5G無線通信における誤り訂正符号化方式の評価に関する研究

早大学位記番号:新8267早稲田大

A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems

The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods

Prevalence and seroprevalence of Plasmodium infection in Myanmar reveals highly heterogeneous transmission and a large hidden reservoir of infection.

Malaria incidence in Myanmar has significantly reduced over recent years, however, completeness and timeliness of incidence data remain a challenge. The first ever nationwide malaria infection and seroprevalence survey was conducted in Myanmar in 2015 to better understand malaria epidemiology and highlight gaps in Annual Parasite Index (API) data. The survey was a cross-sectional two-stage stratified cluster-randomised household survey conducted from July-October 2015. Blood samples were collected from household members for ultra-sensitive PCR and serology testing for P. falciparum and P. vivax. Data was gathered on demography and a priori risk factors of participants. Data was analysed nationally and within each of four domains defined by API data. Prevalence and seroprevalence of malaria were 0.74% and 16.01% nationwide, respectively. Prevalent infection was primarily asymptomatic P. vivax, while P. falciparum was predominant in serology. There was large heterogeneity between villages and by domain. At the township level, API showed moderate correlation with P. falciparum seroprevalence. Risk factors for infection included socioeconomic status, domain, and household ownership of nets. Three K13 P. falciparum mutants were found in highly prevalent villages. There results highlight high heterogeneity of both P. falciparum and P. vivax transmission between villages, accentuated by a large hidden reservoir of asymptomatic P. vivax infection not captured by incidence data, and representing challenges for malaria elimination. Village-level surveillance and stratification to guide interventions to suit local context and targeting of transmission foci with evidence of drug resistance would aid elimination efforts

Predictive Fuzzy Request Control Mechanism in Virtualized Server Environment

In virtualized server environment, variousservices are revealed to high rates of incomingrequests. The servers may become overloadedduring temporary traffic peaks when morerequests arrive than the server is considered for.Request drop rate increase more and morebecause of unfair overload prediction decisionamong admission control method. In this paper,Predictive Fuzzy Request Control mechanism(PFRCM) was developed for preventing andcontrolling server overloads and reducingrequest drop rate, and for getting response timeguarantee for user satisfaction. In this work,overload prediction decision is made byconsidering three main parameters. Theinformation of overload predictor supporteffectively for admission control (AC) process. Soproposed PFRCM can manage multiple requestsand make server more stable and rapid byreducing request drop rate. And then virtualizedserver architecture is set up in our test bed byusing Xen hypervisor technology

Server Workload Classification and Analysis with Machine Learning Algorithms

The main factor in measuring server performance isthe accuracy of detection mechanisms. Sever is neededto detect server overload condition accurately.Therefore, it can be satisfied customers by reducingrequest drop rate. Server overload detection would bean initial step of overload control system. In order toprovide such a detection mechanism, it is important tochoose the best classifier which is the most suitable forour dataset. Selecting correct classifier maximize theperformance of detection mechanism.In this paper, we present how server workloadclassification task is performed by using differentmachine learning classification methods and how thebest classifier improve overload detection mechanism.We make a synthetic dataset by using windowperformance monitor tool. Many classifiers areevaluated over synthetic dataset