A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission

Nowadays, the demand for communication multi-carriers' channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10(-2), which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10(-1), changes from 2.09 dB for the analytical to 1.68 dB for the random SLM

Multi-Objective Optimization Methods Based on Artificial Neural Networks

Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer. This book demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. Although it is targeted to a wide group of readers: researchers, graduate students, and practitioners, it does not offer an exhaustive coverage of search algorithms and applications. The chapters are organized into three parts: Population-based and quantum search algorithms, Search algorithms for image and video processing, and Search algorithms for engineering applications

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

Qualitative dynamic diagnosis of circuits

Abstract We describe ACDS, an automatic diagnostic system. ACDS is capable of diagnosing faults on analog circuits in dynamic conditions. The circuit's dynamic behavior is studied by means of a series of intrastate simulations during which the qualitative state of the circuit does not change. An acquisition board collects the value of a set of quantities corresponding to accessible test points. These measurements are converted into qualitative values and are used for two purposes: first, to determine the state of the circuit components; second, to trigger the diagnostic procedure whenever a discrepancy between observed and predicted behavior is found. The main difficulty in this phase of measurement interpretation consists in obtaining meaningful numerical-qualitative data conversion for values of quantities approaching a boundary between two different qualitative intervals. System performance has been verified through a number of simulations, which have shown the proposed approach to be efficient both in terms of localized faults and of flexibility in adapting to different circuits

Breast milk stem cells: four questions looking for an answer

The finding of stem/progenitor cells in the maternal milk and the discovery of their multilineage potential, associated with some evidence regarding the ability of maternal cells to cross the gastrointestinal barrier and integrate into the organs of the breastfed neonate, has opened an intriguing debate, regarding the strict relationship between mother and son in the postnatal period. In particular, thanks to the discovery of the presence in high quantities of mammary stem cells, a new vision of maternal milk is emerging, in which breastfeeding appears as an unique occasion for reinforcing the physiological development of the newborn, putting all the formulas at a different level of relevance for the neonate. In this contribution the authors try to give an answer to the following 4 questions: 1. is there heterogeneity and a hierarchy among breast milk stem cells? 2. can stem cells present in breast milk enter into the newborn organism? 3. can breast milk stem cells integrate in the neonatal organs and differentiate toward different tissues, including neurons and neuroglia? 4. could metabolomics be useful for the study of stem cells in the human milk

Papillary thyroid carcinoma presented as a hypercaptant nodule: a case report

Hot thyroid nodules are mostly benign and rarely show a malignant nature. Here we present the case of a 45- year-old man with a hypercaptant but ultrasound suspicious nodule; he underwent fine needle aspiration (FNA) and subsequent thyroidectomy. Pathology revealed a papillary thyroid carcinoma (PTC) with focal tall cell features, positivity to BRAF V600E and focal hyperspression of p53. A multidisciplinary clinicopathological approach is crucial for the correct diagnosis

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs

Decision trees to evaluate the risk of developing multiple sclerosis

Introduction: Multiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS. Methods: This paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors. Results: The study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease. Discussion: Given its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease

T-Cell Lymphoblastic Lymphoma Arising in the Setting of Myeloid/Lymphoid Neoplasms with Eosinophilia: LMO2 Immunohistochemistry as a Potentially Useful Diagnostic Marker

Simple Summary Rarely, T-lymphoblastic lymphoma (T-LBL) may develop in the setting of myeloid/lymphoid neoplasms with eosinophilia. Given important therapeutic implications, it is crucial to identify T-LBL arising in this particular context. LIM domain only 2 (LMO2) is known to be overexpressed in almost all sporadic T-LBL and not in immature TdT-positive T-cells in the thymus and in indolent T-lymphoblastic proliferations. We retrospectively evaluated the clinical, morphological, immunohistochemical and molecular features of 11 cases of T-LBL occurring in the setting of myeloid/lymphoid neoplasms with eosinophilia and investigated the immunohistochemical expression of LMO2 in this setting of T-LBL. Interestingly, 9/11 cases were LMO2 negative, with only 2 cases showing partial expression. In our study, we would suggest that LMO2 immunostaining, as part of the diagnostic panel for T-LBL, may represent a useful marker to identify T-LBL developing in the context of myeloid/lymphoid neoplasms with eosinophilia. Background: Rarely, T-lymphoblastic lymphoma (T-LBL) may develop in the setting of myeloid/lymphoid neoplasms with eosinophilia (M/LNs-Eo), a group of diseases with gene fusion resulting in overexpression of an aberrant tyrosine kinase or cytokine receptor. The correct identification of this category has relevant therapeutic implications. LIM domain only 2 (LMO2) is overexpressed in most T-LBL, but not in immature TdT-positive T-cells in the thymus and in indolent T-lymphoblastic proliferations (iT-LBP). Methods and Results: We retrospectively evaluated 11 cases of T-LBL occurring in the context of M/LNs-Eo. Clinical, histological, immunohistochemical and molecular features were collected and LMO2 immunohistochemical staining was performed. The critical re-evaluation of these cases confirmed the diagnosis of T-LBL with morphological, immunohistochemical and molecular features consistent with T-LBL occurring in M/LNs-Eo. Interestingly, LMO2 immunohistochemical analysis was negative in 9/11 cases, whereas only 2 cases revealed a partial LMO2 expression with a moderate and low degree of intensity, respectively. Conclusions: LMO2 may represent a potentially useful marker to identify T-LBL developing in the context of M/LNs-Eo. In this setting, T-LBL shows LMO2 immunohistochemical profile overlapping with cortical thymocytes and iT-LBP, possibly reflecting different molecular patterns involved in the pathogenesis of T-LBL arising in the setting of M/LNs-Eo