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This review was intended to investigate qCON and qNOX variations during outpatient laparoscopic cholecystectomy using remifentanil and desflurane without muscle relaxants, comparing these indices with ANI and MAC. Linear regression was used to examine the relationship between ANI and qNOX, as well as between qCON and MAC. At induction and extubation, higher values of qNOX and qCON were recorded than at other time-points where they were divided between 40 and 60 percent. Between ANI and qNOX, a poor but significant negative linear relationship was found. There was also a negative linear correlation between qCON and MAC, as well as between qNOX and remifentanil infusion rates. During general anesthesia using desflurane and remifentanil without muscle relaxants in patients undergoing ambulatory laparoscopic cholecystectomy, qCON and qNOX monitoring appears to be instructive. Although qCON correlated with MAC, the association of overall qCON and ANI was weak but significant.
Source link: https://doi.org/10.1007/s10877-022-00861-x
Deep learning like convolutional neural networks were playing a significant role as the high success rate rises. A new approach, called Double MAC, is a new approach to double the rate of computation of the CNN accelerators, which is the two operations of multiply and accumulation packed in one block. With this Double MAC strategy, the computation throughput is increased by twice, according to the company. Without compromiseing in the efficiency of output, the success improvement is varied on various CNN and ASIC sizes.
Source link: https://doi.org/10.1007/978-981-19-1669-4_28
Convolutional neural network is a component of deep learning and a significant workload that necessitates significant hardware expansion. The difference between deep learning workloads is that they are highly adaptable for smaller numerical errors and excel on low-fidelity hardware. Using the dual MAC scheme, we have found that a CNN spreadsheet can double the computational throughput of a CNN spreadsheet. Performance enhancements at the network layer are dependent on the CNN program and FPGA scale, from 14% to over 80% in a fully integrated next generation acceleration system, without significantly reducing production rates.
Source link: https://doi.org/10.1007/978-981-19-1669-4_6
Sensor nodes for wireless communications in those environments are widely available, and they are vital to improving network efficiency. The current MAC layer protocols for WSNs are all targeted at achieving high packet reception rates. In this research, we use machine learning techniques to forecast the success of the CSMA/CA MAC protocol. According to our results, the XGBoost prediction model is the most cost-effective supervised machine learning strategy for improving network performance at the MAC layer.
Source link: https://doi.org/10.1007/978-3-031-15191-0_23
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