Design and characterization of large area position sensitive radiation detectors

Planar, large area, position sensitive silicon detectors are widely utilized in high energy physics research and in medical, computed tomography (CT). This thesis describes author's research work relating to development of such detector components. The key motivation and objective for the research work has been the development of novel, position sensitive detectors improving the performance of the instruments they are intended for. Silicon strip detectors are the key components of barrel-shaped tracking instruments which are typically the innermost structures of high energy physics experimental stations. Particle colliders such as the former LEP collider or present LHC produce particle collisions and the silicon strip detector based trackers locate the trajectories of particles emanating from such collisions. Medical CT has become a regular part of everyday medical care in all developed countries. CT scanning enables x-ray imaging of all parts of the human body with an outstanding structural resolution and contrast. Brain, chest and abdomen slice images with a resolution of 0.5 mm are possible and latest CT machines are able to image whole human heart between heart beats. The two application areas are presented shortly and the radiation detection properties of planar silicon detectors are discussed. Fabrication methods and preamplifier electronics of the planar detectors are presented. Designs of the developed, large area silicon detectors are presented and measurement results of the key operating parameters are discussed. Static and dynamic performance of the developed silicon strip detectors are shown to be very satisfactory for experimental physics applications. Results relating to the developed, novel CT detector chips are found to be very promising for further development and all key performance goals are met.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

State estimation using the particle filter with mode tracking

A particle filter is a data assimilation scheme that employs a fully nonlinear, non-Gaussian analysis step. Unfortunately as the size of the state grows the number of ensemble members required for the particle filter to converge to the true solution increases exponentially. To overcome this Vaswani [Vaswani N. 2008. IEEE Trans Signal Process 56:4583–97] proposed a new method known as mode tracking to improve the efficiency of the particle filter. When mode tracking, the state is split into two subspaces. One subspace is forecast using the particle filter, the other is treated so that its values are set equal to the mode of the marginal pdf. There are many ways to split the state. One hypothesis is that the best results should be obtained from the particle filter with mode tracking when we mode track the maximum number of unimodal dimensions. The aim of this paper is to test this hypothesis using the three dimensional stochastic Lorenz equations with direct observations. It is found that mode tracking the maximum number of unimodal dimensions does not always provide the best result. The best choice of states to mode track depends on the number of particles used and the accuracy and frequency of the observations.

Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

Fast frontend electronics for high rate particle detectors

Future experiments in nuclear and particle physics are moving towards the high luminosity regime in order to access rare processes. In this framework, particle detectors require high rate capability together with excellent timing resolution for precise event reconstruction. In order to achieve this, the development of dedicated FrontEnd Electronics (FEE) for detectors has become increasingly challenging and expensive. Thus, a current trend in R&D is towards flexible FEE that can be easily adapted to a great variety of detectors, without impairing the required high performance. This thesis reports on a novel FEE for two different detector types: imaging Cherenkov counters and plastic scintillator arrays. The former requires high sensitivity and precision for detection of single photon signals, while the latter is characterized by slower and larger signals typical of scintillation processes. The FEE design was developed using high-bandwidth preamplifiers and fast discriminators which provide Time-over-Threshold (ToT). The use of discriminators allowed for low power consumption, minimal dead-times and self-triggering capabilities, all fundamental aspects for high rate applications. The output signals of the FEE are readout by a high precision TDC system based on FPGA. The performed full characterization of the analogue signals under realistic conditions proved that the ToT information can be used in a novel way for charge measurements or walk corrections, thus improving the obtainable timing resolution. Detailed laboratory investigations proved the feasibility of the ToT method. The full readout chain was investigated in test experiments at the Mainz Microtron: high counting rates per channel of several MHz were achieved, and a timing resolution of better than 100 ps after walk correction based on ToT was obtained. Ongoing applications to fast Time-of-Flight counters and future developments of FEE have been also recently investigated.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Particle Accelerators and Detectors for medical Diagnostics and Therapy

This Habilitationsschrift (Habilitation thesis) is focused on my research activities on medical applications of particle physics and was written in 2013 to obtain the Venia Docendi (Habilitation) in experimental physics at the University of Bern. It is based on selected publications, which represented at that time my major scientific contributions as an experimental physicist to the field of particle accelerators and detectors applied to medical diagnostics and therapy. The thesis is structured in two parts. In Part I, Chapter 1 presents an introduction to accelerators and detectors applied to medicine, with particular focus on cancer hadrontherapy and on the production of radioactive isotopes. In Chapter 2, my publications on medical particle accelerators are introduced and put into their perspective. In particular, high frequency linear accelerators for hadrontherapy are discussed together with the new Bern cyclotron laboratory. Chapter 3 is dedicated to particle detectors with particular emphasis on three instruments I contributed to propose and develop: segmented ionization chambers for hadrontherapy, a proton radiography apparatus with nuclear emulsion films, and a beam monitor detector for ion beams based on doped silica fibres. Selected research and review papers are contained in Part II. For copyright reasons, they are only listed and not reprinted in this on-line version. They are available on the websites of the journals.

Particle filtering for indoor RFID tag tracking

In this paper, we propose a particle filtering (PF) method for indoor tracking using radio frequency identification (RFID) based on aggregated binary measurements. We use an Ultra High Frequency (UHF) RFID system that is composed of a standard RFID reader, a large set of standard passive tags whose locations are known, and a newly designed, special semi-passive tag attached to an object that is tracked. This semi-passive tag has the dual ability to sense the backscatter communication between the reader and other passive tags which are in its proximity and to communicate this sensed information to the reader using backscatter modulation. We refer to this tag as a sense-a-tag (ST). Thus, the ST can provide the reader with information that can be used to determine the kinematic parameters of the object on which the ST is attached. We demonstrate the performance of the method with data obtained in a laboratory environment.

Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Measurement Function Design for Visual Tracking Applications

Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications.