Duplication elimination in cache-uplink transmission over B5G small cell network

In this era of the digital world, data play a central role and are continuously challenging spectrum efficiency. With the introduction of enriched multimedia user-generated content, the challenges are even more aggravated. In this vein, uplink caching is considered as one of the promising solutions to effectively cater the user’s demands. One of the main challenges for uplink caching is duplication elimination. In this paper, a cache enabled uplink transmission with a duplication elimination scheme is proposed. The proposed scheme matches the mobile’s data to be uploaded with the cached contents both at mobile station (MS) and small base station (SBS). In contrast to existing techniques, the proposed scheme broadcasts the cached contents at an SBS to all the MSs under its footprint. This provides MS an opportunity to exploit the list of cached contents before uploading its data. A MS only uploads its data if it is not already cached at an SBS. This significantly reduces duplication before the real transmission takes place. Furthermore, the proposed technique reduces energy consumption in addition to improving spectral efficiency and network throughput. Besides, a higher caching hit ratio and lower caching miss ratio are also observed as compared to other schemes. The simulation results reveal that the proposed scheme saves 97% energy for SBS, whereas 96–100% energy is saved for MS on average.

Application of Combining 3D Model and Survey on Site to Simulate Dome Lifting

Dome lifting is one of the most important milestones for nuclear power project. The lifting operation must be properly planned and carried out in safety manner. The simulation and analysis are usually done before lifting activities to ensure the smooth lifting implementation. By using of 3D model and survey data on site to assist dome lifting, the risks related to lifting activities can be discovered and the risk elimination scheme can be formulated at early stage. Combining with Power Plant Design Management System (PDMS), design inspection software NAVISWORKS and survey data on site, the accuracy of simulation and verification can be increased, the deviations and non-conformities can be easily found. Through the implementation of this technical scheme, it helps to ensure the smooth and safe implementation of the milestone of dome lifting for nuclear power project. This paper gives an example for combining 3D model and survey on site to simulate dome lifting, methods and suggestions are given for guidance for other projects.

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.

Implementation of the Marchenko Multiple Elimination algorithm

The Marchenko multiple elimination and transmission compensation schemes retrieve primary reflections in the two-way traveltime domain without model information or using adaptive subtraction. Both schemes are derived from projected Marchenko equations and similar to each other, but use different time-domain truncation operators. The Marchenko multiple elimination scheme retrieves a new dataset without internal multiple reflections. The transmission compensated Marchenko multiple elimination scheme does the same and additionally compensates for transmission losses in the primary reflections. Both schemes can be solved with an iterative algorithm based on a Neumann series. At each iteration, a convolution or correlation between the projected focusing function and the measured reflection response are performed and after each convolution or correlation, a truncation in the time domain is applied. After convergence, the resulting projected focusing function is used for retrieving the transmission compensated primary reflections and the projected Green’s function is used for the physical primary reflections. We demonstrate that internal multiples are removed by using time-windowed input data that only contain primary reflections. We evaluate both schemes in detail and develop an iterative implementation that reproduces the presented numerical examples. The software is part of our open-source suite of programs and fits into the Seismic Unix software suite of the Colorado School of Mines.