Microelectronic cmos implementation of a machine learning technique for sensor calibration

An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18μm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error er below 3% within an input span of 130°C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm 2 . The digital programmability of the network weights provides flexibility when a sensor change is required

Low Power CMOS Chopper Preamplifier Based on Source-Degeneration Transconductors

This paper describes the design of a low-power, low-noise flicker CMOS chopper preamplifier for sensor signal conditioning. The core amplifier and the Gm-C output low pass filter of the proposed fully differential preamplifier are based on a source degeneration transconductor. The circuit was designed in a standard 0.18µm CMOS process with 1.8V supply voltage. It shows 42dB gain, 1 kHz bandwidth and a total power consumption of 84 µW. The proposed configuration achieves a noise efficiency factor of 4.6 and a total input-referred noise of 560 nVrms integrated from 0.1 to 1 kHz

High-Linearity Self-Biased CMOS Current Buffer

A highly linear fully self-biased class AB current buffer designed in a standard 0.18 mu m CMOS process with 1.8 V power supply is presented in this paper. It is a simple structure that, with a static power consumption of 48 mu W, features an input resistance as low as 89 Omega, high accuracy in the input-output current ratio and total harmonic distortion (THD) figures lower than -60 dB at 30 mu A amplitude signal and 1 kHz frequency. Robustness was proved through Monte Carlo and corner simulations, and finally validated through experimental measurements, showing that the proposed configuration is a suitable choice for high performance low voltage low power applications

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations. Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches.Comment: Submitted to Pattern Recognitio

Efficacy and safety of lurbinectedin and doxorubicin in relapsed small cell lung cancer. Results from an expansion cohort of a phase I study

Background A phase I study found remarkable activity and manageable toxicity for doxorubicin (bolus) plus lurbinectedin (1-h intravenous [i.v.] infusion) on Day 1 every three weeks (q3wk) as second-line therapy in relapsed small cell lung cancer (SCLC). An expansion cohort further evaluated this combination. Patients and methods Twenty-eight patients with relapsed SCLC after no more than one line of cytotoxic-containing chemotherapy were treated: 18 (64%) with sensitive disease (chemotherapy-free interval [CTFI] ≥90 days) and ten (36%) with resistant disease (CTFI <90 days; including six with refractory disease [CTFI ≤30 days]). Results Ten patients showed confirmed response (overall response rate [ORR] = 36%); median progression-free survival (PFS) = 3.3 months; median overall survival (OS) = 7.9 months. ORR was 50% in sensitive disease (median PFS = 5.7 months; median OS = 11.5 months) and 10% in resistant disease (median PFS = 1.3 months; median OS = 4.6 months). The main toxicity was transient and reversible myelosuppression. Treatment-related non-hematological events (fatigue, nausea, decreased appetite, vomiting, alopecia) were mostly mild or moderate. Conclusion Doxorubicin 40 mg/m(2) and lurbinectedin 2.0 mg/m(2) on Day 1 q3wk has shown noteworthy activity in relapsed SCLC and a manageable safety profile. The combination is being evaluated as second-line therapy for SCLC in an ongoing, randomized phase III trial. Clinical trial registration www.ClinicalTrials.gov code: NCT01970540. Date of registration: 22 October, 2013