings have motivated researchers to explore new modalities. One of these modalities, diffuse optical tomography (DOT), utilizes near-infrared (NIR) light to reveal the optical properties of tissue. NIR-based DOT images the contrast between a suspected lesion’s location and the background tissue, caused by the higher NIR absorption of the hemoglobin which characterizes tumors. The limitation of high light scattering inside tissue is minimized by using ultrasound image to find the tumor location. This thesis focuses on developing a compact, low-cost ultrasound guided diffuse optical tomography imaging system and on improving optical image reconstruction by extracting the tumor’s location and size from co-registered ultrasound images. Several electronic components have been redesigned and optimized to save space and cost and to improve the user experience. In terms of software and algorithm development, manual extraction of tumor information from ultrasound images has been replaced by using a semi-automated ultrasound image segmentation algorithm that reduces the optical image reconstruction time and operator dependency. This system and algorithm have been validated with phantom and clinical data and have demonstrated their efficacy. An ongoing clinical trial will continue to gather more patient data to improve the robustness of the imaging algorithm. Another part of this research focuses on ovarian cancer diagnosis. Ovarian cancer is the most deadly of all gynecological cancers, with a less than 50% five-year survival rate. This cancer can evolve without any noticeable symptom, which makes it difficult to diagnose in an early stage. Although ultrasound-guided photoacoustic tomography (PAT) has demonstrated potential for early detection of ovarian cancer, clinical studies have been very limited due to the lack of robust PAT systems. In this research, we have customized a commercial ultrasound system to obtain real-time co-registered PAT and US images. This system was validated with several phantom studies before use in a clinical trial. PAT and US raw data from 30 ovarian cancer patients was used to extract spectral and statistical features for training and testing classifiers for automatic diagnosis. For some challenging cases, the region of interest selection was improved by reconstructing co-registered Doppler images. This study will be continued in order to obtain quantitative tissue properties using US-guided PAT

An enhanced sensitivity procedure for continuous gravitational wave detection: targeting the Galactic Center

The recent announcement by the LIGO and Virgo Collaborations of the direct detection of gravitational waves started the era of gravitational wave astrophysics. Each of the GW events detected so far, shed light on multiple aspects of gravity. These last two years of great scientific discoveries would not have been possible without the constant work of generations of scientists all around the world. Commissioning and detector characterization activities required a lot of effort and manpower to reach the sensitivity level and stability needed for the detections. In fact, detector characterization activities continue also during data taking, providing important data quality information to data analysis groups. Although in few years several important results have been obtained, this is just the beginning. Indeed there are several other potential sources of gravitational waves not yet detected. In particular, the search for continuous gravitational waves, which are very weak but long and persistent signals, is a very active field. The most probable sources of continuous waves signals are rapidly rotating asymmetric neutron stars, both isolated or in binary systems. In this thesis I will summarize my 3 years PhD work done in the Rome Virgo group. The main subject is the search for gravitational waves signal emitted by isolated non-axisymmetric rotating neutron stars. After a short introduction to gravitational waves and to the principles of detection (Chapters 1 and 2), in Chapter 3 I will talk about my contribution to detector characterization activities, performed during Virgo commissioning and science runs. I will describe the role played by a spectral lines monitoring tool, called NoEMi (Noise Event Miner), developed by the Rome group in 2010, which I have been responsible for, during these 3 years. NoEMi has been used through O1 and O2 Observational runs and in the commissioning phase of LIGO and Virgo detectors. It has been also used for Virgo data validation of the two gravitational wave events GW170814 and GW170817 and it is currently used for the post-commissioning identification of instrumental lines in both LIGO and Virgo data. The second part of the Thesis is dedicated to the new data analysis framework I have developed in the context of continuous gravitational wave searches. It consists of a novel organization of the data, the so-called Band Sampled Data collection, and of several functions needed to efficiently operate on the data itself. This framework dramatically improves the flexibility in data handling, allowing the user to select and manipulate data in a very efficient way, by properly taking into account the characteristics and the needs of the specific type of search she/he is doing. Overall it results in better computational performance (which, at fixed available computing resources, means better search sensitivity) and immediate adaptability to different kinds of search or, even, to different portions of the same multi-step analysis pipeline. To test the capability of this new framework, a complete pipeline for directed searches of continuous waves signals has been developed using the BSD framework. The pipeline has been applied to a real gravitational wave search (Part III), pointing to the Milky Way central region for which a large number of unknown neutron stars are expected to exist. The results of this search, done using the last observational run (O2) of the LIGO detectors, didn’t show any evidence of the presence of continuous wave emission from the few inner parsecs of our Galaxy. Interesting limits on the minimum detectable strain and ellipticity of the sources have been placed. This is the first directed search for continuous waves signals performed within the Virgo Collaboration and the first LIGO-Virgo O2 directed search toward the Galactic center. The BSD framework developed in this thesis project will become the core of all CW searches of the Rome Virgo group. Furthermore, it represents a great starting point for the development of different types of searches, like that for long transient signals that could be emitted by the post-merger remnant of GW170817