Cellular network traffic prediction using exponential smoothing methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods.Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic.The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing.By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy

Enhanced Near-Infrared Fluorescent Sensing Using Metal-Dielectric-Metal Plasmonic Array

This work presents a numerical study of metal-dielectric-metal (MDM) plasmonic structure used to enhance a near-infrared fluorescent sensor. The MDM plasmonic structure consists of silver (Ag) subwavelength disk arrays on a thin silica (SiO2) spacing layer and 100-nm-thick-Ag film on a silicon (Si) substrate. The MDM plasmonic arrays with various structural parameters are designed and numerically investigated using the finite-difference time-domain (FDTD) method. Results show that the optical properties of designed structures are slightly dependent on the height of the Ag disk and strongly dependent on the Ag disk diameter and SiO2 thickness. In the near-infrared wavelength range, the proposed MDM plasmonic array has low ohmic loss and shows the high fluorescent emitting enhancement and directivity of about 16 times and 625.0, respectively, thus making MDM plasmonic array an alternative approach for near-infrared fluorescence bioimaging and biosensing devices

CSA: Thực hành nông nghiệp thông minh với khí hậu ở Việt Nam

During the last five years, Vietnam has been one of the countries most affected by climate change. Severe typhoons, flooding, cold spells, salinity intrusion, and drought have affected agriculture production across the country, from upland to lowland regions. Fortunately for Vietnam, continuous work in developing climate-smart agriculture has been occurring in research organizations and among innovative farmers and entrepreneurs. Application of various CSA practices and technologies to adapt to the impact of climate change in agriculture production have been expanding. However, there is a need to accelerate the scaling process of these practices and technologies in order to ensure growth of agriculture production and food security, increase income of farmers, make farming climate resilient, and contribute to global climate change mitigation. This book aims to provide basic information to researchers, managers, and technicians and extentionists at different levels on what CSA practices and technologies can be up scaled in different locations in Vietnam

CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods.Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic.The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing.By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy

On the maximum imbalance of binary sequences with low aperiodic autocorrelations

Binary sequences with low aperiodic autocorrelation function sidelobes, such as Barker, quasi-Barker, and minimum peak sidelobe sequences have found extensive applications in radar and communications systems. However, there is no systematic method to construct such desirable binary sequences except the use of time consuming and costly exhaustive computer search. In this paper, an upper bound on the maximum imbalance of binary sequences with low aperiodic autocorrelation is derived and is expressed in terms of the sequence length, the window size and the peak sidelobe levels. It sheds some light on the tradeoff between the sequence parameters involved and may be useful in developing efficient sequence searches. © 2011 IEEE

Signal Propagation of LoRa Technology Using for Smart Building Applications

International audienceIn this paper, the signal propagation measurement of LoRa devices that was realized in the indoor environment is presented. The scenario is setup at 12-floor dormitory B of Vietnam National University HCM. The devices using for measurement are a commercial LoRa gateway and the end nodes that designed by University of Information Technology. By placing the gateway at different locations of 12th floor and setting up the end nodes at 6 locations of 12 floors, the results of LoRa signal propagation are observed

The Application of Selective Hepatic Inflow Vascular Occlusion with Anterior Approach in Liver Resection: Effectiveness in Managing Major Complications and Long-Term Survival

Background. Hepatectomy is always a challenge to surgeons and requires an appropriate approach for specific tumors to achieve effective complication management. Selective hepatic pedicle clamping is more considerable strategy when comparing with total hepatic pedicle clamping in the balance between reducing blood loss and transfusion with causing the hepatic parenchyma damages (two main complications affecting liver resection result). Objectives. In this study, we aim to describe the application of selective hepatic inflow vascular occlusion (SHIVO) and anatomical anterior approach in liver resection and evaluate the results, focusing on intraoperative and postoperative complications. Methods. We enrolled 72 patients who underwent liver resection with SHIVO at Viet Duc University Hospital in 4-year period (2011-2014) and then followed up all of them until June 2020 (in 52.6±33 months; range, 2-105 months) or up to the time of death. All the patients were diagnosed with primary or secondary liver cancer, and their future remnant liver volume measured on 64-slice CT scan (dm3) to body weight kg>0.8% (for right hepatectomy). Perioperative parameters were collected and analyzed. Results. The average operation time was 196.2±62.2 minutes, and blood loss was 261.4±202.9 ml; total blood transfusion proportion during and after surgery was 16.7%. Complications accounted for 44.5% of patients in which pleural effusion was the most common one (41.7%). There were no liver failure and biliary fistula after surgery. No deaths were recorded during 30 days postoperatively. Average hospital stay was 11.4±3.7 days. Blood transfusions during the operation and major liver resection were the factors significantly affecting the percentage of complications after liver surgery in our study. In the last follow-up evaluation, 44 patients were dead and 28 patients were alive, in which 7 with recurrence and 21 without recurrence. The overall survival rate was 38.9%. Conclusion. SHIVO in anatomical liver resection is a safe and feasible approach to help resect precisely targeted tumors and manage several complications in liver resection