Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters

Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target tracking in distributed sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest. To this end, we develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure

Nucleon-induced reactions at intermediate energies: New data at 96 MeV and theoretical status

Double-differential cross sections for light charged particle production (up to A=4) were measured in 96 MeV neutron-induced reactions, at TSL laboratory cyclotron in Uppsala (Sweden). Measurements for three targets, Fe, Pb, and U, were performed using two independent devices, SCANDAL and MEDLEY. The data were recorded with low energy thresholds and for a wide angular range (20-160 degrees). The normalization procedure used to extract the cross sections is based on the np elastic scattering reaction that we measured and for which we present experimental results. A good control of the systematic uncertainties affecting the results is achieved. Calculations using the exciton model are reported. Two different theoretical approches proposed to improve its predictive power regarding the complex particle emission are tested. The capabilities of each approach is illustrated by comparison with the 96 MeV data that we measured, and with other experimental results available in the literature.Comment: 21 pages, 28 figure

Application of improved you only look once model in road traffic monitoring system

The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms

DEVELOPMENT OF A ROBUST LINAC-BASED RADIOSURGERY PROGRAM FOR MULTIPLE BRAIN METASTASES & ESTIMATION THE RADIOBIOLOGICAL RESPONSE OF INDIRECT CELL KILL

Accurate and precise delivery of Stereotactic Radiosurgery (SRS) using Gamma Knife (GK) unit by Leksell is a gold standard for multiple intracranial lesions. SRS provides less brain toxicity compared to whole brain radiotherapy techniques historically used. However, these treatments are limited in availability and are accompanied by long treatment times with painful, intolerable headframe fixation. With advancements in linear accelerator (Linac) based SRS, multiple brain lesions can be treated separately with individual isocenters or, more recently, altogether with a single isocenter multi-target (SIMT) volumetric modulated arc therapy (VMAT) technique. SIMT methods reduce the challenges of treating patients with GK by significantly decreasing treatment times, improving patient comfort and clinic workflow. This dissertation explores the usability of SIMT VMAT and presents potential solutions to the challenges of treating multiple brain lesions using Linac-based SRS. Treating multiple brain lesions simultaneously with a SIMT VMAT plan is an efficient treatment option for SRS; however, it does not account for patient setup uncertainty, which degrades treatment delivery accuracy. This dissertation quantifies the loss of target coverage by simulating patient setup errors that would be seen on daily cone beam CT imaging during patient set up and verification. These simulations resulted in dosimetric discrepancies up to 70% (average, 30%), providing suboptimal SRS treatments. It was also found that small tumors were more susceptible to these setup uncertainties and would experience greater losses of target coverage. This means SIMT-VMAT, in its current use, is not an accurate SRS treatment modality for brain metastases. This dissertation aims to provide potential solutions to minimize these spatial uncertainties discussed. First, a novel risk-adapted correction strategy was explored where dose is escalated for small targets at a large distance from the isocenter. These treatments with up to ±1o/1 mm set up errors in all 6-directions demonstrated promising plan quality and treatment delivery accuracy with less spread of intermediate dose to the normal brain. Second, a dual isocenter planning strategy that groups lesions based on brain hemisphere location was proposed. These plans provided similar target coverage and dose conformity as compared to the SIMT plans with less low and intermediate dose to the brain and less dose to surrounding critical organs. These techniques could potentially improve target localization accuracy and be delivered within a standard treatment slot. Though these SIMT VMAT treatments for multiple brain metastases could be at risk of detrimental spatial uncertainties, recent clinical outcome studies suggest high rates of tumor local-control and positive treatment outcomes. In this dissertation, this is explained through a combination of both direct and indirect cell kill. A single dose of 15 Gy or more will cause damage to the weak cellular vasculature of the brain tumors, ultimately resulting in secondary cell death. By inducing clinically observable systematic set up errors, the role of secondary cell death is modeled to define the relationship between achieving required target coverage and spatial uncertainty. For 20 Gy prescription, it was found that patient set up errors of 1.3 mm/1.3°in all 6-directions must be maintained in order to achieve a target dose of 15 Gy or higher with no additional brain toxicity. At this range of uncertainty, devascularization would occur resulting in positive tumor local control, providing guidance to treating physicians for clinically acceptable patient setup errors and perhaps resulting acceptable treatment outcomes. A prospective clinical trial is necessary to further validate this radiobiological model, incorporating secondary cell death with direct cell kill using a single-isocenter VMAT plan for multiple brain lesions

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Cardioprotective and proangiogenic activities of small extracellular vesicles released by amniotic fluid stem cells

Protection against myocardial ischaemia/reperfusion injury and regeneration of the damaged myocardium are long-sought goals. The use of small extracellular vesicles (sEVs) released by mesenchymal stem cells (MSCs) was shown to be of benefit in the myocardial infarction setting. However, MSCs are frequently harvested from aged or diseased patients and suboptimal sEV isolation methods are used. A subtype of young, foetal MSCs, namely spindle-shaped amniotic fluid stem cells (SS-AFSCs), is known to possess better expansion and functional capacity than its adult counterparts. Here, sEVs released by SS-AFSCs were isolated using size-exclusion chromatography (SEC) – an isolation technique that yields vesicles of superior purity – and their cardioprotective and proangiogenic activities were studied. Firstly, using rat blood plasma, it was demonstrated that SEC isolates higher sEV yields with significantly compromised purity, mostly due to the presence of lipoproteins. To overcome this, a serum-free environment was used for sEVs isolation from SS-AFSC-conditioned medium. Comprehensive characterisation experiments showed that the harvested SS-AFSC sEVs are of high purity. Functionally, SS-AFSC sEVs protected the rat myocardium from ischaemia/reperfusion injury in vivo, but not isolated cardiomyocytes in vitro, indicative of indirect cardioprotective effects. Additionally, SS-AFSC sEVs promoted migration of endothelial cells in vitro and recapitulated the promigratory effects of the SS-AFSC-conditioned medium. Using pharmacological inhibition, it was shown that PI3K pathway, a known player in cell migration, mediates the sEV effects, while a series of potential candidates in the sEV cargo were excluded. Finally, cellular sEV uptake was studied by use of lipophilic dye-labelling experiments. Surprisingly, this commonly used approach was found to be unsuitable for sEV tracking due to non-specific dye retention by non-sEV contaminants. Overall, SEC-isolated SS-AFSC sEVs possess cardioprotective potential manifested only in vivo, and promigratory activity which requires PI3K signalling. These data indicate that SS-AFSC sEVs have multifactorial beneficial effects in a myocardial infarction setting

Multi-object filtering with second-order moment statistics

The focus of this work lies on multi-object estimation techniques, in particular the Probability Hypothesis Density (PHD) filter and its variations. The PHD filter is a recursive, closed-form estimation technique which tracks a population of objects as a group, hence avoiding the combinatorics of data association and therefore yielding a powerful alternative to methods like Multi-Hypothesis Tracking (MHT). Its relatively low computational complexity stems from strong modelling assumptions which have been relaxed in the Cardinalized PHD (CPHD) filter to gain more flexibility, but at a much higher computational cost. We are concerned with the development of two suitable alternatives which give a compromise between the simplicity and elegance of the PHD filter and the versatility of the CPHD filter. The first alternative generalises the clutter model of the PHD filter, leading to more accurate estimation results in the presence of highly variable numbers of false alarms; the second alternative provides a closed-form recursion of a second-order PHD filter that propagates variance information along with the target intensity, thus providing more information than the PHD filter while keeping a much lower computational complexity than the CPHD filter. The discussed filters are applied on simulated data, furthermore their practicality is demonstrated on live-cell super-resolution microscopy images to provide powerful techniques for molecule and cell tracking, stage drift estimation and estimation of background noise