Interfacing Detectors to Triggers And DAQ Electronics

The complete design of the front-end electronics interfacing LHCb detectors, Level-0 trigger and higher levels of trigger with flexible configuration parameters has been made for (a) ASIC implementation, and (b) FPGA implementation. The importance of approaching designs in technology-independent form becomes essential with the actual rapid electronics evolution. Being able to constrain the entire design to a few types of replicated components: (a) the fully programmable 3D-Flow system, and (b) the configurable front-end circuit described in this article, provides even further advantages because only one or two types of components will need to migrate to the newer technologies. To base on today's technology the design of a system such as the LHCb project that is to begin working in 2006 is not cost-effective. The effort required to migrate to a higher-performance will, in that case, be almost equivalent to completely redesigning the architecture from scratch. The proposed technology independent design with the current configurable front-end module described in this article and the scalable 3D-Flow fully programmable system described elsewhere, based on the study of the evolution of electronics during the past few years and the forecasted advances in the years to come, aims to provide a technology-independent design which lends itself to any technology at any time. In this case, technology independence is based mainly on generic-HDL reusable code which allows a very rapid realization of the state-of-the-art circuits in terms of gate density, power dissipation, and clock frequency. The design of four trigger towers presently fits into an OR3T30 FPGA. Preliminary test results (provided in this paper) meet the functional requirements of LHCb and provide sufficient flexibility to introduce future changes. The complete system design is also provided along with the integration of the front-end design in the entire system and the cost and dimension of the electronics

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

BACKGROUND: Acute Kidney Injury (AKI), a frequentcomplication of pateints in theIntensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive careactions.METHODS: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model.RESULTS: The deep learning model definedan area under the curve (AUC) of 0.89 (±0.01), sensitivity=0.8 and specificity=0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI.CONCLUSION: In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated inthe ICU setting to better manage, and potentially prevent, AKI episodes

Fast particles identification in programmable form at level-0 trigger by means of the 3D-Flow system

The 3D-Flow Processor system is a new, technology-independent concept in very fast, real-time system architectures. Based on either an FPGA or an ASIC implementation, it can address, in a fully programmable manner, applications where commercially available processors would fail because of throughput requirements. Possible applications include filtering-algorithms (pattern recognition) from the input of multiple sensors, as well as moving any input validated by these filtering-algorithms to a single output channel. Both operations can easily be implemented on a 3D-Flow system to achieve a real-time processing system with a very short lag time. This system can be built either with off-the-shelf FPGAs or, for higher data rates, with CMOS chips containing 4 to 16 processors each. The basic building block of the system, a 3D-Flow processor, has been successfully designed in VHDL code written in ''Generic HDL'' (mostly made of reusable blocks that are synthesizable in different technologies, or FPGAs), to produce a netlist for a four-processor ASIC featuring 0.35 micron CBA (Ceil Base Array) technology at 3.3 Volts, 884 mW power dissipation at 60 MHz and 63.75 mm sq. die size. The same VHDL code has been targeted to three FPGA manufacturers (Altera EPF10K250A, ORCA-Lucent Technologies 0R3T165 and Xilinx XCV1000). A complete set of software tools, the 3D-Flow System Manager, equally applicable to ASIC or FPGA implementations, has been produced to provide full system simulation, application development, real-time monitoring, and run-time fault recovery. Today's technology can accommodate 16 processors per chip in a medium size die, at a cost per processor of less than $5 based on the current silicon die/size technology cost

Investigating the kinematics of the unstable slope of Barberà de la Conca (Catalonia, Spain) and the effects on the exposed facilities by GBSAR and multi-source conventional monitoring

The paper presents a multi-source approach tailored for the analysis of ground movements affecting the village of Barberà de la Conca (Tarragona, Catalonia, Spain), where cracks on the ground and damage of different severity to structures and infrastructure was recorded. For this purpose, monitoring of ground displacements performed by topographic survey, geotechnical monitoring and remote sensing techniques (ground-based synthetic aperture radar, GBSAR) are combined with multi-temporal damage surveys and monitoring of cracks (crackmeters) to get an insight into the kinematics of the urban slope. The obtained results highlight the correspondence between the monitoring data and the effects on the exposed facilities induced by ground displacements, which seem to occur predominantly in the horizontal plane with diverging directions (northward and southward) from the main ground fracture crossing the centre of the village. The case study stands as a further contribution to fostering this kind of integrated approaches that via cross-validations can improve data reliability as well as enrich datasets for slope instability recognition and analysis, which are crucial to plan risk mitigation works