This paper presents a pulse-modulated Class-E RF digital power amplifier (DPA) operating in the 1.4 GHz to 1.9 GHz frequency range, targeting GNSS and NB-IoT frequency bands. The proposed DPA achieves a saturated output power (Psat) of 24.2 dBm and a peak power-added efficiency (PAE) of 40% with the DPA core occupying 0.56mm2. It supports the transmission of GMSK, BPSK, π/4-DQPSK and QPSK modulated signals using a polar transmitter configuration. The pulse modulation approach enhances average power efficiency, delivering an average PAE of 37.7% with an error vector magnitude (EVM) of 0.7% for GMSK signals at a data rate of 30 Msymbols/s. For π/4-DQPSK signals at the same data rate, the DPA achieves an average PAE of 36.7% and an EVM of 7.7%. With the integration of a power combiner, the output power dynamic range can be extended by 9.2 dB while the average PAE of the proposed design outperforms by 4% the conventional Class-E RF PA. The chip is fabricated using GlobalFoundries’ 22nm FD-SOI CMOS technology (GF22FDX).

This paper presents a CMOS transceiver for field monitoring in magnetic resonance imaging (MRI) scanners. The integrated circuit (IC) is optimized for usage in magnetic field strengths of 3T and 7T. The transceiver integrates a transmitter capable of exciting a nuclear magnetic resonance (NMR) field probe up to a peak-to-peak voltage of 143V thanks to a resonant tank. This NMR-on-a-chip also implements a low-IF quadrature receiver. The receiver, including transimpedance amplifiers (TIA) and measurement output buffers, consumes only 14.1mW. The measured signals acquired in a full-body scanner result in an input-referred noise of ${\text{390pV/}}\sqrt {{\text{Hz}}} $ owing to a resonant network passively amplifying the probe signal to the low-noise amplifier (LNA) input. The overall transceiver is integrated into a 65nm CMOS process covering a total area of 1.5mm2.

Ultra-wideband (UWB) technology has emerged as one of the most promising localization solutions, finding extensive use in industrial settings and a growing presence in the Internet of Things (IoT) and mobile devices, including smartphones. What sets UWB apart in the realm of localization and positioning is its impressive centimeter-level accuracy, enabling the development of highly secure, anti-spoofing applications. However, the primary challenge hindering the adoption of UWB in battery-operated and battery-less devices is its relatively high power consumption, especially during idle listening. Current approaches often adopt duty cycling, aiding in power conservation at the cost of increased latency. This paper presents a wake-up radio integrated circuit with addressing capabilities specifically designed for ultra-wideband communication and ranging. This wake-up radio seamlessly functions with any standard commercial UWB transceiver, generating on-off keying messages, eliminating the need for a separate radio. Our proposed solution achieves an impressive sensitivity of −86dBm while consuming an average of 36nW of power in waiting-for-wake-up mode and 1mW during signal sampling. The system supports full asynchronous communication, ensuring millisecond-level latency and maintaining an always-on capability. The digital interface of the design allows one to choose the desired trade-off between latency and power consumption, promising substantial energy savings for applications with strict power constraints.

This paper will present the status of the on-going and planned mission concept studies in the period 2023 to 2025 in the frame of the ESA’s Future Earth Observation Programme (FutureEO).The European Space Agency (ESA) is conducting preparatory activities on innovative Earth Observation (EO) missions to foster better scientific understanding of the Earth system through research missions, respond to the requirements of the operational users via operational missions and assess the potential of "Newspace" approach with smallsat Earth Observation concepts to boost innovation through the Φsat missions.ESA’s program of EO research missions comprises Earth Explorers, Scouts and Mission of Opportunity. Earth Explorer missions objective is to advance Earth science by providing answers to key scientific questions on the Earth system. Up to date, five Earth Explorers have been launched and another five are in different stages of development. ESA’s Earth Explorer 11 mission candidates (CAIRT, Nitrosat, SEASTAR and WIVERN) are undergoing Phase 0 activities while preparatory activities to study mission concepts for the Earth Explorer 12 are expected to start in 2024.Missions of Opportunity are research missions implemented under cooperation frameworks with Space Agencies outside Europe and Canada. Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA-NASA cooperation in the framework of MAGIC (MAss change and Geosciences International Constellation).Scouts are small budget research mission based on small satellites or CubeSats, implemented in three years and complementing Earth Explorers. Two mission concepts, TANGO and NanoMagSat, are currently undergoing risk retirement activities.Preparatory activities for operational mission are underway for the next generation of Sentinels in the frame of the Copernicus programme (e,g. Sentinel-2 Next Generation and Sentinel-3 Next Generation Optical, which are currently in phase 0) in close coordination with the European Commission). Preparatory activities are planned in the coming years for the next generation of meteorological missions (Meteosat 4thGeneration and EPS/MetOp 3rdGeneration) in close coordination with EUMETSAT.Φsat missions are CubeSat missions aimed to demonstrate radical and new technologies or remote sensing concepts in space, such as Φsat -2, which is currently under development and is focused on onboard Artificial Intelligence applications.

GNSS Reflectometry has been developing rapidly as an L-Band remote sensing technology suitable for small satellites and continues to find applications over ocean, ice and land. The TDS-1 [1] and CYGNSS missions [2] were primarily flown to target wind speed over the ocean but subsequently enabled demonstration of the potential for cryospheric and hydrological applications. The proposed HydroGNSS ESA Scout mission concept [3] has been developed to address land-based hydrological essential climate variables, specifically soil moisture, inundation, freeze / thaw state and biomass, as required by GCOS [4]. New techniques anticipated for HydroGNSS include the use of Galileo signals, coherent reflection sensing, dual polarisation and dual frequency reflectometry exploration.

SUMMARY: Many factors can influence results in clinical research, in particular bias in the distribution of samples prior to biochemical preparation. Well Plate Maker is a user-friendly application to design single- or multiple-well plate assays. It allows multiple group experiments to be randomized and therefore helps to reduce possible batch effects. Although primarily fathered to optimize the design of clinical sample analysis by high throughput mass spectrometry (e.g. proteomics or metabolomics), it includes multiple options to limit edge-of-plate effects, to incorporate control samples or to limit cross-contamination. It thus fits the constraints of many experimental fields. AVAILABILITY AND IMPLEMENTATION: Well Plate Maker is implemented in R and available at Bioconductor repository (https://bioconductor.org/packages/wpm) under the open source Artistic 2.0 license. In addition to classical scripting, it can be used through a graphical user interface, developed with Shiny technology.

BACKGROUND: The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. RESULTS: We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. CONCLUSIONS: Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.

A transimpedance amplifier that provides 109 dBOhm within 1% flatness inheriting cross coupled biquad filtering cell is presented. The nested structure has single ended bidirectional current input and differential output, which achieves 4 uA input range and a bandwidth of 10 kHz. The structure features noise shaping by inserting in-band a zero for the noise transfer function. This results in 56 dB SFDR and 1pA/sqrt(Hz) low frequency noise. The circuit occupies 0.02 mm2and consumes 160 nW.

We present ongoing work on a platform for mobile health and implantable telemetry devices with powerful point-of-contact processing capabilities based on our VivoSoC multi-sensor medical instrumentation SoC, a custom power management IC, and only a few additional components - allowing the realisation of sub-ccm devices. We detail the powerful yet efficient acquisition and parallel processing capabilities on the example of first applications, demonstrate system- and chip-level power management and address the application-layer considerations of a file system optimised for serial flash devices and job scheduling.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: [email protected], [email protected], [email protected].

This paper presents a fast-response low-dropout (LDO) regulator using a current feedback loop to realize an adaptive biasing error amplifier. With the proposed current feedback loop, the LDO regulator achieves 34dB higher loop gain, 46% faster transient response, and about 12dB higher power supply rejection above 1MHz than the one with fixed biasing. The adaptively-biased LDO regulator is compensated by a simple Miller compensation using adaptive nulling resistor (SMCANR). The proposed compensation network can realize robust frequency compensation and reduce voltage spikes during load transients. An expanded stability analysis using the Routh-Hurwitz criterion gives designers additional freedom for a stable design. Designed in a 0.13μm CMOS technology for a minimum dropout voltage of 200mV, the LDO regulator supplies 1.2V at 98.2% current efficiency and 10mA load current.

Combining pieces of information provided by several sources without or with little prior knowledge about the behavior of the sources is an old yet still important and rather open problem in the belief function theory. In this paper, we propose an approach to select the behavior of sources based on a very general and expressive fusion scheme, that has the important advantage of making clear the assumptions made about the sources. The selection process itself relies on two cornerstones that are the notions of specificity and consistency of a knowledge representation, and that we adapt to the considered fusion scheme. We illustrate our proposal on different examples and show that the proposed approach actually encompasses some important existing fusion strategies.

Abstract Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing. Availability: pRoloc is implemented in the R language and available under an open-source license from the Bioconductor project (http://www.bioconductor.org/). A vignette with a complete tutorial describing data import/export and analysis is included in the package. Test data is available in the companion package pRolocdata. Contact: [email protected]

As Dempster–Shafer theory spreads in different application fields, and as mass functions are involved in more and more complex systems, the need for algorithms randomly generating mass functions arises. Such algorithms can be used, for instance, to evaluate some statistical properties or to simulate the uncertainty in some systems (e.g., data base content, training sets). As such random generation is often perceived as secondary, most of the proposed algorithms use straightforward procedures whose sample statistical properties can be difficult to characterize. Thus, although such algorithms produce randomly generated mass functions, they do not always produce what could be expected from them (for example, uniform sampling in the set of all possible mass functions). In this paper, we briefly review some well-known algorithms, explaining why their statistical properties are hard to characterize. We then provide relatively simple algorithms and procedures to perform efficient random generation of mass functions whose sampling properties are controlled.

Recently, the problem of measuring the conflict between two bodies of evidence represented by belief functions has known a regain of interest. In most works related to this issue, Dempster's rule plays a central role. In this paper, we propose to study the notion of conflict from a different perspective. We start by examining consistency and conflict on sets and extract from this settings basic properties that measures of consistency and conflict should have. We then extend this basic scheme to belief functions in different ways. In particular, we do not make any a priori assumption about sources (in)dependence and only consider such assumptions as possible additional information.

A new classification technique, PerTurbo, has been investigated in the context on hyperspectral remote sensing images context. In this framework, each class is characterised by its Laplace-Beltrami operator, then approximated by the spectrum of K(S), whose terms are derived from the Gaussian kernel. The method is very simple, easy to implement and involves few parameters to tune. It also allows the definition of a simple multi-class strategy, and, as a class-wise classification method, the addition of a new class does not requires the re-training of the pre-existing class models. We conducted experiments on two datasets: results for Pavia Centre dataset are encouraging, while results obtained on Pavia University show that SVM clearly outperforms PerTurbo. Nevertheless, we believe that this difference comes from a bad parametrization of the algorithm (for which we used a rule of thumb, contrarily to the SVM procedure which was fully optimized). Hence, a systematic search for the optimal value of the parameter would improve the results. Moreover, there are several other possible improvements coming from the fields of regularization methods of dimensionality reduction techniques which let us think that this first experiment is promising. In the near future, we also plan to investigate rules leading to a better choice of s. We are also interested in studying the behavior of PerTurbo when the classes are heterogeneous with only few training samples available, or when the classes in the training set are highly unbalanced.

Recognizing the importance of interfacing a variety of sensors and networking such sensors around the body area and by cellular services, a Swiss project within the Nano-Tera.ch Initiative is dedicated to developing a platform of circuit technologies for medical data acquisition and communication.

In this paper, a novel rejection strategy is proposed to optimize the reliability of an handwritten word recognition system. The proposed approach is based on several steps. First, we combine the outputs of several HMM classifiers using the Dempster-Shafer theory (DST). Then, we take advantage of the expressivity of mass functions (the counter part of probability distributions in DST) to characterize the quality/reliability of the classification. Finally, we use this characterization to decide whether a test word is rejected or not. Experiments carried out on RIMES and IFN/ENIT datasets show that the proposed approach outperforms other state-of-the-art rejection methods.

The classification process in handwriting recognition is designed to provide lists of results rather than single results, so that context models can be used as post-processing. Most of the time, the length of the list is determined once and for all the items to classify. Here, we present a method based on Dempster-Shafer theory that allows a different length list for each item, depending on the precision of the information involved in the decision process. As it is difficult to compare the results of such an algorithm to classical accuracy rates, we also propose a generic evaluation methodology. Finally, this algorithm is evaluated on Latin and Arabic handwritten isolated word datasets.

The Cognitive Radio (CR) concept relies on observing the surrounding radio spectrum, sensing changes and reacting accordingly. therefore a CR-enabled User Equipment (UE) demands enhanced strategies for efficient interference detection, rapid spectrum evaluation and a highly flexible, reconfigurable architecture to be able to support the multitude of today's as well as future radio access technologies (RATs). It is shown that classical methods of spectrum analysis like the Fast Fourier Transformation (FFT) have critical shortcomings in fullfilling the requirements of CR and how analog keyblocks like the wideband ΣΓ-ADC can be implemented. The pivotal focus of this paper is to propose alternative approaches suitable for integration into the Digital Front-End (DFE) and the UE.

Cued speech is a specific visual coding that complements oral language lip-reading, by adding static hand gestures (a static gesture can be presented on a single photograph as it contains no motion). By nature, cued speech is simple enough to be believed as automatically recognizable. Unfortunately, despite its static definition, fluent cued speech has an important dynamic dimension due to co-articulation. Hence, the reduction from a continuous cued speech coding stream to the corresponding discrete chain of static gestures is really an issue for automatic cued speech processing. We present here how the biological motion analysis method are presented has been combined with a fusion strategy based on the belief theory in order to perform such a reduction.

Support vector machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method does not use the entire information from the classifier responses. In this paper, we present a method which partially prevents these two losses of information by applying belief theories (BTs) to SVM fusion, while keeping the efficient aspect of the classical methods

As part of the THIMP project (Telephony for Hearing- IMpaired People), we aim at automatically analyzing Cued Speech [1] and translating it into oral spoken language. This work focuses on vowel classification and will be part of this transcoding process as a preprocessing step of the input data analysis. Its objective is to identify vowels produced by a speaker pronouncing and coding in Cued Speech a set of French sentences, knowing: - The Cued Speech Hand Placement, - The analysis of defined Labial Parameters. Here, we will show that the crossing of these two sources of information allows to automatically identify vowels. These results have to be compared to performances of hearingimpaired people in perception of Cued Speech.

An innovative approach for testing PLLs in open loop-mode is presented. The operational method consists of ramping the PLL's power supply by means of a periodic sawtooth signal. The reference and feedback inputs of the PLL in open-loop mode are connected to the clock reference signal or to ground. Then, the corresponding quiescent current, clock output, and oscillator control voltage signatures are monitored and sampled at specific times. When the power supply is swept, all transistors are forced into various regions of operation causing the sensitivity of the faults to the specific stimulus to be magnified. The developed method of structural testing for PLLs yields high fault coverage results making it a potential and attractive technique for production wafer testing.

An optimal design procedure to achieve minimum power consumption for a given technology and gain bandwidth is presented. Regulated cascode gain enhancement is used to ensure sufficient DC-gain at minimum gate length transistors. To validate the approach five folded cascode OTA's have been implemented, spanning a bias range of 1µA - 10mA, with measured unity-gain bandwidths within 20% of the designed value. For 17 mW at 3 V, a 0.5 µm CMOS OTA achieves 630 MHz with 51° phase margin. The method has been applied in the design of a 3rd order ΔΣ modulator for GSM receivers. The modulator consumes 2.8 mW at 3 V and has a dynamic range of 86 dB for a 100 kHz input signal bandwidth.