Monolithic ultrasound fingerprint sensor.

This paper presents a 591×438-DPI ultrasonic fingerprint sensor. The sensor is based on a piezoelectric micromachined ultrasonic transducer (PMUT) array that is bonded at wafer-level to complementary metal oxide semiconductor (CMOS) signal processing electronics to produce a pulse-echo ultrasonic imager on a chip. To meet the 500-DPI standard for consumer fingerprint sensors, the PMUT pitch was reduced by approximately a factor of two relative to an earlier design. We conducted a systematic design study of the individual PMUT and array to achieve this scaling while maintaining a high fill-factor. The resulting 110×56-PMUT array, composed of 30×43-μm2 rectangular PMUTs, achieved a 51.7% fill-factor, three times greater than that of the previous design. Together with the custom CMOS ASIC, the sensor achieves 2 mV kPa-1 sensitivity, 15 kPa pressure output, 75 μm lateral resolution, and 150 μm axial resolution in a 4.6 mm×3.2 mm image. To the best of our knowledge, we have demonstrated the first MEMS ultrasonic fingerprint sensor capable of imaging epidermis and sub-surface layer fingerprints

A dicing free SOI process for MEMS devices

This paper presents a full wafer, dicing free, dry release process for MEMS silicon-on-insulator (SOI) sensors and actuators. The developed process is particularly useful for inertial sensors that benefit from a large proof mass, for example accelerometers and gyroscopes. It involves consecutive front and backside deep reactive ion etching (DRIE) of the substrate to define the device features, release holes, and trenches. This is followed by hydrofluoric acid vapor phase etching (HF VPE) to release the proof mass and the handle wafer underneath to allow vertical displacements of the proof mass. The release process also allows the devices to be detached from each other and the substrate without the need of an extra dicing step that may damage the delicate device features or create debris. In the work described here, the process is demonstrated for the full wafer release of a high performance accelerometer with a large proof mass measuring 4 × 7 mm2. The sensor was successfully fabricated with a yield of over 95

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges

A wireless RF CMOS mixed-signal interface for soil moisture measurements

This paper describes a wireless RF CMOS interface for soil moisture measurements. The interface basically comprises a Delta-Sigma (ΔΣ) modulator for acquiring an external sensor signal, and a RF section where data is transmitted to a local processing unit. The ΔΣ modulator is a single-bit, second-order modulator and it is implemented using switched-capacitors techniques in a fully-differential topology. With a sampling frequency of 423.75 kHz and an oversampling ratio (OSR) of 256, the modulator achieves a dynamic range of 98.7 dB (16.1 bit). The output of the modulator is applied to a counter, as a first-order decimation filter, and the result is stored. Prior to transmission, data is encoded as a pulse width modulated signal and assembled in a frame containing preamble and checksum control fields. This frame is then transmitted through a power amplifier operating at 433.92 MHz in class-E mode. To evaluate the ΔΣ modulator performance, the bitstream was acquired and transferred to a personal computer to perform digital filtering and decimation using MATLAB. The soil moisture sensor is based on dual-probe heat-pulse (DPHP) method and is implemented by using an integrated temperature sensor and a heater. After applying a heat-pulse for a fixed period of time, the temperature rise, that is a function of soil moisture, generates a differential voltage that is amplified and applied to the mixed-signal interface input. The described interface can also be used with other kinds of environmental sensors in a wireless sensors network. The CMOS mixed-signal interface has been implemented in a single-chip using a standard CMOS 0.7 μm process (AMI C07M-A, n-well, 2 metals and 1 poly)

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner

Nacije i brojevi: osnovno matematičko obrazovanje kao instrument nacionalizacije

One of the central elements of the nation-building process in the 19th century was the attempt to homogenize the citizenry, i.e. to fabricate national citizens. Besides the military and church, schools were considered to be the main agencies capable of achieving this national homogenization. In this paper, focusing on the education in Switzerland and France, I argue that elementary mathematics education was also used for this particular purpose. I make the case that throughout the 19th century mathematics education became a way to familiarize the people with a standardized language – a language that was supposed to help them master their specific social, cultural and political realities.Jedan od središnjih elemenata procesa izgradnje nacije u 19. stoljeću bio je pokušaj homogenizacije građanstva, tj. stvaranja nacionalnih građana. Osim vojske i crkve, škole su smatrane glavnim sredstvom u postizanju nacionalne homogenizacije. U ovom radu, koji se fokusira na obrazovanje u Švicarskoj i Francuskoj, tvrdim da je elementarno matematičko obrazovanje također korišteno za ovu posebnu svrhu. Dokazujem da je tijekom 19. stoljeća matematičko obrazovanje postalo način upoznavanja ljudi sa standardiziranim jezikom - jezikom koji im je trebao pomoći pri svladavanju vlastitih specifičnih socijalnih, kulturoloških i političkih stvarnosti

Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breas

Using data mining for wine quality assessment

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physico- chemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certifi- cation step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a re- gression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its do- main. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selec- tion and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regres- sion and neural network methods. Such model is useful for understand- ing how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production