Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

Fuzzy logic controller optimized by particle swarm optimization for DC motor speed control

In this paper, we presented an optimized fuzzy logic controller using particle swarm optimization for DC motor speed control. The controller model is simulated using MATLAB software and also experimentally tested on a laboratory DC motor. A comparison of the performance of different controllers such as PID controller, fuzzy logic controller and optimized fuzzy logic controller is presented as well. With reference to the results of digital simulations and experiment, the designed FLC-PSO speed controller obtains much better dynamic behavior compared to PID and the normal FLC designed. Moreover, it can acquire superior performance of the DC motor, and also perfect speed tracking with no overshoot. The optimized membership functions (MFs) are obviously proved to be able to provide a better performance and higher robustness in comparison with a regular fuzzy model, when the MFs were heuristically defined. Besides, experimental results verify the ability of proposed FLC under sudden change of the load torque which leads to speed variances.

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For t (t) over bar events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p(T) > 0.9GeV is 94% for pseudorapidities of vertical bar eta vertical bar < 0.9 and 85% for 0.9 < vertical bar eta vertical bar < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p(T) = 100GeV emitted at vertical bar eta vertical bar < 1.4, the resolutions are approximately 2.8% in p(T), and respectively, 10 m m and 30 mu m in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10-12 mu m in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung.

Synthesis and characterization of silver/alanine nanocomposites for radiation detection in medical applications: the influence of particle size on the detection properties

Silver/alanine nanocomposites with varying mass percentage of silver have been produced. The size of the silver nanoparticles seems to drive the formation of the nanocomposite, yielding a homogeneous dispersion of the silver nanoparticles in the alanine matrix or flocs of silver nanoparticles segregated from the alanine crystals. The alanine crystalline orientation is modified according to the particle size of the silver nanoparticles. Concerning a mass percentage of silver below 0.1%, the nanocomposites are homogeneous, and there is no particle aggregation. As the mass percentage of silver is increased, the system becomes unstable, and there is particle flocculation with subsequent segregation of the alanine crystals. The nanocomposites have been analyzed by transmission electron microscopy (TEM), UV-Vis absorption spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy and they have been tested as radiation detectors by means of electron spin resonance (ESR) spectroscopy in order to detect the paramagnetic centers created by the radiation. In fact, the sensitivity of the radiation detectors is optimized in the case of systems containing small particles (30 nm) that are well dispersed in the alanine matrix. As the agglomeration increases, particle growth (up to 1.5 mu m) and segregation diminish the sensitivity. In conclusion, nanostructured materials can be used for optimization of alanine sensitivity, by taking into account the influence of the particles size of the silver nanoparticles on the detection properties of the alanine radiation detectors, thus contributing to the construction of small-sized detectors.

Exploring nu signals in dark matter detectors

We investigate standard and non-standard solar neutrino signals in direct dark matter detection experiments. It is well known that even without new physics, scattering of solar neutrinos on nuclei or electrons is an irreducible background for direct dark matter searches, once these experiments reach the ton scale. Here, we entertain the possibility that neutrino interactions are enhanced by new physics, such as new light force carriers (for instance a "dark photon") or neutrino magnetic moments. We consider models with only the three standard neutrino flavors, as well as scenarios with extra sterile neutrinos. We find that low-energy neutrino-electron and neutrino-nucleus scattering rates can be enhanced by several orders of magnitude, potentially enough to explain the event excesses observed in CoGeNT and CRESST. We also investigate temporal modulation in these neutrino signals, which can arise from geometric effects, oscillation physics, non-standard neutrino energy loss, and direction-dependent detection efficiencies. We emphasize that, in addition to providing potential explanations for existing signals, models featuring new physics in the neutrino sector can also be very relevant to future dark matter searches, where, on the one hand, they can be probed and constrained, but on the other hand, their signatures could also be confused with dark matter signals.

Applications of High Speed Configurable Logic Devices in Modern Particle Physics Experiments

Several activities were conducted during my PhD activity. For the NEMO experiment a collaboration between the INFN/University groups of Catania and Bologna led to the development and production of a mixed signal acquisition board for the Nemo Km3 telescope. The research concerned the feasibility study for a different acquisition technique quite far from that adopted in the NEMO Phase 1 telescope. The DAQ board that we realized exploits the LIRA06 front-end chip for the analog acquisition of anodic an dynodic sources of a PMT (Photo-Multiplier Tube). The low-power analog acquisition allows to sample contemporaneously multiple channels of the PMT at different gain factors in order to increase the signal response linearity over a wider dynamic range. Also the auto triggering and self-event-classification features help to improve the acquisition performance and the knowledge on the neutrino event. A fully functional interface towards the first level data concentrator, the Floor Control Module, has been integrated as well on the board, and a specific firmware has been realized to comply with the present communication protocols. This stage of the project foresees the use of an FPGA, a high speed configurable device, to provide the board with a flexible digital logic control core. After the validation of the whole front-end architecture this feature would be probably integrated in a common mixed-signal ASIC (Application Specific Integrated Circuit). The volatile nature of the configuration memory of the FPGA implied the integration of a flash ISP (In System Programming) memory and a smart architecture for a safe remote reconfiguration of it. All the integrated features of the board have been tested. At the Catania laboratory the behavior of the LIRA chip has been investigated in the digital environment of the DAQ board and we succeeded in driving the acquisition with the FPGA. The PMT pulses generated with an arbitrary waveform generator were correctly triggered and acquired by the analog chip, and successively they were digitized by the on board ADC under the supervision of the FPGA. For the communication towards the data concentrator a test bench has been realized in Bologna where, thanks to a lending of the Roma University and INFN, a full readout chain equivalent to that present in the NEMO phase-1 was installed. These tests showed a good behavior of the digital electronic that was able to receive and to execute command imparted by the PC console and to answer back with a reply. The remotely configurable logic behaved well too and demonstrated, at least in principle, the validity of this technique. A new prototype board is now under development at the Catania laboratory as an evolution of the one described above. This board is going to be deployed within the NEMO Phase-2 tower in one of its floors dedicated to new front-end proposals. This board will integrate a new analog acquisition chip called SAS (Smart Auto-triggering Sampler) introducing thus a new analog front-end but inheriting most of the digital logic present in the current DAQ board discussed in this thesis. For what concern the activity on high-resolution vertex detectors, I worked within the SLIM5 collaboration for the characterization of a MAPS (Monolithic Active Pixel Sensor) device called APSEL-4D. The mentioned chip is a matrix of 4096 active pixel sensors with deep N-well implantations meant for charge collection and to shield the analog electronics from digital noise. The chip integrates the full-custom sensors matrix and the sparsifification/readout logic realized with standard-cells in STM CMOS technology 130 nm. For the chip characterization a test-beam has been set up on the 12 GeV PS (Proton Synchrotron) line facility at CERN of Geneva (CH). The collaboration prepared a silicon strip telescope and a DAQ system (hardware and software) for data acquisition and control of the telescope that allowed to store about 90 million events in 7 equivalent days of live-time of the beam. My activities concerned basically the realization of a firmware interface towards and from the MAPS chip in order to integrate it on the general DAQ system. Thereafter I worked on the DAQ software to implement on it a proper Slow Control interface of the APSEL4D. Several APSEL4D chips with different thinning have been tested during the test beam. Those with 100 and 300 um presented an overall efficiency of about 90% imparting a threshold of 450 electrons. The test-beam allowed to estimate also the resolution of the pixel sensor providing good results consistent with the pitch/sqrt(12) formula. The MAPS intrinsic resolution has been extracted from the width of the residual plot taking into account the multiple scattering effect.

Tracking, b-tagging and measurement of the b-jet production cross section with the ATLAS detector

The Standard Model of elementary particle physics was developed to describe the fundamental particles which constitute matter and the interactions between them. The Large Hadron Collider (LHC) at CERN in Geneva was built to solve some of the remaining open questions in the Standard Model and to explore physics beyond it, by colliding two proton beams at world-record centre-of-mass energies. The ATLAS experiment is designed to reconstruct particles and their decay products originating from these collisions. The precise reconstruction of particle trajectories plays an important role in the identification of particle jets which originate from bottom quarks (b-tagging). This thesis describes the step-wise commissioning of the ATLAS track reconstruction and b-tagging software and one of the first measurements of the b-jet production cross section in pp collisions at sqrt(s)=7 TeV with the ATLAS detector. The performance of the track reconstruction software was studied in great detail, first using data from cosmic ray showers and then collisions at sqrt(s)=900 GeV and 7 TeV. The good understanding of the track reconstruction software allowed a very early deployment of the b-tagging algorithms. First studies of these algorithms and the measurement of the b-tagging efficiency in the data are presented. They agree well with predictions from Monte Carlo simulations. The b-jet production cross section was measured with the 2010 dataset recorded by the ATLAS detector, employing muons in jets to estimate the fraction of b-jets. The measurement is in good agreement with the Standard Model predictions.

Fast and accurate numerical solutions in some problems of particle and radiation transport: synthetic acceleration for the method of short characteristics, Doppler-broadened scattering kernel, remote sensing of the cryosphere

The aim of this work is to present various aspects of numerical simulation of particle and radiation transport for industrial and environmental protection applications, to enable the analysis of complex physical processes in a fast, reliable, and efficient way. In the first part we deal with speed-up of numerical simulation of neutron transport for nuclear reactor core analysis. The convergence properties of the source iteration scheme of the Method of Characteristics applied to be heterogeneous structured geometries has been enhanced by means of Boundary Projection Acceleration, enabling the study of 2D and 3D geometries with transport theory without spatial homogenization. The computational performances have been verified with the C5G7 2D and 3D benchmarks, showing a sensible reduction of iterations and CPU time. The second part is devoted to the study of temperature-dependent elastic scattering of neutrons for heavy isotopes near to the thermal zone. A numerical computation of the Doppler convolution of the elastic scattering kernel based on the gas model is presented, for a general energy dependent cross section and scattering law in the center of mass system. The range of integration has been optimized employing a numerical cutoff, allowing a faster numerical evaluation of the convolution integral. Legendre moments of the transfer kernel are subsequently obtained by direct quadrature and a numerical analysis of the convergence is presented. In the third part we focus our attention to remote sensing applications of radiative transfer employed to investigate the Earth's cryosphere. The photon transport equation is applied to simulate reflectivity of glaciers varying the age of the layer of snow or ice, its thickness, the presence or not other underlying layers, the degree of dust included in the snow, creating a framework able to decipher spectral signals collected by orbiting detectors.

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

Dual-Phase Liquid Xenon Detectors for Dark Matter Searches

Dual-phase time projection chambers (TPCs) filled with the liquid noble gas xenon (LXe) are currently the most sensitive detectors searching for interactions of WIMP dark matter in a laboratory-based experiment. This is achieved by combining a large, monolithic dark matter target of a very low background with the capability to localize the interaction vertex in three dimensions, allowing for target fiducialization and multiple-scatter rejection. The background in dual-phase LXe TPCs is further reduced by the simultaneous measurement of the scintillation and ionization signal from a particle interaction, which is used to distinguish signal from background signatures. This article reviews the principle of dual-phase LXe TPCs, and provides an overview about running as well as future experimental efforts.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.