Performance Analysis of Differential Beamforming in Decentralized Networks

This paper proposes and analyzes a novel differential distributed beamforming strategy for decentralized two-way relay networks. In our strategy, the phases of the received signals at all relays are synchronized without requiring channel feedback or training symbols. Bit error rate (BER) expressions of the proposed strategy are provided for coherent and differential M-PSK modulation. Upper bounds, lower bounds, and simple approximations of the BER are also derived. Based on the theoretical and simulated BER performance, the proposed strategy offers a high system performance and low decoding complexity and delay without requiring channel state information at any transmitting or receiving antenna. Furthermore, the simple approximation of the BER upper bound shows that the proposed strategy enjoys the full diversity gain which is equal to the number of transmitting antennas

A computationally efficient detector for MIMO systems

In this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques

Computationally Efficient Multi-Antenna Techniques for Multi-User Two-Way Wireless Relay Networks

In this work, we are interested in implementing, developing and evaluating multi-antenna techniques used for multi-user two-way wireless relay networks that provide a good tradeoff between the computational complexity and performance in terms of symbol error rate and achievable data rate. In particular, a variety of newly multi-antenna techniques is proposed and studied. Some techniques based on orthogonal projection enjoy low computational complexity. However, the performance penalty associated with them is high. Other techniques based on maximum likelihood strategy enjoy high performance, however, they suffer from very high computational complexity. The Other techniques based on randomization strategy provide a good trade-off between the computational complexity and performance where they enjoy low computational complexity with almost the same performance as compared to the techniques based on maximum likelihood strategy

Artificial Intelligence in Cardiac Magnetic Resonance Imaging to Predict Prognosis and Treatment Response

Background Pulmonary arterial hypertension (PAH) is a serious disease of the heart and lungs. Its impact on patients can be severe, including limitation of day-to-day activities and high mortality. The diagnosis, treatment and monitoring of PAH are challenging and there is a need for tools that can aid clinical decision-making to optimise patient outcomes. Cardiac MRI (CMR) provides both qualitative and quantitative information about cardiac function and is an important method for evaluating the severity of PAH. The application of machine learning (ML) tools is of growing interest in medical imaging. ML has the potential to automate complex and repetitive tasks, including the rapid segmentation of anatomical structures on images and extraction of clinically useful information. Aims This thesis proposes the combination of CMR with two different ML tools to predict prognosis and treatment response in PAH. The first ML tool involves the automated measurement of different cardiac parameters and assesses their utility in predicting prognosis and treatment response. The second ML tool involves the extraction of imaging features directly without the need for segmentation to predict the risk of mortality. My Contribution The ML models in this thesis were developed at the University of Sheffield in collaboration with Leiden University. Sheffield is a centre of excellence in PAH treatment thanks to the Sheffield Pulmonary Vascular Disease Unit, which is one of the largest internationally. Each year, more than 700 PAH patients undergo CMR for diagnosis and monitoring. Additionally, each newly diagnosed patient has accompanying in-depth clinical phenotypic data, including right heart catheterisation, exercise and pulmonary function tests, and quality of life assessment. During my research, I created and curated a dataset combining imaging and time-matched clinical data. I identified eligible CMR scans, landmarked and contoured cardiac chambers on multiple sequences and organised the collaboration with computer scientists at Leiden and Sheffield. I arranged image anonymisation, storage and transfer and advised computer scientists on the clinical relevance of CMR images. I performed quality control on ML analyses, collated their results, and analysed the data within clinical context. I have written all chapters in this thesis and clarified the roles of my co-authors at the end of each chapter. Thesis Outline Chapter 1 provided an overview of the growing role of CMR in the diagnosis and evaluation of PAH. Chapter 2 summarised the prognostic value of CMR measurements in the prediction of clinical worsening and mortality in PAH patients. Chapter 3 illustrated the rapid expansion of research using AI approaches to automate CMR measurements. The quality of the existing literature was reviewed, significant shortcomings in the transparency of studies were identified and solutions were recommended. Chapter 4 showed our experience in developing, validating and testing a fully automatic CMR segmentation tool. Our tool was developed in one of the largest multi-vendor, multi-centre and multi-pathology reported datasets, and included a large group of patients with right heart disease. We implemented the lessons learned in Chapter 3 and provided extensive descriptions of our datasets, ML model and performance. Our model showed excellent reliability, generalisability, agreement with CMR experts and correlation with invasive haemodynamics. Chapter 5 demonstrated that the automatic CMR measurements allowed assessment of patient-orientated outcomes and prediction of mortality. Thresholds of changes in CMR metrics were identified that could inform clinical decisions in the monitoring of PAH patients. Chapter 6 showed promising results of an ML tool to extrapolate prognostic CMR features with incremental value compared to clinical risk scores and volumetric CMR measurements. Finally, Chapter 7 showed that myocardial T1 mapping could potentially add diagnostic and prognostic value in PAH. Impact and Future Direction In addition to the known advantages of ML for providing rapid results with minimal human involvement, the ML tools developed in this thesis allow visualisation of outcomes and are transparent to the human assessor. ML applications to automate the measurement of CMR metrics and extract prognostic imaging features have potential to add clinical value by (i) streamlining prognostication, (ii) informing treatment selection, (iii) assisting the monitoring of treatment response and (iv) ultimately improving clinical decision-making and patient outcomes. Additionally, these tools could point to new CMR end-points for clinical trials, accelerating the development of new treatments for PAH. ML will likely elevate the role of CMR as a powerful prognostic modality in the years to come. Looking ahead, I hope to combine multi-source clinical, imaging and patient-orientated data from several ML tools into a single package to facilitate the assessment of cardiovascular disease

Distributed differential beamforming and power allocation for cooperative communication networks

Many coherent cooperative diversity techniques for wireless relay networks have recently been suggested to improve the overall system performance in terms of the achievable data rate or bit error rate (BER) with low decoding complexity and delay. However, these techniques require channel state information (CSI) at the transmitter side, at the receiver side, or at both sides. Therefore, due to the overhead associated with estimating CSI, distributed differential space-time coding techniques have been suggested to overcome this overhead by detecting the information symbols without requiring any (CSI) at any transmitting or receiving antenna. However, the latter techniques suffer from low performance in terms of BER as well as high latency and decoding complexity. In this paper, a distributed differential beamforming technique with power allocation is proposed to overcome all drawbacks associated with the later techniques without needing CSI at any antenna and to be used for cooperative communication networks. We prove through our analytical and simulation results that the proposed technique outperforms the state-of-the-art techniques in terms of BER with comparably low decoding complexity and latency

A novel beamforming emulating photonic nanojets for wireless relay networks

© 2021 Tech Science Press. All rights reserved.In this article, a low-cost electromagnetic structure emulating photonic nanojets is utilized to improve the efficiency of wireless relay networks. The spectral element method, due to its high accuracy, is used to verify the efficiency of the proposed structure by solving the associate field distribution. The application of optimal single-relay selection method shows that full diversity gain with low complexity can be achieved. In this paper, the proposed technique using smart relays combines the aforementioned two methods to attain the benefits of both methods by achieving the highest coding and diversity gain and enhances the overall network performance in terms of bit error rate (BER). Moreover, we analytically prove the advantage of using the proposed technique. In our simulations, it can be shown that the proposed technique outperforms the best known state-of-the-art single relay selection technique. Furthermore, the BER expressions obtained from the theoretical analysis are perfectly matched to those obtained from the conducted simulations

A low complexity distributed differential scheme based on orthogonal space time block coding for decode-and-forward wireless relay networks

This work proposes a new differential cooperative diversity scheme with high data rate and low decoding complexity using the decode-and-forward protocol. The proposed model does not require either differential encoding or channel state information at the source node, relay nodes, or destination node where the data sequence is directly transmitted and the differential detection method is applied at the relay nodes and the destination node. The proposed technique enjoys a low encoding and decoding complexity at the source node, the relay nodes, and the destination node. Furthermore, the performance of the proposed strategy is analyzed by computer simulations in quasi-static Rayleigh fading channel and using the decode-and-forward protocol. The simulation results show that the proposed differential technique outperforms the corresponding reference strategies

Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging

BackgroundSegmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods—particularly deep learning (DL)—can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT.MethodsEmbase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).Results18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83–0.91), left ventricle (0.91, IQR 0.89–0.94), left ventricle myocardium (0.83, IQR 0.82–0.92), right atrium (0.88, IQR 0.83–0.90), right ventricle (0.91, IQR 0.85–0.92), and pulmonary artery (0.92, IQR 0.87–0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%).ConclusionSupervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings.Systematic Review Registration[www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113]

Colchicine for children with pericarditis: systematic review of clinical studies

OBJECTIVE: To review the evidence for the efficacy and safety of colchicine in children with pericarditis. DESIGN: Systematic review. SEARCH STRATEGY: The following databases were searched for studies about colchicine in children with pericarditis (June 2015): Cochrane Central, Medline, EMBASE and LILACS. ELIGIBILITY CRITERIA: All observational and experimental studies on humans with any length of follow-up and no limitations on language or publication status were included. The outcomes studied were recurrences of pericarditis and adverse events. DATA EXTRACTION: Two authors extracted data and assessed quality of included studies using the Cochrane risk of bias tool for non-randomised trials. RESULTS: Two case series and nine case reports reported the use of colchicine in a total of 86 children with pericarditis. Five articles including 74 paediatric patients were in favour of colchicine in preventing further pericarditis recurrences. Six studies including 12 patients showed that colchicine did not prevent recurrences of pericarditis. LIMITATIONS: No randomised controlled trials (RCTs) were found. CONCLUSIONS: Although colchicine is an established treatment for pericarditis in adults, it is not routinely used in children. There is not enough evidence to support or discourage the use of colchicine in children with pericarditis. Further research in the form of large double-blind RCTs is needed to establish the efficacy of colchicine in children with pericarditis

Left Ventricular Blood Flow Kinetic Energy Assessment by 4D Flow Cardiovascular Magnetic Resonance: A Systematic Review of the Clinical Relevance

There is an emerging body of evidence that supports the potential clinical value of left ventricular (LV) intracavity blood flow kinetic energy (KE) assessment using four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR). The aim of this systematic review is to summarize studies evaluating LV intracavity blood flow KE quantification methods and its potential clinical significance. Methods: A systematic review search was carried out on Medline, Pubmed, EMBASE and CINAHL. Results: Of the 677 articles screened, 16 studies met eligibility. These included six (37%) studies on LV diastolic function, another six (37%) studies on heart failure or cardiomyopathies, three (19%) studies on ischemic heart disease or myocardial infarction and finally, one (6%) study on valvular heart disease, namely, mitral regurgitation. One of the main strengths identified by these studies is high reproducibility of LV blood flow KE hemodynamic assessment (mean coefficient of variability = 6 ±  2%) for the evaluation of LV diastolic function. Conclusions: The evidence gathered in this systematic review suggests that LV blood flow KE has great promise for LV hemodynamic assessment. Studies showed increased diagnostic confidence at no cost of additional time. Results were highly reproducible with low intraobserver variability