<p>In various VLSI based digital systems, on-chip interconnects have become the system bottleneck in state-of-the-art chips, limiting the performance of high-speed clock distributions and data communication devices in terms of propagation delay and power consumption. Increasing power requirements and power distribution to multi-core architectures is also posing a challenge to power distribution networks in the integrated circuits. Clock distribution networks for the switched capacitor converters becomes a non-trivial task and the increased interconnect lengths cause clock degradation and power dissipation. Therefore, this paper introduce low swing signaling schemes to decrease delay and power consumption. A comparative study presented of low voltage signaling schemes in terms of delay, power consumption and power delay product. Here, we have presented a power efficient signaling topology for driving the clocks to higher interconnect lengths.</p>
ABSTRACT Static Var Compensator (SVC) is a relatively new kind of reactive power and voltage control device. It can be used not only to improve dynamic and transient stability of power system, but is especially suitable for voltage control of long distance bulk power transmission lines. To enhance the performance of SVC, in addition to voltage control (main control), a supplementary control (auxiliary control) is required. Variable Structure (VS) Stabilizer is proposed in this paper, as supplementary controller to SVC. The machine, excitation and SVC dynamics are considered. In this paper, a systematic procedure for evaluating gains for sliding mode to occur is presented. The switching vector is constructed using pole-assignment method. Trial and error, and heuristics approach is completely avoided for evaluating the gains in the sliding mode
Background/Objectives: In this paper it offers a system that coordinates the sensor data to the cloud. There is an emphasis on using sensors in agriculture and offering a higher yielding crop capacity. Methods/Statistical Analysis: In this the need of the soil moisture is to sense the moisture. It makes the usage of soil moisture sensor to sense the moisture in the soil and a device that converts the input raw data to digital format. The received digital data is thus transferred to cloud. The method used for converting analog to digital is done by using myDAQ. The input is given to a computer, shows the data. The values that are displayed are in voltages, which are previously calibrated with sensor under different waterlevel conditions. Findings: Today there has been a lot of degradation in the cultivation as climate conditions have been changing continuously. Though there has been a development in productivity but there has been no improvement in the factors of agriculture. So a step needs to be taken to enhance these problems. The study shows us that how the technology can be increased to compensate this problem and overcome the various backdrops. Applications/Improvements: It can be further extended with implementation of GPS Technology and some of the sensors that can be used in future. Keywords: Cloud Technology and myDAQ (Device used for Processing), IOT, Soil Moisture Sensor
This study is based on experimental approach to linear displacement measurement using RGB color coding algorithm. This system is based on the auto-calibration procedure which can be implemented in a circuit, based on the temporal changes in the intensity of light, with the help of a light dependent resistor (LDR). The system consists of two LDRs and an LED placed on one side and an RGB color coded reflective paper on the opposite side. PIC microcontroller is used for powering the LED, processing of data for feedback control and to display the output on an LCD. LDR1 reading is used for displaying the relative linear distance, by mapping the voltage as a function of distance. This reading is used as a feedback to a PID controller to correct for the deviation in the measurement. Extensive experimental observations are conducted to analyze the reliability of the results in accordance to the wavelength of light reflected, the signal voltage and power output of the system. Investigation of the optimum positioning of the LED and the reflective RGB color coded paper is performed by repeatability analysis and hysteresis effects. Furthermore, the efficiency of the system is increased by implementing a PID controller upon investigating the different controller design, viz. P, PI and PID. A high resolution of 0.1 [mm] is obtained for such a simple and economical system, thereby making it highly efficient, in both minute measurements as well as over the entire bandwidth range of the visible light spectrum.
Introduction: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest. Methods: In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine (i.e., the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. Our model was trained on hourly-averaged measurements of these internal subparameters. We compared this model’s performance to the modified Westhall qualitative EEG scoring framework. Results: Maximum prognostic accuracy in the Derivation Cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, our model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristic curve: 0.86, accuracy: 0.87, sensitivity: 0.83, specificity: 0.88, positive predictive value: 0.71, negative predictive value: 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest. Conclusions: In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative gold-standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.
The dural puncture epidural (DPE) technique has a faster onset, better sacral spread, and improved bilateral coverage when compared to the conventional epidural (EPL) technique. Whether these qualities translate into a lower bupivacaine dose to provide initial analgesia is unknown. We sought to determine the effective dose of bupivacaine to achieve initial (first 30 minutes) labor analgesia in 90% of patients (ED90) with the DPE and EPL techniques, using a biased-coin, sequential allocation method.
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