Performance Analysis of the Unary Coding Aided SWIPT in a Single-User Z-Channel

Radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technique for satisfying both the communication and charging requests of the massively deployed IoT devices. Different from the physical layer and the medium-access-control layer design for coordinating the SWIPT in the RF band, we study its coding-level control from the information theoretical perspective. Due to its practical implementation of the decoder and its flexibility on the codeword structure, the unary code is chosen as a potential joint information and energy encoder. By conceiving the classic Z-channel, the mutual information and the energy harvesting performance of the unary coding aided SWIPT transceiver is analysed. Furthermore, the optimal codeword distribution is obtained for maximising the mutual information, while satisfying the minimum energy harvesting requirement. Our theoretical analysis and the optimal coding design are demonstrated by the numerical results

Research on Multiple Complex Data Processing Methods Based on OpenStack Cloud Platform

OpenStack is an open source cloud computing management platform project that supports almost all types of cloud environment. It can achieve data processing services among the interactive information storages, and it can also be stored in the virtual machine of cloud computing platform in various services. When performing complex data combination processing, each service cooperates with other services according to the interaction information, and finally completes the processing of complex data

Effects of Light and pH on Cell Density of Chlorella Vulgaris

AbstractImproving the cell density of microalgae cultivation is one of the keys to reduce the cost of microalgae biodiesel. Many studies showed that the adjustment of pH and light intensity could increase cell density. The effects of light intensities, pH and pH adjustments on the growth of Chlorella vulgaris were studied in light incubator. The light intensities were set at 3960, 7920 and 11920lux; values of pH were 7, 8, 9 and 10 respectively; and pH adjustment methods included without and with pH control. Results show that: (1) In terms of light intensity, without pH control, the cell density under 3960lux is highest. With pH control, the cell density under 7920lux is higher than other levels. (2) In terms of pH, under the same light intensity, the cell density with pH control at 10 is highest, which indicates the light intensity will not affect the optimal pH value. And the pH fluctuates between 10 and 10.5 with pH control at 10, which is the most suitable range of pH for Chlorella vulgaris cultivation. (3) For pH adjustment methods, under 7920lux, the cell density with pH control at 10.0 is 56.7% higher than that with initial pH at 10.0, while the cell density with initial pH at 7.0 is 34.7% than that with pH control at 7.0, which indicates the method with pH control at values of the optimum pH makes better growth of microalgae

Unary Coding Controlled Simultaneous Wireless Information and Power Transfer

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design

COVIDanno, COVID-19 Annotation in Human

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim of this resource is to provide a reference resource of intensive functional annotations of differentially expressed genes (DEGs) among different time points of COVID-19 infection in human in vitro models. To do this, we performed differential expression analysis for 136 individual datasets across 13 tissue types. In total, we identified 4,935 DEGs. We performed multiple bioinformatics/computational biology studies for these DEGs. Furthermore, we developed a novel tool to help users predict the status of SARS-CoV-2 infection for a given sample. COVIDanno will be a valuable resource for identifying SARS-CoV-2-related genes and understanding their potential functional roles in different time points and multiple tissue types

Alarm reduction and root cause inference based on association mining in communication network

With the growing demand for data computation and communication, the size and complexity of communication networks have grown significantly. However, due to hardware and software problems, in a large-scale communication network (e.g., telecommunication network), the daily alarm events are massive, e.g., millions of alarms occur in a serious failure, which contains crucial information such as the time, content, and device of exceptions. With the expansion of the communication network, the number of components and their interactions become more complex, leading to numerous alarm events and complex alarm propagation. Moreover, these alarm events are redundant and consume much effort to resolve. To reduce alarms and pinpoint root causes from them, we propose a data-driven and unsupervised alarm analysis framework, which can effectively compress massive alarm events and improve the efficiency of root cause localization. In our framework, an offline learning procedure obtains results of association reduction based on a period of historical alarms. Then, an online analysis procedure matches and compresses real-time alarms and generates root cause groups. The evaluation is based on real communication network alarms from telecom operators, and the results show that our method can associate and reduce communication network alarms with an accuracy of more than 91%, reducing more than 62% of redundant alarms. In addition, we validate it on fault data coming from a microservices system, and it achieves an accuracy of 95% in root cause location. Compared with existing methods, the proposed method is more suitable for operation and maintenance analysis in communication networks