Bone Vibration Analysis as a Novel Screening Tool for Long Bone Fractures

The aim of this study is to reduce the number of X-ray scans taken to detect fractures, by developing a bone fracture screening system. When assessing a bone injury, doctors need to decide whether the injury has resulted in a fracture or a sprain so that they can provide appropriate treatments. The current way to differentiate between these is by an X-ray scan. In 2011, the 46,000 children attending Sheffield Children’s Hospital Emergency Department had 10,400 X-rays, mostly to help diagnose bone fractures. Roughly half the X-ray scans taken indicate that the injury is sprain. Unnecessary X-ray scan means raising costs and exposing patients to ionising radiation. Vibration analysis is a well-established technology for condition monitoring for defect detection in industries however; its use in the medical field is still evolving. In bone vibration analysis, periodic or aperiodic oscillations or oscillating signals are introduced, and subsequent responses are recorded followed by using mathematical methods to reach a conclusion. In this study, a computer-controlled mechanism induces a mild vibration and successive responses are recorded via a piezoelectric sensor. To demonstrate the method's feasibility, a preliminary study was carried out on five blocks of wood of different density, with the same dimensions. The tests indicated a significant reduction in the blocks' vibration frequency following their fracture. After obtaining National Health Services (NHS) Research Approval, appropriate number of bone vibration responses was recorded from adults’ wrists and children’s wrists and ankles who attended local hospitals following wrist or ankle injuries. Suitable signal processing and pattern recognition techniques were developed on the basis of vibration responses from bones at various stages to interpret the recorded signals. Data were acquired from healthy participants at a local school which were compared with the data acquired from hospital participants to verify the methods. Currently, this study differentiates around 80% of the injuries accurately. Additionally, both the data acquisition program and the device have been modified to improve the developed procedures. This study made some promising discoveries and the resulting techniques can be used for further explorations

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach

Advanced zinc-doped adhesives for high performance at the resin-carious dentin interface

The purpose of this study was to evaluate the remineralization ability of an etch-and-rinse Zn-doped resin applied on caries-affected dentin (CAD). CAD surfaces were subjected to: (i) 37% phosphoric acid (PA) or (ii) 0.5M ethylenediaminetetraacetic acid (EDTA). 10wt% ZnO nanoparticles or 2wt% ZnCl2 were added into the adhesive Single Bond (SB), to create the following groups: PA+SB, PA+SB-ZnO, PA+SB-ZnCl2, EDTA+SB, EDTA+SB-ZnO, EDTA+SB-ZnCl2. Bonded interfaces were submitted to mechanical loading or stored during 24h. Remineralization of the bonded interfaces was studied by AFM nano-indentation (hardness and Young׳s modulus), Raman spectroscopy [mapping with principal component analysis (PCA), and hierarchical cluster analysis (HCA)] and Masson׳s trichrome staining technique. Dentin samples treated with PA+SB-ZnO attained the highest values of nano-mechanical properties. Load cycling increased both mineralization and crystallographic maturity at the interface; this effect was specially noticed when using ZnCl2-doped resin in EDTA-treated carious dentin. Crosslinking attained higher frequencies indicating better conformation and organization of collagen in specimens treated with PA+SB-ZnO, after load cycling. Trichrome staining technique depicted a deeper demineralized dentin fringe that became reduced after loading, and it was not observable in EDTA+SB groups. Multivariate analysis confirmed de homogenizing effect of load cycling in the percentage of variances, traces of centroids and distribution of clusters, especially in specimens treated with EDTA+SB-ZnCl2.Project MAT2014-52036-P supported by the Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (FEDER)

Machine vision applications in UAVs for autonomous aerial refueling and runway detection

This research focuses on the application of Machine Vision (MV) techniques and algorithms to the problems of Autonomous Aerial Refueling (AAR) and Runway Detection. In particular, real laboratory based hardware was used in a simulated environment to emulate real-life conditions for AAR. It was shown that the K-Means Clustering Algorithm solution to the Marker Detection problem could be executed at a frame rate of 30 Hz and it averaged a tracking error of less than one pixel while utilizing only 0.16% of the image. It was also shown that the solution to the Runway Detection problem could be executed at a frame rate of 20 Hz which is acceptable for use in an UAV performing reconnaissance work. Data from these tests suggest that both software schemes are suitable for applications in moving vehicles and that the accuracy of the measurements produced by the schemes make them suitable for UAV applications

Assessment of monthly rain fade in the equatorial region at C & KU-band using measat-3 satellite links

C & Ku-band satellite communication links are the most commonly used for equatorial satellite communication links. Severe rainfall rate in equatorial regions can cause a large rain attenuation in real compared to the prediction. ITU-R P. 618 standards are commonly used to predict satellite rain fade in designing satellite communication network. However, the prediction of ITU-R is still found to be inaccurate hence hinder a reliable operational satellite communication link in equatorial region. This paper aims to provide an accurate insight by assessment of the monthly C & Ku-band rain fade performance by collecting data from commercial earth stations using C band and Ku-band antenna with 11 m and 13 m diameter respectively. The antennas measure the C & Ku-band beacon signal from MEASAT-3 under equatorial rain conditions. The data is collected for one year in 2015. The monthly cumulative distribution function is developed based on the 1-year data. RMSE analysis is made by comparing the monthly measured data of C-band and Ku-band to the ITU-R predictions developed based on ITU-R’s P.618, P.837, P.838 and P.839 standards. The findings show that Ku-band produces an average of 25 RMSE value while the C-band rain attenuation produces an average of 2 RMSE value. Therefore, the ITU-R model still under predicts the rain attenuation in the equatorial region and this call for revisit of the fundamental quantity in determining the rain fade for rain attenuation to be re-evaluated

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time

Nuclear Power Plants

This book covers various topics, from thermal-hydraulic analysis to the safety analysis of nuclear power plant. It does not focus only on current power plant issues. Instead, it aims to address the challenging ideas that can be implemented in and used for the development of future nuclear power plants. This book will take the readers into the world of innovative research and development of future plants. Find your interests inside this book

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%