The near-infrared spectra and distribution of excited states of electrodeless discharge rubidium vapour lamps

The population ratio between the excited states of rubidium in the electrodeless discharge rubidium vapour lamp is calculated according to the near-infrared spectra in the region of 780-1550 nm. By using a 1529 nm laser, we measure the density of natural rubidium atoms at the 5P3/2 level. The populations of different excited states are then clarified.

Analysis of influence of RF power and buffer gas pressure on sensitivity of optically pumped cesium magnetometer

A further study is conducted on two factors which respectively influence the sensitivity of optically pumped cesium magnetometer （CsOPM）. The influence of radio frequency （RF） power and the buffer gas pressure on the sensitivity is theoretically analyzed, and some properties are predicted. Based on the established measurement system and the visible Zeeman spectrum, not only is the real influence of these factors studied, but also, under our experimental condition, optimum parameters based on the measured curves are ascertained. The properties of these measured curves match the theoretical result very well. Our research attempts to provide theory reference to help magnetometer designers determine optimum parameters under certain conditions.

Study on the investment value and investment opportunity of renewable energies under the carbon trading system

China has promised to start the national carbon trading system in 2017.In the carbon trading system,the renewable energy projects may obtain additional benefits through the Certified Carbon Emission Reduction(CCER) trade.As the carbon price fluctuates along with the market conditions,such fluctuation enables the renewable power projects to acquire the rights of an option,i.e.it may contain an even higher value due to the uncertainties in the future.While making an investment decision,the renewable power companies may choose to make the investment immediately,or postpone the investment and accumulate more information to increase the return of investment;and for immediate investments,the return must be sufficient to exceed the potential value of a waiting option.To study the investment in renewable power projects subject to the fluctuation of carbon price,this paper adopts the trinomial tree model of real options to estimate the net present value(NPV) and real option value(ROV) of three types of renewable power projects;according to the decision-making rules of real options to defer,all the three types of projects will exercise the option to postpone the investment decision.This thesis also calculates the benchmark prices of the three types of renewable projects at different times,in the two situations of having no government subsidy and having the government subsidy,so as to determine the investment opportunity of a project.The benchmark price decreases gradually along with the increase of government subsidy,indicating that the government subsidy will stimulate the investment in renewable projects.The benchmark price also increases gradually along with the lapse of time,indicating that the uncertainty will increase together with the time span and thus requires an even higher carbon price to determine the investment opportunity.This thesis also analyzes the sensitivity of factors affecting the investment in renewable projects and draws the conclusion that the fluctuation of carbon price is positively related with the benchmark price of renewable power projects,which indicates that the fluctuation of carbon price increases the option value of an investment but postpones the time of investment.As the China's carbon trading system improves gradually,the carbon price will reach a stable status,thus stimulate the power companies to invest in the renewable projects.

Analysis of Laser-Diode and Lamp Optical Pumping for a Rubidium Beam

The time evolution of populations of the ground state hyperfine Zeeman levels of ^(87)Rb atoms is analyzed using the Runge-Kutta method for the case that a rubidium beam is optically pumped,under different conditions of polarization,with a weak monochromatic laser and an ^(87)R lamp.On the base of the results above,the least estimated laser and ^(87)R lamp power for the best population inversion is obtained,aiding further investigations.

Design and Implementation of Online Experimental Platform for Computer Networks Course

Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks course based on Dynamips simulator.Instructors and students can access the platform by IE Browser to manage and take router experiments.On the basis of deployment and testing,the platform is effective and flexible.

A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

LanePost:Lane-Based Optimal Routing Protocol for Delay-Tolerant Maritime Networks

Ship.to.ship, ship.to.shore radio links empowered by Wi Fi, Wi MAX etc have been recently exploited to build maritime multi.hop mesh networks to provide internet services to on.ship users. However, because of the mobility of the vessels/ships and the large inter.ship distances, nodes in the maritime network are frequently disconnected, forcing data communication in the maritime mesh networks to be opportunistic and delay.tolerant. In this paper, we present Lane Post, an optimization approach for maritime delay.tolerant routing protocol. We exploit the shipping lane information to predict the rendezvous opportunities of the ships to optimize the route selection in delay.tolerant routing. In particular, we show that when the shipping lane information is available, an opportunistic routing graph(ORG) for each ship can be constructed to predict its multi.hop data routing opportunities to the other ships or to the shore. Based on the ORG, we develop an optimal route protocol(i.e., Lane Post) for each ship to minimize its delay of multi.hop packet delivery via dynamic programming. We discussed the ways of collecting shipping lane information by centralized method or distributed method.The proposed Lane Post protocol was evaluated by ONE, an open.source delay.tolerant network simulator, which shows its dramatic performance improvement in terms of delay reduction compared to the state.of.the.art opportunistic routing protocols.

Implementation of an 8-bit bit-slice AES S-box with rapid single flux quantum circuits

Rapid single flux quantum(RSFQ)circuits are a kind of superconducting digital circuits,having properties of a natural gate-level pipelining synchronous sequential circuit,which demonstrates high energy efficiency and high throughput advantage.We find that the high-throughput and high-speed performance of RSFQ circuits can take the advantage of a hardware implementation of the encryption algorithm,whereas these are rarely applied to this field.Among the available encryption algorithms,the advanced encryption standard(AES)algorithm is an advanced encryption standard algorithm.It is currently the most widely used symmetric cryptography algorithm.In this work,we aim to demonstrate the SubByte operation of an AES-128 algorithm using RSFQ circuits based on the SIMIT Nb0_(3) process.We design an AES S-box circuit in the RSFQ logic,and compare its operational frequency,power dissipation,and throughput with those of the CMOS-based circuit post-simulated in the same structure.The complete RSFQ S-box circuit costs a total of 42237 Josephson junctions with nearly 130 Gbps throughput under the maximum simulated frequency of 16.28 GHz.Our analysis shows that the frequency and throughput of the RSFQ-based S-box are about four times higher than those of the CMOS-based S-box.Further,we design and fabricate a few typical modules of the S-box.Subsequent measurements demonstrate the correct functioning of the modules in both low and high frequencies up to 28.8 GHz.

Towards a Source-Code Oriented Attestation

The Binary-based attestation （BA） mechanism presented by the Trusted Computing Group can equip the application with the capability of genuinely identifying configurations of remote system. However, BA only supports the attestation for specific patterns of binary codes defined by a trusted party, mostly the software vendor, for a particular version of a software. In this paper, we present a Source-Code Oriented Attestation （SCOA） framework to enable custom built application to be attested to in the TCG attestation architecture. In SCOA, security attributes are bond with the source codes of an application instead of its binaries codes. With a proof chain generated by a Trusted Building System to record the building procedure, the challengers can determine whether the binary interacted with is genuinely built from a particular set of source codes. Moreover, with the security attribute certificates assigned to the source codes, they can determine the trustworthiness of the binary. In this paper, we present a TBS implementation with virtualization.

Variant Map System to Simulate Complex Properties of DNA Interactions Using Binary Sequences

Stream cipher, DNA cryptography and DNA analysis are the most important R&D fields in both Cryptography and Bioinformatics. HC-256 is an emerged scheme as the new generation of stream ciphers for advanced network security. From a random sequencing viewpoint, both sequences of HC-256 and real DNA data may have intrinsic pseudo-random properties respectively. In a recent decade, many DNA sequencing projects are developed on cells, plants and animals over the world into huge DNA databases. Researchers notice that mammalian genomes encode thousands of large noncoding RNAs (lncRNAs), interact with chromatin regulatory complexes, and are thought to play a role in localizing these complexes to target loci across the genome. It is a challenge target using higher dimensional visualization tools to organize various complex interactive properties as visual maps. The Variant Map System (VMS) as an emerging scheme is systematically proposed in this paper to apply multiple maps that used four Meta symbols as same as DNA or RNA representations. System architecture of key components and core mechanism on the VMS are described. Key modules, equations and their I/O parameters are discussed. Applying the VM System, two sets of real DNA sequences from both sample human (noncoding DNA) and corn (coding DNA) genomes are collected in comparison with pseudo DNA sequences generated by HC-256 to show their intrinsic properties in higher levels of similar relationships among relevant DNA sequences on 2D maps. Sample 2D maps are listed and their characteristics are illustrated under controllable environment. Visual results are briefly analyzed to explore their intrinsic properties on selected genome sequences.

计算机系统导论课程改革探索(英文)

In the paper,we analyzed the teaching problems in the course of "Introduction to Computer Systems",proposed guidelines with teaching practice and implement bilingual teaching.Then we explored the reform ways in the course of "Introduction to Computer Systems",to enhance students' self-learning ability through the full use of networked multimedia tools;and also the improvement of the teaching level of teachers.

Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy

Structured illumination microscopy(SIM)has been widely used in live-cell superresolution(SR)imaging.However,conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios(SNRs).Deep-learning(DL)-based methods can address this challenge but may lead to degradation and hallucinations.By combining the physical inversion model with a total deep variation(TDV)regularization,we propose a hybrid restoration method(TDV-SIM)that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining resolutions.We demonstrate the performance superiority of TDV-SIM in restoring actin filaments,endoplasmic reticulum,and mitochondrial cristae from extremely low SNR raw images.Thus TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and photodamage.Overall,TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones,possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.

Sequential Bag-of-Words model for human action classification

Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.

An Integrated Framework of CAD Tool for an SOI-Based FPGA

For an SOI-FPGA （silicon-on-insulator field programmable gate arrays） （VS1000） fabricated with 0.5 ttm SOI-CMOS （silicon-on-insulator complementary-metal-oxide-semiconductor） process, a complete integrated platform of FPGA computer-aided design （CAD） toolset （VDK） is developed, which can convert the Verilog HDL （hardware description language） description into a bitstream and finally download it into an FPGA. Experiments and testing verify that this FPGA CAD works well and efficiently.

Symmetric Learning Data Augmentation Model for Underwater Target Noise Data Expansion

An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are today.However,the cost of obtaining abundant real-world environment is high,especially for underwater environments.It is more straightforward to simulate data that is closed to that from real environment.In this paper,a simple and easy symmetric learning data augmentation model(SLDAM)is proposed for underwater target radiate-noise data expansion and generation.The SLDAM,taking the optimal classifier of an initial dataset as the discriminator,makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation.It generates data similar to the initial dataset that can be used to supplement training data sets.This model has taken into consideration feature loss and sample loss function in model training,and is able to reduce the dependence of the generation and expansion on the feature set.We verified that the SLDAM is able to data expansion with low calculation complexity.Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy,for practical application in a production environment.

A Rough Set Based Optimization Method for Elderly Evaluation

In order to improve the efficiency of elderly evaluation, an optimization method based on rough set is proposed. Compared with the traditional rough set attribute reduction, the redundant evaluation items are eliminated by items’ correlation. It avoids a big overhead of calculating the core of rough sets that have many attributes. A novel rule reduction method is proposed based on reliability and coverage, in order to solve the problem of rarely appeared rules and conflict rules in traditional rough set. A sorting algorithm based on coverage is used to optimize the traditional flat evaluation questionnaire model with a hierarchical order. By these optimizations, the number of items that need to evaluate is greatly reduced. The proposed approach is deployed in an elderly service company named Lime family. Real-life result shows that the method can reduce more than 40% items with over 90% accuracy prediction rate. Compared with decision tree and the method based on expert knowledge in reduction rate and accuracy rate, the method has same performance in one index, and 20% improvement on average in the other one.

螺旋式方法在软件工程学习中的应用(英文)

The spiral learning method aims at improving study interest and setting up a learning target and changing passive learning to change into active learning.In software engineering study,the realization of the spiral learning method sets up a learning target according to some items or knowledge and divides a big target into a few of small targets in order to easily make them and helps students better learning in the process of the realization.

A model of techno-economic evaluation for eco-industrial park project

An eco-industrial park or estate is a community of manufactaring and service businesses located together on a common property. The goat of ElP is to create a win-win harmonious development aspect of ecooomic development and environmental protection. This paper emphasizes that the external .effect of an EIP is its main characteristic of technoeconomic evaluation for eco-industrial park project. From the view of the property, rights, the EIP＇s product is typicalty public-private. The government should take some inca.rares for the quantitative analysis on ecological positive externalities of the enterprises in EIP, and also should adopt Coase＇s Theorem, which supports that the market transaction is the best way to deal with positive externalities （external economics or diseconoraics）, or Pigou＇s Theorem, which holds that the government anti-positive externalities programs are the best way to cope with positive externalities, to internalize the EIP＇s external effects, which is also a fundamental tool to encourage investors to actively invest in EIP projects, Furthermore. this paper thinks that the EIP ＇s income should be equal to the income of staple products of the private property, and that of its by-products of the public property. According to this principle, this paper has put forward three major indicators, net present value （NPV）, internal rate of renan （IRR）, and investment repayment period （IRP）, which are also extensively used indicators in ardinary project techno-economic evaluation model to evaluate EIP technoeconomic effects. Theoretically, the indicatory not only can be used in EIP project evaluation, but also can provide a quantitative measure toot for the government to support EIP＇s construction to the maximum. In the end. a case is analyzed.