École nationale d'ingénieurs de Brest

Abstract This paper presents a receiving scheme intended to combat the detrimental effects of intersymbol interference for digital transmissions protected by convolutional codes. The receiver performs two successive soft‐output decisions, achieved by a symbol detector and a channel decoder, through an iterative process. At each iteration, extrinsic information is extracted from the detection and decoding steps and is then used at the next iteration as in turbo‐decoding. From the implementation point of view, the receiver can be structured in a modular way and its performance, in bit error rate terms, is directly related to the number of modules used. Simulation results are presented for transmissions on Gauss and Rayleigh channels. The results obtained show that turbo‐equalization manages to overcome multipath effects, totally on Gauss channels, and partially but still satisfactorily on Rayleigh channels.

This paper describes an iterative decoding algorithm for any product code built using linear block codes. It is based on soft-input/soft-output decoders for decoding the component codes so that near-optimum performance is obtained at each iteration. This soft-input/soft-output decoder is a Chase decoder which delivers soft outputs instead of binary decisions. The soft output of the decoder is an estimation of the log-likelihood ratio (LLR) of the binary decisions given by the Chase decoder. The theoretical justifications of this algorithm are developed and the method used for computing the soft output is fully described. The iterative decoding of product codes is also known as the block turbo code (BTC) because the concept is quite similar to turbo codes based on iterative decoding of concatenated recursive convolutional codes. The performance of different Bose-Chaudhuri-Hocquenghem (BCH)-BTCs are given for the Gaussian and the Rayleigh channel. Performance on the Gaussian channel indicates that data transmission at 0.8 dB of Shannon's limit or more than 98% (R/C>0.98) of channel capacity can be achieved with high-code-rate BTC using only four iterations. For the Rayleigh channel, the slope of the bit-error rate (BER) curve is as steep as for the Gaussian channel without using channel state information.

Significant advances in the management of cystic fibrosis (CF) in recent decades have dramatically changed the epidemiology and prognosis of this serious disease, which is no longer an exclusively pediatric disease. This paper aims to review the changes in the incidence and survival of CF and to assess the impact of the discovery of the responsible gene (the CFTR gene) on these changes. The incidence of CF appears to be decreasing in most countries and patient survival, which can be monitored by various indicators, has improved substantially, with an estimated median age of survival of approximately50 years today. Cloning of the CFTR gene 30 years ago and efforts to identify its many mutations have greatly improved the management of CF. Implementation of genetic screening policies has enabled earlier diagnosis (via newborn screening), in addition to prevention within families or in the general population in some areas (via prenatal diagnosis, family testing or population carrier screening). In the past decade, in-depth knowledge of the molecular bases of CF has also enabled the emergence of CFTR modulator therapies which have led to major clinical advances in the treatment of CF. All of these phenomena have contributed to changing the face of CF. The advent of targeted therapies has paved the way for precision medicine and is expected to further improve survival in the coming years.

Impulse differential inclusions are introduced as a framework for modeling hybrid phenomena. Connections to standard problems in the area of hybrid systems are discussed. Conditions are derived that allow one to determine whether a set of states is viable or invariant under the action of an impulse differential inclusion. For sets that violate these conditions, methods are developed for approximating their viability and invariance kernels, that is the largest subset that is viable or invariant under the action of the impulse differential inclusion. The results are demonstrated on examples.

The tropical Atlantic Ocean is characterized by a large seasonal cycle around which there are climatically significant interannual and decadal timescale variations. The most pronounced of these interannual variations are equatorial warm events, somewhat similar to the El Niño events for the Pacific, and the so-called Atlantic sea surface temperature dipole. Both of these phenomena in turn may be related to El Niño-Southern Oscillation variability in the tropical Pacific and other modes of regional climatic variability in ways that are not yet fully understood. PIRATA (Pilot Research Moored Array in the Tropical Atlantic) will address the lack of oceanic and atmospheric data in the tropical Atlantic, which limits our ability to make progress on these important climate issues. The PIRATA array consists of 12 moored Autonomous Temperature Line Acquisition System buoy sites to be occupied during the years 1997–2000 for monitoring the surface variables and upper-ocean thermal structure at key locations in the tropical Atlantic. Meteorological and oceanographical measurements are transmitted via satellite in real time and are available to all interested users in the research or operational communities. The total number of moorings is a compromise between the need to put out a large enough array for a long enough period of time to gain fundamentally new insights into coupled ocean–atmosphere interactions in the region, while at the same time recognizing the practical constraints of resource limitations in terms of funding, ship time, and personnel. Seen as a pilot Global Ocean Observing System/Global Climate Observing System experiment, PIRATA contributes to monitoring the tropical Atlantic in real time and anticipates a comprehensive observing system that could be operational in the region for the 2000s.

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.

This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and nonverbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and nonverbal behaviors required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and nonverbal behavior since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on nonverbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling, etc. We first report on three prototype versions of the SAL scenario in which the behavior of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analyzing and synthesizing the respective behaviors. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behavior, dialogue management, and synthesis of speaker and listener behavior of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.

Antimicrobial photodynamic therapy (aPDT) has become a fundamental tool in modern therapeutics, notably due to the expanding versatility of photosensitizers (PSs) and the numerous possibilities to combine aPDT with other antimicrobial treatments to combat localized infections. After revisiting the basic principles of aPDT, this review first highlights the current state of the art of curative or preventive aPDT applications with relevant clinical trials. In addition, the most recent developments in photochemistry and photophysics as well as advanced carrier systems in the context of aPDT are provided, with a focus on the latest generations of efficient and versatile PSs and the progress towards hybrid-multicomponent systems. In particular, deeper insight into combinatory aPDT approaches is afforded, involving non-radiative or other light-based modalities. Selected aPDT perspectives are outlined, pointing out new strategies to target and treat microorganisms. Finally, the review works out the evolution of the conceptually simple PDT methodology towards a much more sophisticated, integrated, and innovative technology as an important element of potent antimicrobial strategies.

BACKGROUND: Glucocorticoids are the cornerstone treatment of polymyalgia rheumatica (PMR) but induce adverse events. OBJECTIVES: To evaluate the efficacy and safety of first-line tocilizumab in PMR. METHODS: In a prospective open-label study (ClinicalTrials.gov: NCT01713842), 20 glucocorticoid-free patients fulfilling Chuang's PMR criteria, with symptom onset within the last 12 months and a PMR activity score (PMR-AS) >10, each received three tocilizumab infusions at 4-week intervals, without glucocorticoids, followed by oral prednisone from weeks 12 to 24 (0.15 mg/kg if PMR-AS ≤10 and 0.30 mg/kg otherwise). The primary end point was the proportion of patients with PMR-AS≤10 at week 12. RESULTS: Baseline median PMR-AS was 36.6 (IQR 30.4-43.8). At week 12, all patients had PMR-AS≤10 and received the low prednisone dosage. Median PMR-AS at weeks 12 and 24 was 4.5 (3.2-6.8) and 0.95 (IQR 0.4-2), respectively (p<0.001 vs baseline for both time points). No patient required rescue treatment. Positron emission tomography-CT showed significant improvements. The most common adverse events were transient neutropenia (n=3) and leucopenia (n=5); in one patient, the second tocilizumab infusion was omitted due to leucopenia. CONCLUSIONS: Tocilizumab monotherapy is effective in recent-onset PMR. Randomised controlled trials are warranted. TRIAL REGISTRATION NUMBER: NCT01713842.

During an epidemiological survey of viral encephalopathy and retinopathy (VER) in diseased sea bass Dicentrarchus labrax, a nodavirus isolate was recovered from net pen-reared sea bream Sparus aurata harboured in the same farming premises. After the virus was isolated and identified by immunofluorescence on SSN-1 cells, sequence analysis with a PCR product from the T4 region of the capsid protein gene indicated that the virus shared 100% identity with a pathogenic virus strain isolated from sea bass. Infection trials demonstrated the pathogenicity of the sea bream virus isolate for juvenile sea bass whereas sea bream infected with the same virus isolate remained asymptomatic even following intramuscular injection of virus. Nevertheless, the sea bream appeared to be a potential carrier of nodavirus, as juvenile sea bass became infected when maintained in a tank containing experimentally contaminated sea bream.

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EV's motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

The key objective of the ASIMOV project is the development and integration of advanced technological systems to achieve coordinated operation of an Autonomous Surface Craft (ASC) and an Autonomous Underwater Vehicle (AUV) while ensuring a fast communication link between the two vehicles. The ASC/AUV ensemble is being used to study the extent of shallow water hydrothermalism and to determine the patterns of community diversity at vents in the D. Joao de Castro (DJC) bank in the Azores.

From the recent availability of images recorded by synthetic aperture radar (SAR) airborne systems, automatic results of digital elevation models (DEMs) on urban structures have been published lately. This paper deals with automatic extraction of three-dimensional (3-D) buildings from stereoscopic high-resolution images recorded by the SAR airborne RAMSES sensor from the French Aerospace Research Center (ONERA). On these images, roofs are not very textured whereas typical strong L-shaped echoes are visible. These returns generally result from dihedral corners between ground and structures. They provide a part of the building footprints and the ground altitude, but not the building heights. Thus, we present an adapted processing scheme in two steps. First is stereoscopic structure extraction from L-shaped echoes. Buildings are detected on each image using the Hough transform. Then they are recognized during a stereoscopic refinement stage based on a criterion optimization. Second, is height measurement. As most of previous extracted footprints indicate the ground altitude, building heights are found by monoscopic and stereoscopic measures. Between structures, ground altitudes are obtained by a dense matching process. Experiments are performed on images representing an industrial area. Results are compared with a ground truth. Advantages and limitations of the method are brought out.

Newborn screening (NBS) for cystic fibrosis (CF) has been performed in many countries for as long as four decades and has transformed the routine method for diagnosing this genetic disease and improved the quality and quantity of life for people with this potentially fatal disorder. Each region has typically undertaken CF NBS after analysis of the advantages, costs, and challenges, particularly regarding the relationship of benefits to risks. The very fact that all regions that began screening for CF have continued their programs implies that public health and clinical leaders consider early diagnosis through screening to be worthwhile. Currently, many regions where CF NBS has not yet been introduced are considering options and in some situations negotiating with healthcare authorities as policy and economic factors are being debated. To consider the assigned question (where is it worthwhile?), we have completed a worldwide analysis of data and factors that should be considered when CF NBS is being contemplated. This article describes the lessons learned from the journey toward universal screening wherever CF is prevalent and an analytical framework for application in those undecided regions. In fact, the lessons learned provide insights about what is necessary to make CF NBS worthwhile.

Cystic fibrosis (CF) is a genetic disease with mutational changes leading to profound dysbiosis, both pulmonary and intestinal, from a very young age. This dysbiosis plays an important role in clinical manifestations, particularly in the lungs, affected by chronic infection. The range of microbiological tools has recently been enriched by metagenomics based on next-generation sequencing (NGS). Currently applied essentially in a gene-targeted manner, metagenomics has enabled very exhaustive description of bacterial communities in the CF lung niche and, to a lesser extent, the fungi. Aided by progress in bioinformatics, this now makes it possible to envisage shotgun sequencing and opens the door to other areas of the microbial world, the virome, and the archaeome, for which almost everything remains to be described in cystic fibrosis. Paradoxically, applying NGS in microbiology has seen a rebirth of bacterial culture, but in an extended manner (culturomics), which has proved to be a perfectly complementary approach to NGS. Animal models have also proved indispensable for validating microbiome pathophysiological hypotheses. Description of pathological microbiomes and correlation with clinical status and therapeutics (antibiotic therapy, cystic fibrosis transmembrane conductance regulator (CFTR) modulators) revealed the richness of microbiome data, enabling description of predictive and follow-up biomarkers. Although monogenic, CF is a multifactorial disease, and both genotype and microbiome profiles are crucial interconnected factors in disease progression. Microbiome-genome interactions are thus important to decipher.

A simple optical modulation scheme using a lithium niobate Mach-Zehnder modulator driven by a three level drive waveform is proposed. The two-level intensity modulated (IM) optical signal obtained possesses a smaller optical bandwidth and thus greater chromatic dispersion tolerance compared with existing two-level IM methods used for high data rate transmission (e.g. 10 Gbit/s).

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this letter, we report on the design, simulation and implementation of an active negative group delay circuit that operates at 1 GHz with a group delay and a gain, respectively, around <formula formulatype="inline"><tex>${-}$</tex> </formula>2 ns and 2 dB. Analytical formulas are proposed to demonstrate that the adopted topology is able to simultaneously achieve negative group delay (NGD) and gain while fulfilling active device constraints. The theoretical and simulated results are both validated by frequency measurements of a two-stage active microwave circuit. </para>

This paper describes a theoretical study of the small-signal modulation behavior of an injection-locked semiconductor laser. Illustrative examples are given, shelling a comparison between the free-running laser and the same laser with light injection. The results show that a substantial reduction of the chirp-to-power ratio (CPR) can be obtained, depending on both the injection level and the frequency detuning between the master and slave lasers. The behavior of the intensity modulation responses is also investigated, with the modulation conditions chosen in the dynamically stable locking range. It appears that the injection-locked laser may present a larger resonance frequency or modulation bandwidth with respect to those of the same laser under free-running operation.

This paper describes a sensor fault-tolerant control (FTC) for electric-vehicle (EV) powertrains. The proposed strategy deals with speed sensor failure detection and isolation within a reconfigurable induction-motor direct torque control (DTC) scheme. To increase the vehicle powertrain reliability regarding speed sensor failures, a maximum-likelihood voting (MLV) algorithm is adopted. It uses two virtual sensors [extended Kalman filter (EKF) and a Luenberger observer (LO)] and a speed sensor. Experiments on an induction-motor drive and simulations on an EV are carried out using a European urban and extraurban driving cycle to show that the proposed sensor FTC approach is effective and provides a simple configuration with high performance in terms of speed and torque responses.