Auto-zero stabilized CMOS amplifiers for very low voltage or current offset

In this paper, we present two amplifiers designed in CMOS technology and including an auto-zero architecture for very low offset control

A low power 12-bit and 25-MS/s pipelined ADC for the ILC/Ecal integrated readout

International audienc

A very low power and low signal 5 bit 50 M samples/s double sampling pipelined ADC for Monolithic Active Pixel Sensors in high energy physics and biomedical imaging applications

International audienc

A low power and low signal 4 bit 50MS/s double sampling pipelined ADC for Monolithic Active Pixel Sensors

International audienceA 4 bit very low power and low incoming signal analog to digital converter (ADC) using a double sampling switched capacitor technique, designed for use in CMOS monolithic active pixels sensor readout, has been implemented in 0.35μm CMOS technology. A non-resetting sample and hold stage is integrated to amplify the incoming signal by 4. This first stage compensates both the amplifier offset effect and the input common mode voltage fluctuations. The converter is composed of a 2.5 bit pipeline stage followed by a 2 bit flash stage. This prototype consists of 4 ADC double-channels; each one is sampling at 50MS/s and dissipates only 2.6mW at 3.3V supply voltage. A bias pulsing stage is integrated in the circuit. Therefore, the analog part is switched OFF or ON in less than 1μs. The size for the layout is 80μm*0.9mm. This corresponds to the pitch of 4 pixel columns, each one is 20μm wide

A low power and low signal 5-bit 25MS/s pipelined ADC for monolithic active pixel sensors

For CMOS monolithic active pixels sensor readout, we developed a 5 bit low power analog to digital converter using a pipelined architecture. A non-resetting sample and hold stage is included to amplify the signal by a factor of 4. Due to the very low level of the incoming signal, this first stage compensates both the amplifier offset effect and the input common mode voltage dispersion. The converter consists of three 1.5 bit sub-ADC and a 2 bit flash. We present the results of a prototype, made of eight ADC channels. The maximum sampling rate is 25MS/s. The total DC power consumption is 1.7mW/channel on a 3.3V supply voltage recommended for the process. But at a reduced 2.5V supply, it consumes only 1.3mW. The size of each ADC channel layout is only 43μm*1.43mm. This corresponds to the pitch of two pixel columns each one would be 20μm wide. The full analog part of the converter can be quickly switched to a standby idle mode in less than 1μs; thus reducing the power dissipation to a ratio better than 1/1000. This fast shutdown is very important for the ILC vertex detector as the total DC power dissipation becomes directly proportional to the low beam duty cycle

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

A low power and low signal 4 bit 50MS/s double sampling pipelined ADC for Monolithic Active Pixel Sensors

soumis à JINSTA 4 bit very low power and low incoming signal analog to digital converter (ADC) using a double sampling switched capacitor technique, designed for use in CMOS monolithic active pixels sensor readout, has been implemented in 0.35µm CMOS technology. A non-resetting sample and hold stage is integrated to amplify the incoming signal by 4. This first stage compensates both the amplifier offset effect and the input common mode voltage fluctuations. The converter is composed of a 2.5 bit pipeline stage followed by a 2 bit flash stage. This prototype consists of 4 ADC double-channels; each one is sampling at 50MS/s and dissipates only 2.6mW at 3.3V supply voltage. A bias pulsing stage is integrated in the circuit. Therefore, the analog part is switched OFF or ON in less than 1µs. The size for the layout is 80µm*0.9mm. This corresponds to the pitch of 4 pixel columns, each one is 20µm wide

ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText