Signal Processing in Wireless Communications: Device Fingerprinting and Wide-Band Interference Rejection

The rapid progress of wireless communication technologies that has taken place in recent years has significantly improved the quality of everyday life. However with this expansion of wireless communication systems come significant security threats and significant technological challenges, both of which are due to the fact that the communication medium is shared. The ubiquity of open wireless Internet access networks creates a new avenue for cyber-criminals to impersonate and act in an unauthorized way. The increasing number of deployed wide-band wireless communication systems entails technological challenges for effective utilization of the shared medium, which implies the need for advanced interference rejection methods. Wireless security and interference rejection in wide-band wireless communications are therefore often considered as the two main challenges in wireless network\u27s design and research. Important aspects of these challenges are illuminated and addressed in this dissertation. This dissertation considers signal processing approaches for exploiting or mitigating the effects of non-ideal components in wireless communication systems. In the first part of the dissertation, we introduce and study a novel, model-based approach to wireless device identification that exploits imperfections in the transmitter caused by manufacturing process nonidealities. Previous approaches to device identification based on hardware imperfections vary from transient analysis to machine learning but have not provided verifiable accuracy. Here, we detail a model-based approach, that uses statistical models of RF transmitter components: digital-to-analog converter, power amplifier and RF oscillator, which are amenable for analysis. Our proposed approach examines the key device characteristics that cause anonymity loss, countermeasures that can be applied by the nodes to regain the anonymity, and ways of thwarting such countermeasures. We develop identification algorithms based on statistical signal processing methods and address the challenging scenario when the units that need to be distinguished from one another are of the same model and from the same manufacturer. Using simulations and measurements of components that are commonly used in commercial communications systems, we show that our anonymity breaking techniques are effective. In the second part of the dissertation, we consider innovative approaches for the acquisition of frequency-sparse signals with wide-band receivers when a weak signal of interest is received in the presence of a very strong interference, and the effects of the nonlinearities in the low-noise amplifier at the receiver must be mitigated. All samples with amplitude above a given threshold, dictated by the linear input range of the receiver, are discarded to avoid the distortion caused by saturation of the low noise amplifier. Such a sampling scheme, while avoiding nonlinear distortion that cannot be corrected in the digital domain, poses challenges for signal reconstruction techniques, as the samples are taken non-uniformly, but also non-randomly. The considered approaches fall into the field of compressive sensing (CS); however, what differentiates them from conventional CS is that a structure is forced upon the measurement scheme. Such a structure causes a violation of the core CS assumption of the measurements\u27 randomness. We consider two different types of structured acquisition: signal independent and signal dependent structured acquisition. For the first case, we derive bounds on the number of samples needed for successful CS recovery when samples are drawn at random in predefined groups. For the second case, we consider enhancements of CS recovery methods when only small-amplitude samples of the signal that needs to be recovered are available for the recovery. Finally, we address a problem of spectral leakage due to the limited processing block size of block processing, wide-band receivers and propose an adaptive block size adjustment method, which leads to significant dynamic range improvements

Performance Bounds for Grouped Incoherent Measurements in Compressive Sensing

Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. However, for many practical signal acquisition applications, including medical imaging and remote sensing, this assumption is violated as the projections must be taken in groups. In this paper, we consider such applications and derive requirements on the number of measurements needed for successful recovery of signals when groups of dependent projections are taken at random. We find a penalty factor on the number of required measurements with respect to the standard CS scheme that employs conventional independent measurement selection and evaluate the accuracy of the predicted penalty through simulations.Comment: Revised for publication. 21 pages, 10 figure

Model of the distribution of diastolic left ventricular posterior wall thickness in healthy adults and its impact on the behavior of a string of virtual cardiomyocytes

Correlation of the thickness of the left ventricular posterior wall (LVPWd) with various parameters, including age, gender, weight and height, was investigated in this study using regression models. Multicenter derived database comprised over 4,000 healthy individuals. The developed models were further utilized in the in vitro-in vivo (IVIV) translation of the drug cardiac safety data with use of the mathematical model of human cardiomyocytes operating at the virtual healthy population level. LVPWd was assumed to be equivalent to the length of one-dimensional string of virtual cardiomyocyte cells which was presented, as other physiological factors, to be a parameter influencing the simulated pseudo-ECG (pseudoelectrocardiogram), QTcF and $\Delta$QTcF, both native and modified by exemplar drug (disopyramide) after $I_{Kr}$ current disruption. Simulation results support positive correlation between the LVPWd and QTcF/$\Delta$QTc. Developed models allow more detailed description of the virtual population and thus inter-individual variability influence on the drug cardiac safet

Liver transplantation for patients with acute-on-chronic liver failure (ACLF) in Europe: Results of the ELITA/EF-CLIF collaborative study (ECLIS)

BACKGROUND AND AIMS: Liver transplantation (LT) has been proposed to be an effective salvage therapy even for the sickest patients with acute-on-chronic liver failure (ACLF). This large collaborative study was designed to address the current clinical practice and outcomes of ACLF patients wait listed (WL) for LT in Europe. METHODS: Retrospective study including 308 consecutive ACLF patients, listed in 20 centres across 8 European countries, from January 2018 to June 2019. RESULTS: 2677 patients received a LT, 1216 (45.4%) for decompensated cirrhosis (DC). Of these, 234 (19.2%) had ACLF at LT: ACLF-1, 58 (4.8%); ACLF-2, 78 (6.4%); and ACLF-3, 98 (8.1%). Wide variations were observed amongst countries: France and Germany had high rates of ACLF-2/3 (27-41%); Italy, Switzerland, Poland and Netherlands had medium rates (9-15%); and United Kingdom and Spain had low rates (3-5%) (p 4 mmol/L (HR 3.14, 95% CI 1.37-7.19), recent infection from multi-drug resistant organisms (HR 3.67, 95% CI 1.63-8.28), and renal replacement therapy (HR 2.74, 95% CI 1.37-5.51) were independent predictors of post-LT mortality. During the same period, 74 patients with ACLF died on the WL. In an intention-to-treat analysis, one-year survival of ACLF patients on the LT WL was 73% for ACLF-1 or -2 and 50% for ACLF-3. CONCLUSION: The results reveal wide variations in listing patients with ACLF in Europe despite favorable post-LT survival. Risk factors for mortality were identified, allowing a more precise prognostic assessment of ACLF patients for potential LT. LAY SUMMARY: Acute on chronic liver failure (ACLF) is a severe clinical condition for which liver transplantation is an effective therapeutic option. This study has demonstrated that in Europe, referral and access to liver transplantation (LT) for patients with ACLF needs to be harmonized to avoid inequities. Post-LT survival for patients with ACLF was >80% after 1 year and some factors have been identified for selecting patients with favorable outcomes

Delivery of hepato-pancreato-biliary surgery during the COVID-19 pandemic: an European-African Hepato-Pancreato-Biliary Association (E-AHPBA) cross-sectional survey

Background: The extent of the COVID-19 pandemic and the resulting response has varied globally. The European and African Hepato-Pancreato-Biliary Association (E-AHPBA), the premier representative body for practicing HPB surgeons in Europe and Africa, conducted this survey to assess the impact of COVID-19 on HPB surgery. Methods: An online survey was disseminated to all E-AHPBA members to assess the effects of the pandemic on unit capacity, management of HPB cancers, use of COVID-19 screening and other aspects of service delivery. Results: Overall, 145 (25%) members responded. Most units, particularly in COVID-high countries (>100,000 cases) reported insufficient critical care capacity and reduced HPB operating sessions compared to COVID-low countries. Delayed access to cancer surgery necessitated alternatives including increased neoadjuvant chemotherapy for pancreatic cancer and colorectal liver metastases, and locoregional treatments for hepatocellular carcinoma. Other aspects of service delivery including COVID-19 screening and personal protective equipment varied between units and countries. Conclusion: This study demonstrates that the COVID-19 pandemic has had a profound adverse impact on the delivery of HPB cancer care across the continents of Europe and Africa. The findings illustrate the need for safe resumption of cancer surgery in a “new” normal world with screening of patients and staff for COVID-19

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

Recurrence of primary sclerosing cholangitis after liver transplantation – analysing the European Liver Transplant Registry and beyond

Liver transplantation for primary sclerosing cholangitis (PSC) can be complicated by recurrence of PSC (rPSC). This may compromise graft survival but the effect on patient survival is less clear. We investigated the effect of post-transplant rPSC on graft and patient survival in a large European cohort. Registry data from the European Liver Transplant Registry regarding all first transplants for PSC between 1980 and 2015 were supplemented with detailed data on rPSC from 48 out of 138 contributing transplant centres, involving 1,549 patients. Bayesian proportional hazards models were used to investigate the impact of rPSC and other covariates on patient and graft survival. Recurrence of PSC was diagnosed in 259 patients (16.7%) after a median follow-up of 5.0 years (quantile 2.5%-97.5%: 0.4–18.5), with a significant negative impact on both graft (HR 6.7; 95% CI 4.9–9.1) and patient survival (HR 2.3; 95% CI 1.5–3.3). Patients with rPSC underwent significantly more re-transplants than those without rPSC (OR 3.6, 95% CI 2.7–4.8). PSC recurrence has a negative impact on both graft and patient survival, independent of transplant-related covariates. Recurrence of PSC leads to higher number of re-transplantations and a 33% decrease in 10-year graft survival

Large scale variation in the rate of germ-line de novo mutation, base composition, divergence and diversity in humans

It has long been suspected that the rate of mutation varies across the human genome at a large scale based on the divergence between humans and other species. However, it is now possible to directly investigate this question using the large number of de novo mutations (DNMs) that have been discovered in humans through the sequencing of trios. We investi- gate a number of questions pertaining to the distribution of mutations using more than 130,000 DNMs from three large datasets. We demonstrate that the amount and pattern of variation differs between datasets at the 1MB and 100KB scales probably as a consequence of differences in sequencing technology and processing. In particular, datasets show differ- ent patterns of correlation to genomic variables such as replication time. Never-the-less there are many commonalities between datasets, which likely represent true patterns. We show that there is variation in the mutation rate at the 100KB, 1MB and 10MB scale that can- not be explained by variation at smaller scales, however the level of this variation is modest at large scales–at the 1MB scale we infer that ~90% of regions have a mutation rate within 50% of the mean. Different types of mutation show similar levels of variation and appear to vary in concert which suggests the pattern of mutation is relatively constant across the genome. We demonstrate that variation in the mutation rate does not generate large-scale variation in GC-content, and hence that mutation bias does not maintain the isochore struc- ture of the human genome. We find that genomic features explain less than 40% of the explainable variance in the rate of DNM. As expected the rate of divergence between spe- cies is correlated to the rate of DNM. However, the correlations are weaker than expected if all the variation in divergence was due to variation in the mutation rate. We provide evidence that this is due the effect of biased gene conversion on the probability that a mutation will become fixed. In contrast to divergence, we find that most of the variation in diversity can be explained by variation in the mutation rate. Finally, we show that the correlation between divergence and DNM density declines as increasingly divergent species are considered

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA