Session RF Photonics for CATV and HFC Systems (28-30 July) Reduction of 3 rd Order Intermod

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sensors and actuators b-chemical模板 -回复

sensors and actuators b-chemical模板-回复Sensors and Actuators: Introduction to Chemical Sensors and ActuatorsIntroduction:In the world of technology, sensors and actuators play a pivotal role in many applications. These devices are designed to monitor and control various processes, often providing important feedback to a control system. In this article, we will delve into the fascinating realm of chemical sensors and actuators, exploring their function, types, and applications. So let's begin our journey into the world of chemical sensors and actuators.Understanding Chemical Sensors:Chemical sensors are devices that are specifically designed to detect and measure the concentration of chemical species in a given environment. These sensors are widely used in industries such as healthcare, environmental monitoring, agriculture, and food processing. The primary goal of chemical sensors is to convert a chemical signal into an electrical signal, which can be easily measured and interpreted.Functioning of Chemical Sensors:Chemical sensors work on the principle of selectivity and sensitivity. Selectivity refers to the ability of a sensor to respond only to a specific target chemical, while sensitivity refers to the ability of a sensor to detect and measure small changes in the concentration of the target chemical. Chemical sensors typically consist of a sensing element, transducer, and signal processing system.Types of Chemical Sensors:There are various types of chemical sensors, each designed to detect specific types of chemicals. Some of the most commonly used chemical sensors include gas sensors, pH sensors, biosensors, and electrochemical sensors.1. Gas Sensors:Gas sensors are used to detect and measure the concentration of specific gases in the atmosphere. These sensors are widely used in industries such as oil and gas, automotive, and environmental monitoring. Gas sensors can detect gases such as carbon dioxide, carbon monoxide, methane, and ozone.2. pH Sensors:pH sensors are used to measure the acidity or alkalinity of a solution. These sensors are extensively used in industries such as pharmaceuticals, agriculture, and water treatment. pH sensors are based on the principle of ion-selective electrodes, which generate an electrical signal proportional to the hydrogen ion concentration in the solution.3. Biosensors:Biosensors are specifically designed to detect and measure the concentration of biological molecules such as proteins, enzymes, and antibodies. These sensors find applications in medical diagnostics, food safety, and environmental monitoring. Biosensors typically consist of a bioreceptor, transducer, and signal processing system.4. Electrochemical Sensors:Electrochemical sensors are widely used to measure the concentration of ions in a solution. These sensors are based on the principle of electrochemical reactions, where the analyte reacts with the electrode surface, generating an electrical signal. Electrochemical sensors are used in applications such as waterquality monitoring, chemical analysis, and medical diagnostics.Working of Chemical Actuators:Actuators, on the other hand, are devices that are used to control or manipulate physical systems based on the feedback received from sensors. Chemical actuators, specifically, are devices that convert chemical energy into mechanical motion. These devices are extensively used in robotics, industrial automation, and microfluidics.Applications of Chemical Sensors and Actuators:Chemical sensors and actuators find applications in a wide range of industries and fields. Some of the key applications include environmental monitoring, healthcare diagnostics, industrial process control, and food safety.In conclusion, chemical sensors and actuators play a crucial role in various industries by detecting and measuring the concentration of chemical species and controlling physical systems. These devices enable us to monitor and control processes, ensuring safety, efficiency, and accuracy. With advancements in technology,the field of chemical sensors and actuators is continuously evolving, opening up new avenues for research and innovation.。

生物质气化技术研究现状与发展

燃气气源与加工利用生物质气化技术研究现状与发展陈冠益, 高文学, 颜蓓蓓, 贾佳妮(天津大学环境科学与工程学院,天津300072)摘要: 综述了生物质气化技术的分类、气化炉特点、气化性能影响因素及评价指标。

介绍了生物质气化技术在国内外的发展现状,阐明了生物质气化技术需要解决的问题,提出了我国生物质气化技术的发展方向。

关键词: 生物质气化; 气化炉; 气化性能; 影响因素; 评价指标; 发展方向中图分类号:TU996 文献标识码:A 文章编号:1000-4416(2006)07-0020-07Present R esearch Status and D evelop m ent of B i o massG asificati on Technol ogiesCHEN Guan y,i GAO W en xue, YAN Be i be,i JI A Jia n i (S chool of Environm ent Science&Technology,T ianjin University,T ianjin300072,China)Abst ract: The c lassification of b i o m ass gasificati o n techno log ies,the characteristics o f gasifiers, the infl u encing factors of gasification perfo r m ance and the evaluati n g i n d icator are rev ie w ed.The presen t deve l o p m ent status o f bio m ass gasificati o n technolog ies at ho m e and abroad is i n tr oduced,the proble m s that should be so l v ed for bio m ass gasification technolog ies are descri b ed,and the deve l o pm ent d irecti o n o f b io m ass gasification technolog ies in Ch i n a is put for w ard.K ey w ords: bio m ass gasification; gasifier; gasificati o n perfor m ance; i n fluenc i n g factor; e va l u ating i n d icator; developm ent d irecti o n1 生物质气化原理与工艺1.1 生物质气化原理生物质气化是指生物质原料(薪柴、锯末、麦秸、稻草等)压制成型或经简单的破碎加工处理后,在欠氧条件下,送入气化炉中进行气化裂解,得到可燃气体并进行净化处理而获得产品气的过程。

中科院自动化所的中英文新闻语料库

中科院自动化所的中英文新闻语料库中科院自动化所（Institute of Automation, Chinese Academy of Sciences）是中国科学院下属的一家研究机构，致力于开展自动化科学及其应用的研究。

该所的研究涵盖了从理论基础到技术创新的广泛领域，包括人工智能、机器人技术、自动控制、模式识别等。

下面将分别从中文和英文角度介绍该所的相关新闻语料。

[中文新闻语料]1. 中国科学院自动化所在人脸识别领域取得重大突破中国科学院自动化所的研究团队在人脸识别技术方面取得了重大突破。

通过深度学习算法和大规模数据集的训练，该研究团队成功地提高了人脸识别的准确性和稳定性，使其在安防、金融等领域得到广泛应用。

2. 中科院自动化所发布最新研究成果：基于机器学习的智能交通系统中科院自动化所发布了一项基于机器学习的智能交通系统研究成果。

通过对交通数据的收集和分析，研究团队开发了智能交通控制算法，能够优化交通流量，减少交通拥堵和时间浪费，提高交通效率。

3. 中国科学院自动化所举办国际学术研讨会中国科学院自动化所举办了一场国际学术研讨会，邀请了来自不同国家的自动化领域专家参加。

研讨会涵盖了人工智能、机器人技术、自动化控制等多个研究方向，旨在促进国际间的学术交流和合作。

4. 中科院自动化所签署合作协议，推动机器人技术的产业化发展中科院自动化所与一家著名机器人企业签署了合作协议，共同推动机器人技术的产业化发展。

合作内容包括技术研发、人才培养、市场推广等方面，旨在加强学界与工业界的合作，加速机器人技术的应用和推广。

5. 中国科学院自动化所获得国家科技进步一等奖中国科学院自动化所凭借在人工智能领域的重要研究成果荣获国家科技进步一等奖。

该研究成果在自动驾驶、物联网等领域具有重要应用价值，并对相关行业的创新和发展起到了积极推动作用。

[英文新闻语料]1. Institute of Automation, Chinese Academy of Sciences achievesa major breakthrough in face recognitionThe research team at the Institute of Automation, Chinese Academy of Sciences has made a major breakthrough in face recognition technology. Through training with deep learning algorithms and large-scale datasets, the research team has successfully improved the accuracy and stability of face recognition, which has been widely applied in areas such as security and finance.2. Institute of Automation, Chinese Academy of Sciences releases latest research on machine learning-based intelligent transportationsystemThe Institute of Automation, Chinese Academy of Sciences has released a research paper on a machine learning-based intelligent transportation system. By collecting and analyzing traffic data, the research team has developed intelligent traffic control algorithms that optimize traffic flow, reduce congestion, and minimize time wastage, thereby enhancing overall traffic efficiency.3. Institute of Automation, Chinese Academy of Sciences hosts international academic symposiumThe Institute of Automation, Chinese Academy of Sciences recently held an international academic symposium, inviting automation experts from different countries to participate. The symposium covered various research areas, including artificial intelligence, robotics, and automatic control, aiming to facilitate academic exchanges and collaborations on an international level.4. Institute of Automation, Chinese Academy of Sciences signs cooperation agreement to promote the industrialization of robotics technologyThe Institute of Automation, Chinese Academy of Sciences has signed a cooperation agreement with a renowned robotics company to jointly promote the industrialization of robotics technology. The cooperation includes areas such as technology research and development, talent cultivation, and market promotion, aiming to strengthen the collaboration between academia and industry and accelerate the application and popularization of robotics technology.5. Institute of Automation, Chinese Academy of Sciences receivesNational Science and Technology Progress Award (First Class) The Institute of Automation, Chinese Academy of Sciences has been awarded the National Science and Technology Progress Award (First Class) for its important research achievements in the field of artificial intelligence. The research outcomes have significant application value in areas such as autonomous driving and the Internet of Things, playing a proactive role in promoting innovation and development in related industries.。

氯化石蜡的环境分析方法研究进展

引用格式：周婷婷, 杨倩玲, 翁冀远, 等. 氯化石蜡的环境分析方法研究进展[J]. 中国测试，2024, 50(4): 1-15. ZHOU Tingting,YANG Qianling, WENG Jiyuan, et al. A review on analysis of chlorinated paraffins in environment[J]. China Measurement & Test,2024, 50(4): 1-15. DOI: 10.11857/j.issn.1674-5124.2022080162氯化石蜡的环境分析方法研究进展周婷婷1,2,3，杨倩玲1,2,3，翁冀远1,3，乔林1，高丽荣1,2,3，郑明辉1,2,3(1. 中国科学院生态环境研究中心，环境化学与生态毒理学国家重点实验室，北京 100085;2. 国科大杭州高等研究院环境学院，浙江杭州 310000; 3. 中国科学院大学，北京 100049)摘　要: 短链氯化石蜡（SCCPs ）具有持久性、生物毒性和生物累积性等，是一种持久性有机污染物，被列入《斯德哥尔摩公约》附件A 中，中链氯化石蜡具有SCCPs 相似的性质也备受关注。

氯化石蜡（chlorinated paraffins ，CPs ）拥有成千上万种同系物、异构体、对映体，加之环境基质中存在其他有机卤素化合物，CPs 分离分析困难，目前我国仍没有环境样品中CPs 分析的标准方法。

该文对近年来不同环境基质中的CPs 分析所采用的样品前处理技术和仪器分析方法两个方面进行综述，提供CPs 分析方法最新发展动态，为相关人员对开展此方面的研究提供参考。

关键词: 短链氯化石蜡; 中链氯化石蜡; 分析方法; 前处理方法中图分类号: TB9; X-1文献标志码: A文章编号: 1674–5124(2024)04–0001–15A review on analysis of chlorinated paraffins in environmentZHOU Tingting 1,2,3, YANG Qianling 1,2,3, WENG Jiyuan 1,3, QIAO Lin 1, GAO Lirong 1,2,3, ZHENG Minghui 1,2,3(1. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; 2. School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China; 3. University of ChineseAcademy of Sciences, Beijing 100049, China)Abstract : Short-chain chlorinated paraffins (SCCPs) are persistent organic pollutants with persistence, toxicity,bioaccumulation, and were listed in Annex A of the Stockholm Convention. Medium-chain chlorinated paraffins (MCCPs) have also gained attention due to its similar properties to SCCPs. Chlorinated paraffins (CPs) have thousands of congeners, isomers and enantiomers and the presence of other organohalogen compounds in the environmental matrices makes the separation and analyze of CPs difficult. At present, there is still no standard analytical method to analysis CPs in the different environmental matrices. This article will review the sample preparation and instrumental analysis of CPs in different environmental matrices in recent years and provide an update on the latest developments in the analytical methods for CPs.Keywords : short-chain chlorinated paraffins; medium-chain chlorinated paraffins; analytical methods;preparation methods收稿日期: 2022-08-27；收到修改稿日期: 2022-10-04基金项目: 国家环境保护环境监测质量控制重点实验室开放基金（KF202203）作者简介: 周婷婷（1997-），女，福建漳州市人，硕士研究生，专业方向为新污染物的分析方法与环境行为的研究。

IPCC政府间气候变化委员会第2次评估报告

UNEP PNUEWMO OMM INTERGOVERNMENTAL PANEL ON CLIMATE CHANGEIPCC Second AssessmentClimate Change 1995A REPORT OF THEINTERGOVERNMENTAL PANEL ON CLIMATE CHANGEUNEPPNUEWMO OMM INTERGOVERNMENTAL PANEL ON CLIMATE CHANGEIPCC Second AssessmentClimate Change 1995A REPORT OF THEINTERGOVERNMENTAL PANEL ON CLIMATE CHANGEiiiivG.O.P. ObasiSecretary-GeneralWorld Meterological Organization Ms E. DowdeswellExecutive DirectorUnited Nations Environment Programmeviii N. SundararamanSecretary of the IPCC B. Bolin Chairman of the IPCC34563.1This section provides scientific and technical informationthat can be used, inter alia , in evaluating whether the projectedrange of plausible impacts constitutes “dangerous anthropogenicinterference with the climate system”, as referred to in Article 2, andin evaluating adaptation options. However, it is not yet possible tolink particular impacts with specific atmospheric concentrations ofgreenhouse gases.3.2Human health, terrestrial and aquatic ecological systems,and socio-economic systems (e.g., agriculture, forestry, fisheries andwater resources) are all vital to human development and well-beingand are all sensitive to both the magnitude and the rate of climatechange. Whereas many regions are likely to experience the adverseeffects of climate change — some of which are potentiallyirreversible — some effects of climate change are likely to bebeneficial. Hence, different segments of society can expect toconfront a variety of changes and the need to adapt to them.3.3Human-induced climate change represents an importantadditional stress, particularly to the many ecological and socio-economic systems already affected by pollution, increasing resourcedemands, and non-sustainable management practices. Thevulnerability of human health and socio-economic systems — and,to a lesser extent, ecological systems — depends upon economiccircumstances and institutional infrastructure. This implies thatsystems typically are more vulnerable in developing countries whereeconomic and institutional circumstances are less favourable.3.4Although our knowledge has increased significantlyduring the last decade and qualitative estimates can be developed,quantitative projections of the impacts of climate change on anyparticular system at any particular location are difficult becauseregional-scale climate change projections are uncertain; our currentunderstanding of many critical processes is limited; systems aresubject to multiple climatic and non-climatic stresses, the interac-tions of which are not always linear or additive; and very few studies have considered dynamic responses to steadily increasing concen-trations of greenhouse gases or the consequences of increases beyond a doubling of equivalent atmospheric CO 2concentrations.3.5Unambiguous detection of climate-induced changes in most ecological and social systems will prove extremely difficult in the coming decades. This is because of the complexity of these systems, their many non-linear feedbacks, and their sensitivity to a large number of climatic and non-climatic factors, all of which are expected to continue to change simultaneously. As future climate extends beyond the boundaries of empirical knowledge (i.e., the documented impacts of climate variation in the past), it becomes more likely that actual outcomes will include surprises and unanticipated rapid changes.Sensitivity of systems Terrestrial and aquatic ecosystems 3.6Ecosystems contain the Earth’s entire reservoir of genetic and species diversity and provide many goods and services including:(i) providing food, fibre, medicines and energy; (ii) processing and storing carbon and other nutrients; (iii) assimilating wastes, purifying water, regulating water runoff, and controlling floods, soil degrada-tion and beach erosion; and (iv) providing opportunities for recreation and tourism. The composition and geographic distribu-tion of many ecosystems (e.g., forests, rangelands, deserts, mountain systems, lakes, wetlands and oceans) will shift as individual species respond to changes in climate; there will likely be reductions in biological diversity and in the goods and services that ecosystems provide society. Some ecological systems may not reach a new equi-librium for several centuries after the climate achieves a new balance.78910Figure 1 (a). Carbon dioxide concentration profiles leading to stabilization at 450, 550, 650 and 750 ppmv following the pathways defined in IPCC (1994) (solid curves) and for pathways that allow emissions to follow IS92a until at least the year 2000 (dashed curves). A single profile that stabilizes at a carbon dioxide concen-tration of 1000 ppmv and follows IS92a emissions until at least the year 2000 has also been defined. Stabilization at concentrations of 450, 650 and 1000 ppmv would lead to equilibrium temperature increases relative to 199014due to carbon dioxide alone (i.e., not including effects of other greenhouse gases (GHGs) and aerosols) of about 1°C (range: 0.5 to 1.5°C), 2°C (range: 1.5 to 4°C) and 3.5°C (range: 2 to 7°C), respectively. A doubling of the pre-industrial carbon dioxide concentration of 280 ppmv would lead to a concen-tration of 560 ppmv and doubling of the current concentration of 358 ppmv would lead to a concentration of about 720 ppmv.Figure 1 (b). Carbon dioxide emissions leading to stabilization at concentrations of 450, 550, 650, 750 and 1000 ppmv following the profiles shown in (a) from a mid-range carbon cycle model. Results from other models could differ from those presented here by up to approximately ±15%. For comparison, the carbon dioxide emis-sions for IS92a and current emissions (fine solid line) are alsoshown.11121314157.1Economic development, social development and environ-mental protection are interdependent and mutually reinforcingcomponents of sustainable development, which is the frameworkfor our efforts to achieve a higher quality of life for all people. TheUNFCCC notes that responses to climate change should be coordi-nated with social and economic development in an integratedmanner with a view to avoiding adverse impacts on the latter,taking into full account the legitimate priority needs of developingcountries for the achievement of sustainable development and theeradication of poverty. The Convention also notes the common butdifferentiated responsibilities and respective capabilities of allParties to protect the climate system. This section reviews brieflywhat is known about the costs and benefits of mitigation and adap-tation measures as they relate, inter alia , to the sustainability ofeconomic development and environment.Social costs of climate change7.2Net climate change damages include both market and non-market impacts as far as they can be quantified at present and, in somecases, adaptation costs. Damages are expressed in net terms to accountfor the fact that there are some beneficial impacts of climate changeas well, which are, however, dominated by the damage costs. Non-market impacts, such as human health, risk of human mortality anddamage to ecosystems, form an important component of available esti-mates of the social costs of climate change. The estimates ofnon-market damages, however, are highly speculative and notcomprehensive and are thus a source of major uncertainty in assess-ing the implications of global climate change for human welfare.7.3The assessed literature quantifying total damages from 2 to 3°C warming provides a wide range of point estimates for damages given the presumed change in atmospheric greenhouse gas concen-trations. The aggregate estimates tend to be a few per cent of world GDP, with, in general, considerably higher estimates of damage to developing countries as a share of their GDP. The aggregate estimates are subject to considerable uncertainty, but the range of uncertainty cannot be gauged from the literature. The range of estimates cannot be interpreted as a confidence interval given the widely differing assumptions and methodologies in the studies. Aggregation is likely to mask even greater uncertainties about damage components.Regional or sectoral approaches to estimating the consequences of climate change include a much wider range of estimates of the net economic effects. For some areas, damages are estimated to be signif-icantly greater and could negatively affect economic development.For others, climate change is estimated to increase economic produc-tion and present opportunities for economic development.Equalizing the value of a statistical life at the level typical of that in developed countries would increase monetized damages several times, and would further increase the share of the developing coun-tries in the total damage estimate. Small islands and low-lying coastal areas are particularly vulnerable. Damages from possible large-scale catastrophes, such as major changes in ocean circulation, are not reflected in these estimates. Benefits of limiting climate change 7.4The benefits of limiting greenhouse gas emissions and enhancing sinks are: (a ) the climate change damages and167.12Cost estimates for a number of specific approaches to miti-gating emissions or enhancing sinks of greenhouse gases vary widely and depend on site-specific characteristics. This is true for renewable energy technologies, for example, as well as carbon sequestration options. The latter could offset as much as 15-30% of 1990 global energy-related emissions each year in forests for the next 50 years. The costs of carbon sequestration, which are compet-itive with source control options, differ among regions of the world.7.13Control of emissions of other greenhouse gases, especially methane and nitrous oxide, can provide significant cost-effective oppor-tunities in some countries. About 10% of anthropogenic methane emissions could be reduced at negative or low cost using available miti-gation options for such methane sources as natural gas systems, waste management and agriculture. Costs differ between countries and regions for some of these options.Subsidies, market imperfections and barriers7.14The world economy and indeed some individual national economies suffer from a number of price distortions which increase greenhouse gas emissions, such as some agricultural and fuel sub-sidies and distortions in transport pricing. A number of studies of this issue indicate that global emissions reductions of 4-18 % together with increases in real incomes are possible from phasing out fuel subsidies.7.15Progress has been made in a number of countries in cost-effectively reducing imperfections and institutional barriers in markets through policy instruments based on voluntary agreements, energy efficiency incentives, product efficiency standards and energy efficiency procurement programmes involving manufacturers and utility regulatory reforms. Where empirical evaluations have been made, many have found that the benefit-cost ratio of increasing energy efficiency was favourable, suggesting the practical feasibility of realizing “no regrets” potentials at negative net cost.Value of better information and research7.16The value of better information about the processes, impacts of and responses to climate change is likely to be great. Analysis of economic and social issues related to climate change, especially in developing countries, is a high priority for research. Further analysis is required concerning effects of response options on employment, inflation, trade, competitiveness and other public issues.1718Tundra, Taiga-Tundra, Ice BorealForestsTemperateForestsTropicalForestsSavannas,Dry Forests,WoodlandsGrasslands,Shrublands,Deserts313233regional scales, even at mid-latitudes. There may be increased risk of hunger and famine in some locations; many of the world’s poorest people — particularly those living in subtropical and tropical areas and dependent on isolated agricultural systems in semi-arid and arid regions — are most at risk of increased hunger. Many of these at-risk populations are found in sub-Saharan Africa; south, east andsoutheast Asia; and tropical areas of Latin America, as well as some Pacific island nations.Adaptation — such as changes in crops and crop varieties, improved water-management and irrigation systems, and changes in planting schedules and tillage practices — will be important in limiting34353637383940。

IPCC_Special_Report_on_Carbon_Dioxide_Capture_and_Storage

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nature photonics 模板 -回复

nature photonics 模板-回复Nature Photonics: A Template for Advancements in Photonic TechnologiesIntroduction:Photonics, a field that deals with the generation, manipulation, and detection of light, has emerged as a revolutionary technology impacting various sectors such as telecommunications, energy, healthcare, and information processing. Nature Photonics, a prestigious interdisciplinary scientific journal, has been at the forefront of showcasing breakthrough research and cutting-edge technologies in this field. This article aims to explore the contributions of Nature Photonics and discuss how it has shaped the advancements in photonic technologies.Section 1: Establishing a Premier Platform for Photonics Research Nature Photonics, since its inception in 2007, has played a critical role in providing a platform for researchers to publish theirhigh-impact studies. The journal follows a rigorous peer-review process, ensuring that only significant and groundbreaking work gets published. This approach has fostered a culture of excellence and has attracted researchers worldwide to submit their studies toNature Photonics.Section 2: Key Discoveries and InnovationsOver the years, Nature Photonics has featured numerous studies that have pushed the boundaries of photonic technologies. From ultrafast lasers to quantum optics to optical materials, the journal has showcased a diverse range of topics. One significant breakthrough that deserves attention is the development of photonic integrated circuits (PICs). These circuits, which combine multiple photonic components on a single chip, have revolutionized optical communications, enabling high-speed data transfer and increased bandwidth.Another groundbreaking area that Nature Photonics has extensively covered is biophotonics. Researchers have explored the use of light for medical diagnostics, imaging, and therapy. For instance, novel imaging techniques such as multiphoton microscopy and optical coherence tomography have allowed for high-resolution imaging of biological tissues, leading to advancements in disease diagnosis and monitoring. Additionally, photonic-based therapies, like photodynamic therapy and optogenetics, have shown promising results in cancer treatmentand neurobiology, respectively.Section 3: Bridging the Gap between Academia and Industry Nature Photonics recognizes the importance of translating research findings into practical applications. The journal has actively encouraged authors to highlight the potential commercial implications of their work. This emphasis on industry relevance has fostered collaborations between academia and industry, facilitating the development of photonic technologies for real-world applications. Several startups and spin-offs have emerged based on research features in Nature Photonics, further validating the impact of the journal.Section 4: Perspective and Future DirectionsNature Photonics has been instrumental in propelling the field of photonics to new heights. However, challenges remain, and future research directions need to address them. One area that warrants attention is the development of compact and efficient light sources for integrated photonics. Compact, low-power lasers are crucial for applications such as on-chip optical interconnects and quantum computing. Additionally, innovative and cost-effective manufacturing techniques for PICs can enhance their scalabilityand decrease production costs.Furthermore, efforts should continue to bridge the gap between fundamental research and industry applications.Industry-academia collaborations need to be fostered, and policies that incentivize commercialization of photonic technologies should be implemented. Finally, increased emphasis on sustainability should be incorporated into photonic research, promoting the development of energy-efficient photonic devices and materials.Conclusion:Nature Photonics has undeniably played a pivotal role in advancing the field of photonics. Through its commitment to excellence, the journal has provided a platform for groundbreaking research, fostering collaborations between academia and industry. Looking ahead, Nature Photonics will continue to shape the trajectory of photonic technologies, pushing boundaries, and enabling novel applications for the betterment of society.。

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sc

Julia:A Generic Static Analyser for the Java BytecodeFausto SpotoDipartimento di Informatica,Verona,Italyfausto.spoto@univr.itAbstract.We describe our software tool Julia for the static analysisof full Java bytecode,for optimisation as well as veriﬁcation.This toolis generic since abstract domains(analyses)are not part of Julia butrather external classes that specialise its behaviour.Static analysis isperformed through a denotational or constraint-basedﬁxpoint calcula-tion,focused on some program points called watchpoints.These pointsspecify where the result of the analysis is useful,and can be automati-cally placed by the abstract domain or manually provided by the user.Julia can be instructed to include a given set of library Java classes inthe analysis,in order to improve its precision.Moreover,it gives abstractdomains the opportunity to approximate control and data-ﬂow arisingfrom exceptions and subroutines.1IntroductionThis paper describes the Julia software tool that we have developed in order to apply the abstract interpretation technique[10]to the static analysis of Java bytecode[16].The motivation underlying our eﬀort is to provide a software support for optimising,verifying and reasoning upon Java bytecode applications before they are run,and when their source code is not available or does not even exist.Forseeing the behaviour of programs,before their actual execution, becomes more and more relevant as such programs increase in complexity and get used in critical situations such as medical operations,ﬂight control or banking cards.Being able to prove,in an automatic way,that programs do adhere to their functional speciﬁcations is a basic factor to their success.This is particularly true for applications written in Java bytecode,distributed on the Internet or used inside a smart card,and hence potentially harmful to the client.In this perspective,analyses for security are attracting more and more interest[20].But the information inferred by a static analysis can also be used for optimisation, documentation and debugging.Abstract interpretation[10]has served as a primary framework for the formal derivation of static analyses from the property of interest.It features the ability to express correctness as well as optimality of a static analysis.It consists in executing the program over a description(the abstract domain)of the actual run-time data.By saturating all possible program execution paths,we get adomain description743841775101610302659539Fig.1.The abstract domains currently implemented inside Julia.Their size is given in number of Java source code lines,comments included.provably correct picture of its run-time behaviour,which is more or less precise, depending on how much the chosen description approximates the actual data.The goal of Julia was to fulﬁll the following criteria:–the analyser is generic i.e.,it does not embed any speciﬁc abstract domain but allows instead the addition of new abstract domains as external classes;–the analyser allows one to specify the set of classes which must be analysed, called the application classes.They must not change from analysis-time to run-time(through dynamic loading);this would otherwise break the correct-ness of the analysis;–the abstract domain developer has his work simpliﬁed as much as possible.Namely,he must be able to apply the formal framework of abstract interpre-tation to deﬁne its abstract domain,even for the most complex bytecodes and in the presence of all the intricacies of the Java bytecode.All he needs to do is to provide implementations of the abstract operations corresponding to the concrete bytecodes,together with a bottom element and a least upper bound operator;–the analysis is localised i.e.,its cost is proportional to the number of program points where the abstract information must be computed(the watchpoints);–the analyser does not impose any constraint on the precision of the abstract ly,it allows a given abstract domain to exploit theﬂow of control due to exceptions and subroutines to get a more precise analysis,yet allowing another domain to disregard the sameﬂows and get a less precise analysis.Precision remains a domain-related issue[10];–the analyser uses eﬃcient techniques for computing theﬁxpoint needed for the static analysis[10].These techniques are domain-independent,so that the abstract domain developer does not need to care about how theﬁxpoint is computed for its abstract domain.Julia is free software[23].It currently includes seven abstract domains, which are described in Figure1.Class analysis is used to transform some virtual calls into static calls,whenever their target is statically determined[25].Escape analysis determines which creation instructions can safely allocate objects in the activation stack instead of the heap,since those objects will never outlive themethod which creates them[7,15].Information-ﬂow analysis approximates the ﬂow of data in a program,permitting one to spot violations of non-interference conditions in the analysis of security[20].Static initialisation analysis determines the set of classes which are deﬁnitely initialised in a given program point,so that references to such classes do not induce a call to their static initialiser.We discuss it in Section9as a simple example of abstract domain.The paper is organised as follows.Section2describes related work.Sections3 to8show how each of the previous criteria have been attained with Julia. Section9shows an example of abstract domain which can be plugged inside Julia.Section10discusses the application of Julia to multi-threaded programs. Section11presents the cost in time for the analysis of some non-trivial Java bytecode applications.Section12concludes.2Related WorkBecause of the actual complexity of the Java bytecode,static analysers for full Java bytecode have not been developed intensively yet.A decompilation tool,such as Soot[26],is often used as the front-end of a static analyser for Java bytecode.Currently,class analyses similar to rapid type analysis are implemented inside Soot.Decompilation is problematic when the bytecode is not the result of the compilation of Java,and maybe contains some exotic features of the Java bytecode that have no direct counterpart in Java, such as overlapping or recursive exception handlers(i.e.,catching exceptions thrown by themselves),or recursive Java bytecode subroutines(which cannot be decompiled into finally clauses).The Indus tool[1]is an analyser based on Soot.It currently includes someﬂavour of class analysis,escape analysis and analyses targetted for concurrent programs,to be coupled with a model-checker.In[17],a set of tools and components for building language runtimes is shown.The bytecode they consider includes the Java bytecode as a special case. Such tools have been used to implement some static analyses as well.Their code preprocessing is much lighter than ours and consequently much faster.A generic analyser for a subset of Java(rather than Java bytecode)is de-scribed in[19].It has only be applied to small programs.It allows completely relational,ﬂow and context-sensitive static analyses.In this sense it is quite close to our work.Variousﬂavours of rapid class analyses have been implemented in[25].The tool is not available and genericity is not mentioned.Some benchmarks are similar to ours and their analysis seems faster than ours.Escape analysis has been implemented through specialised analysers for Java source code only,rather than Java bytecode.For instance,the analysis in[7] works inside a commercial Java compiler.The construction is speciﬁc to escape analysis,and it cannot be immediately applied to other analyses.A more complex escape analysis has been implemented in[9]and it seems to perform like ours. However,it is not a generic nor localised tool.A generic analyser for the Java Card bytecode has been deﬁned in [8].The approach is fascinating,since it is based on the automatic derivation of a correct static analyser from its same proof of correctness.However,the Java Card byte-code is simpler than the Java bytecode.Moreover,exceptions are not considered.No examples of analysis are shown.Hence,actual analysis times are unknown.jDF A [2]performs constant propagation and liveness analysis for variables,but none of the analyses we show in Figure 9.JNUKE [4]performs dynamic rather than static analysis.No sensible comparison is hence possible with Julia 3A Generic AnalyserBeing generic is a useful feature of a modern static analyser.Current program-ming languages,such as the Java bytecode,are so complex that the development of a new static analysis is hard and error-prone.However,diﬀerent static analyses do share a lot.The preprocessing phase (Section 5)and the ﬁxpoint computa-tion (Section 8)are the same for every abstract domain.And they represent by themselves most of the development eﬀort of a static analysis.It is hence convenient to develop and debug them once and for all,and to see new abstract domains as plug-in’s which are added to the static analyser in order to specialise its behaviour.Genericity requires however to provide an interface between the code prepro-cessor and the analyser.We solved this problem by specifying all bytecodes as state transformers.For each state transformer the abstract domain provides an approximation (see for instance Section9).bytecode results Julia Fig.2.The structure of Julia .To fulﬁll this requirement,westructured Julia as in Figure 2.Acode preprocessor,called Romeo ,feeds the preprocessed code into a generic ﬁxpoint engine calledJuliet .The latter uses an exter-nal module,the abstract domain,to abstract every single bytecode,but uses its own ﬁxpoint strategies,independent from the abstract do-main.The Bcel library [12]is alow-level interface to .class ﬁles.Figure 1shows that genericityleads to small abstract domain implementations,and hence faster and simpler development.4Application ClassesThe Java Virtual Machine loads classes dynamically as they are needed during the execution of a program.Hence,we have no guarantee that the classes that will be loaded at run-time will correspond to those that were present in the systemduring the static analysis.We might think to analyse a class without assuming anything about its surrounding environment.Any reference to an external class is treated through a worst-case assumption[11]claiming that nothing is known about its outcome.This is deﬁnitely correct,but often useless in an object-oriented language,where classes are tightly coupled through virtual method invocations,ﬁeld accesses and constructor chaining.This approach results in static analyses of very little precision.Instead,we follow here the solution to this problem used in the decompilation tool Soot[26],which allows one to make explicit assumptions about which classes(called application classes)are not allowed to change from analysis-time to run-time.As a consequence,we can inspect them during the analysis and gather abstract information which improves the precision of the analysis.Application classes are typically those of the application we are analysing. Libraries are not considered application classes,usually.Hence,any reference to a library class is resolved through the worst-case assumption.However,stronger hypotheses than the worst-case assumption can be made.For instance,in[25], the set of application classes is assumed to be closed wrt.subclassing.This improves the precision of the analysis.We assume that every abstract domain plugged inside Julia decides how to deal with references to non-application classes.It can use a worst-case assump-tion or other,stronger hypotheses.This must be clearly stated in its deﬁnition, so that the user of Julia can judge whether such hypotheses are realistic or not for his own analyses.For instance,our abstract domain rt for rapid type analysis assumes that application classes are downward closed,as in[25],while our domain er for escape analysis assumes that non-application classes have the same method andﬁeld signatures as in the system used for the analysis;their implementation can however change.5Bytecode Simpliﬁcation(Preprocessing)The application of abstract interpretation to a complex language such as the Java bytecode is a real challenge.This is because abstract interpretation allows us to derive a static analysis from a speciﬁcation of the concrete semantics of a program given as an(operational or denotational)input/ouput map.But some Java bytecodes cannot be immediately seen as input/output maps.Examples are the control-related bytecodes such as goto or lookupswitch.Other bytecodes are input/ouput maps,but they are so complex that the application of abstract interpretation is very hard and error-prone.Examples are the four invoke byte-codes.Moreover,exceptions break the input/output behaviour of a bytecode, since for some input state there is no output state,but rather an exceptional state.We want to spare as much as possible the abstract domain developer from knowing the intricacies of the bytecode,and allow him to deﬁne correct(and potentially optimal)operations on the abstract domain corresponding to the concrete bytecodes.To this goal,we apply a light preprocessing to the Java bytecode,in the sense that most of the bytecodes,those which are already input/output maps,are not transformed.The result is a graph of basic blocks[3]of a simpliﬁed Java byte-code,which we call Juliet bytecode.Edges between basic blocks model control. Conditional jumps use newﬁlter bytecodes,which play exactly the same role as the assume statements used in[6].Theseﬁlter bytecodes can be used to improve the precision of a static analysis,as[6]shows.An example is in Section9.They can also be conservatively abstracted as no-ops.Figure3shows a bytecode and its translation into a graph of basic blocks,where the goon newﬁlter bytecodes select the execution path on the basis of the outcome of the ifnewκbytecode be-haves like the old newκbytecode,but it does not check for initialisation.All bytecodes in Figure4are now in-put/output maps.This compilation ofthe original new bytecode simpliﬁes thesubsequent static analysis.An example is in Section9.In a similar way,an invoke instruction is compiled into a Juliet code which explicitly resolves the class,then resolves the method,then looks for the target method of the call (through a compiled lookup procedure),then creates the activation frame for the method,then calls the selected method andﬁnally moves the return value of the called method into the operand stack of the caller.The domain devel-oper does not need to know how a method is resolved and looked up by the Java Virtual Machine[16].He does not need to know about visibility modiﬁers, nor about the exceptions which might be thrown during the method call.Ev-erything has been compiled,he just has to abstract the resulting code.Also exception handlers are compiled into the code.xκyFig.4.The compilation of a new bytecode.Since Juliet bytecode is derived by splitting complex Java bytecodes,it is moreﬁne-grained than Java bytecode.Hence all properties of the Java bytecode can be expressed as properties of Juliet bytecode.In particular,we claim that the resulting Juliet bytecode has the same concrete semantics as the original Java bytecode.We are conﬁdent in this result since most of the Java bytecodes are not changed during the translation.The most complex bytecodes are trans-lated by following their operational semantics in the Java Virtual Machine oﬃcial documentation[16].Since a graph of basic blocks of bytecode is used,we canﬁt all the complex features of the Java bytecode into that formalism(see Section2).Namely,edges connecting the blocks of code let us represent exception handlers of any shape and recursive subroutines.6LocalisationThe information computed by a static analysis is typically useful in some special program points only,called watchpoints.The number and position of the watch-points depends on the way the abstract information is used to reason about the program.For instance,in the case of class analysis we want to know which virtual calls are actually determined i.e.,always lead to the same target method[25]. Hence a watchpoint must be put before the virtual calls of the program,so that we can use the abstract information collected there to spot determinism.In the case of escape analysis,we bracket,between an entry and an exit watchpoint, the methods containing a new bytecode.This allows us to spot the new byte-codes creating objects that never escape their creating method.Those objects can hence be allocated in the activation stack instead of the heap[7,15].Since,in general,watchpoints are internal program points,the denotation computed by a static analyser cannot be just an input/ouput map.A richer structure is needed.Moreover,it is desirable that the cost of the analysis scale with the number of watchpoints,in which case we say that the static analysis is focused or localised.This is important because it allows us to concentrate the typically little computational resources of time and memory on the watchpoints only,instead of the whole program.Hence larger programs can be analysed.A general framework for focused static analyses was developed in[22]for a simple high-level language.In[21]we show how it can be applied to the Java bytecode,by exploiting the same simpliﬁcation of the bytecode highlighted in Section5.We have then implemented this localised analysis inside Juliet, theﬁxpoint engine of Julia.Our experiments conﬁrm that the resulting static analyses outperform their unfocused versions[21].A positive property of our focused analyses is that the abstract domain de-veloper is not aware of how the focusing technique works[22,21].He develops his abstract domain as for a simple input/ouput analysis.Abstract domains for Julia put watchpoints automatically,since they are aware of the goal of the analysis they implement.We report an example in Section9.But the user can put watchpoints explicitly if he wants.Julia runs also unfocused,constraint-based analyses.Although they often perform worse than their focused versions,such analyses are often simpler to de-velop.For instance,we have developed both class and escape analysis in focused (denotational)and unfocused(constraint-based)way,which resulted in the fo-cused ps and er and in the unfocused cps and cer domains(Figure1).This does not mean that cps and cer are useless.Constraint-based static analyses can be made(completely or partially)ﬂow-insensitive by merging(all or some) variable approximations.The same is much harder to achieve with denotational abstract domains.Hence,ifﬂow-sensitivity is not important,as experiments show for class and escape analysis,then constraint-based analyses provide fast static analyses.In other cases,such as static initialisation analysis and control-ﬂow analysis,ﬂow-sensitivity is very important,so denotational,localised static analyses should be preferred.7No Constraints on PrecisionAbstract interpretation[10]entails that the precision of a static analysis is domain-related.The precision of diﬀerent domains can be formally compared without considering their implementation in the analysis.It is desirable that this situation be maintained in practice.Hence the analyser should not limit the precision of the analysis because of spurious constraints due to the way the analysis is implemented.Many static analyses compile the source program into a constraint whose solution is an approximation of the abstract behaviour of the program.This has the drawback that a given variable of the constraint is used to represent the approximation of a program variable throughout its whole existence.But a program variable can hold diﬀerent values in diﬀerent program points(ﬂow sensitiveness).Hence,this technique merges all those approximations in the same variable,thus imposing a limit to the precision of the analysis.This situation improves by using variable splitting or variable indexing in or-der to multiply the variables used in the constraint to represent a given program variable.This means that the domain developer(who writes the compilation of the source program into a constraint)must be aware of the problem.Moreover,if a given method is called from diﬀerent contexts,the same approximation is still used for all such calls.Method cloning[27]can be used here,which further complicates the analysis.Consequently,to the best of our knowledge,it has never been implemented for the Java bytecode.We prefer instead to stick to the traditional deﬁnition of abstract interpre-tation[10],so that static analysis works by computing a denotationalﬁxpoint over data-ﬂow equations derived from the structure of the program.This results in aﬂow and context sensitive analysis.The abstract domain might decide not to exploit this opportunity of precision,but no constraint is imposed by the analyser itself.Similarly,the preprocessing of the Java bytecode performed by Julia(Sec-tion5)exposes theﬂows of control arising from the lookup procedures for virtual method invocations,from exceptions and from subroutines.Again,it is an ab-stract domain matter to decide whether thoseﬂows of control must be selectively chosen,in order to get a more precise analysis,or rather they must be considered as non-deterministic choices without any preference,thus getting a less precise analysis.Often,it is just a matter of trade-oﬀbetween precision and cost of the analysis.For instance,the rt domain for rapid type analysis chooses between thoseﬂows non-deterministically,while the abstract domain ps for class analysis selects them in order to drive the analysis and collect more precise information. 8Fixpoint EngineComputing a globalﬁxpoint over data-ﬂow equations can be computationally expensive or even prohibitive.We have used some techniques to tame this com-plexity issue.Theﬁrst consists in building the maximal strongly connected components of the call graph of basic blocks and methods.These components are then sorted topologically and used to build the analysis of the whole program through local ﬁxpoints.There is a localﬁxpoint for each recursive component.For instance, Figure3contains three components.The static analyser works byﬁrst computing the analysis for component3(which does not require anyﬁxpoint)then the analysis for component2(which does require a localﬁxpoint)and,ﬁnally,the analysis for component1(without anyﬁxpoint).A second technique was originally developed for the static analysis of logic programs and is known as abstract compilation[14].During the abstract inter-pretation process,a given bytecode is repeatedly abstracted because of loops and recursion.It becomes hence convenient to abstract it once and for all,and com-pute theﬁxpoint over an abstract program i.e.,a program where each bytecode has been substituted with its abstraction into the abstract domain.For instance, the code shown in Figure3ﬁrst gets abstracted into the chosen abstract domain, as Figure5shows.Then,theﬁxpoint mechanism is applied as before.Abstract compilation can be applied repeatedly.If the analysis a of a piece of code c is stable i.e.,it will not change anymore during the analysis,then c can be substituted with a.For instance,during the computation of the localﬁxpointFig.5.The abstract compilation of the program in Figure3.for component2in Figure5we compute repeatedly the sequential composition of a4and of a 6.It is hence convenient to compute these compositions once and for all,before theﬁxpoint mechanism starts.It must be noted that the abstract domain designer is not aware of the use of strongly connected components and abstract compilation,which are domain-independent techniques.Domain-speciﬁcﬁxpoint acceleration techniques will be added in the future to Julia,through widening operators[10].They are essential for using abstract domains with inﬁnite ascending chains,such as polyhedra.9Writing Abstract Domains for JuliaNew abstract domains can be developed and plugged inside Julia.The domain developer must deﬁne the abstract counterparts of the concrete bytecodes,a bottom element,a least upper bound operator and how the watchpoints are put in the source code to perform the analysis implemented by the domain.Ab-stract domains for Julia are Java classes that extend juliet.BottomUpDomain, if localisation is used(Section6),or juliet.ConstraintDomain,otherwise.We describe here an abstract domain of theﬁrst kind,which is used for static initialisation analysis.This analysis collects the set of classes which are deﬁ-nitely initialised in each given program point.This information is useful before a goon ifnotinitialised bytecode(Figure4),which might be found to be redundant.If that is the case,they can be safely re-moved from the code,so that subsequent static analyses(class,escape analysis, etc.)will run on a simpliﬁed program,and be potentially faster and more pre-cise.The set of initialised classes enlarges only when an initialiseκor a gooninitialised.From this idea,we implemented the abstract domain for static ini-tialisation shown in Figures6,7and8.In Figure6,we see that an abstract element contains the set initialised of deﬁnitely initialised classes.The init method prepares the domain for the static analysis.In our case,it puts a watchpoint in front of each goonifnotinitialised bytecodes,since they are always coupled with a goonifinitialised tests.There is also a method that computes the bottom element of the abstract domain,another that clones an abstract domain element and another that computes the least upper bound(lub)of two abstract domain elements.This last operation consists in the intersection of the two sets of initialised classes,since a class is deﬁnitely initialised after a conditional if it is deﬁnitely initialised at the end of both its branches.The compose method com-putes the sequential composition of two abstract domain elements.It computes the union of the classes which are deﬁnitely initialised in both.The analyse method computes the abstraction of a bytecode in occurring in a basic block cb (Section5).Normally,it returns an abstract domain element whose set of ini-tialised classes is empty,except for the initialiseκand goonifinitialisedκbytecode,and checks whether the set of initialised classes there includesκ.If this is the case, the corresponding test for initialisation is useless since it will always succeed.Appropriate import statements must be put at the beginning of Figure6. The actual code is in theﬁlestatic is needed since a keyword cannot be a package name in Java).However,the code in Figure6, 7and8is perfectly working,and able to analyse all valid Java bytecode.For instance,you can apply it to itself(i.e.,its compiled code)in library mode i.e., by assuming that all its public methods may be called from outside.The result is that11out of a total of36static initialisation tests are found to be redundant.In Figures6,7and8,they are ly,the recursive static calls inside putWatchpoints do not need any initialisation of the Static class,since it has already been initialised by theﬁrst static call.The StringBuffer creation inside toString does not need any initialisation of StringBuffer since it has already been initialised by the creation of a StringBuffer object in the previous line.public class Static extends BottomUpDomain{private HashSet initialised=new HashSet();//the classes initialised private static HashSet all;//all classes checked for initialisation private static int useless,total;//useless/total initialisation tests private Static(HashSet initialised){this.initialised=initialised;} public void init(Loader loader){//we put the watchpoints in each methodfor(int i=0;i<loader.program.methods.length;i++)putWatchpoints(loader.program.methods[i]);}private static void putWatchpoints(MethodCode mc){//we add the watchpoints in the instructionsfor(int i=0;i<mc.blocks.length;i++)mc.blocks[i].ins=putWatchpoints(mc,((SEQ)ins).left);((SEQ)ins).right=putWatchpoints。

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Summer Topicals ’99CONTRIBUTED PAPER FORMPaper Title:Reduction of 3rd Order Intermodulation of a Semiconductor Laser by an Adaptive Low-Cost Predistortion Circuit at 1.8 GHzAbstract: A low-cost adaptive predistortion circuit to reduce 3rd order intermodulation of semiconductor lasers has been designed. Using a microcontroller to adjust thecontrol voltages the intermodulation has been reduced by 20 dB over thefrequency range of 1750 to 1870 MHz.Author: Guido SteinerAffiliation: Swiss Federal Institute of Technology Zurich, Laboratory for Electromagnetic Fields and Microwave ElectronicsAddress:Gloriastrasse 35City:ZurichZip:CH-8092Country:SwitzerlandPhone:+41 1 632 66 81Fax:+41 1 632 11 98E-mail:steiner@ifh.ee.ethz.chSession: RF Photonics for CATV and HFC Systems (28-30 July)Reduction of 3rd Order Intermodulation of a Semiconductor Laser by an Adaptive Low-Cost Predistortion Circuit at 1.8 GHzGuido Steiner, Stephan Hunziker* and Werner BaechtoldSwiss Federal Institute of Technology (ETH) ZurichLaboratory for Electromagnetic Fields and Microwave ElectronicsCH-8092 Zurich, Switzerland, e-mail: steiner@ifh.ee.ethz.ch* Contraves Space, CH-8052 Zurich, Switzerland, e-mail: czhs@mail.wee.ocag.chAbstract- A low-cost adaptive predistortion circuit to reduce 3rd order intermodulation of semi-conductor lasers has been designed. Using a microcontroller to adjust the control voltages the intermodulation has been reduced by 20 dB over the frequency range of 1750 to 1870 MHz.I. INTRODUCTIONAnalog fiber communications systems require high linearity. In fiber optic links the laser diodes (LDs) are the most important sources of nonlinearities [1]. By decreasing the 3rd order intermodulation (IM3) the spurious free dynamic range (SFDR) of the overall link improves. There are two major methods to reduce intermodulation: feedforward compen-sation [2] and predistortion [1], [3]. Feedforward compensation also reduces the laser intensity noise, but requires a greater number of components such as an additional LD, a photodiode and an optical coupler resulting in higher costs for the total system.A simpler approach is the predistortion technique. Predistortion can reduce the IM3 of the LD by generating an IM3 with the same amplitude but inverse phase. Unlike a feedforward system, the predistortion technique does not reduce noise. A common structure of a predistortion circuit is reported in [3]. The circuit consists of two paths: A linear path and a 3rd order distortion path with a cubic law device, an adjustable phase shifter and an adjustable attenuator.Using such a predistortion circuit a LD IM3-reduction of 23 dB over 25 MHz has already been reported [1]. The bandwidth and the IM3-reduction of the predistortion circuit described in [1] is limited due to the amplitude and phase deviations between the linear and the 3rd order distortion path. In order to reduce IM3 by 20 dB the amplitude deviation must be less than 60.5 dB and the phase deviation must be less than 658. Mainly because of the amplitude deviation of the phase shifter theserequirements are hard to meet.II. NEW TOPOLOGY AND EXPERIMENTALRESULTSAdding a phase shifter and an attenuator to the linearpath improves the symmetry of the two paths withrespect to time delay, amplitude and phase deviation.This leads to an increase of bandwidth and IM3-reduction. An additional improvement can beachieved by placing the phase shifter and theattenuator in front of the cubic law device. Thisrelaxes the bandwidth requirement of the phaseshifter and the attenuator to one third, because theIM at 2f16f2 and 2f26f1 (f1, f2: input frequencies) is generated after this devices. In such a configurationit must be guaranteed that the phase shifters and theattenuators generate much less IM3 than the cubiclaw device. A schematic diagram of the predistortioncircuit is shown in Fig. 1.Fig. 1:Schematic diagram of the predistortioncircuit.The proposed predistortion circuit has been designedin microstrip technology. A 1808-hybrid (Fig. 2, a) delivers the input signal for the linear and the nonlinear path. The signal in the linear path passes through a phase shifter (b) which is kept at a fix bias voltage where the amplitude deviation shows aminimum over the desired frequency range. The phase shifter in the nonlinear path (c), providing a phase shift of more than 3608, ensures an exact phase difference of 1808 between the two paths. In the ideal case, this phase shifter would be biased at the same voltage as the phase shifter in the linear path, resulting in equal amplitude transfer functions. An adjustable attenuator (d) sets the correct amplitude of the predistorted signal. Placing an identical attenuator (biased at a fix voltage) also in the linear path (e) maintains high symmetry. An amplifier (f) ensures that the cubic law device (g), consisting of a 1808-hybrid and two antiparallel diodes [1], operates in its nonlinear region. Completing the linear path with an amplifier (h) and a delay line (j) keeps the symmetry at a high level. The delay line has been designed to have the same electrical length as the cubic law device. AThe output signal of the predistortion circuit is fed to a 1.3 µm Fabry-Perot MQW-LD (Hitachi HL 1326CF). Fig. 3 shows the measured output signals without (top left) and with predistortion circuit (left), where the control voltages were set manually. The input signals at 1790 and 1830 MHz generate IM3 at 1750 and 1870 MHz, respectively. Setting the appropriate phase and amplitude of the nonlinear path reduces the IM3 by more than 25 dB over a bandwidth of 120 MHz. The suppression of IM3 at higher power levels is limited by the occurrence ofthe 5th order intermodulation, which cannot be reduced by the presented predistortion circuit.III. ADAPTIVE PREDISTORTIONUsing the predistortion circuit for real world optical transmission links, the control voltages for the phase shifter and the attenuator have to be readjusted due to laser aging effects [1]. Downconverting and lowpass-filtering of the IM3 generates a mixing signal proportional to the IM3 in the transmission band. After the A/D-conversion, a cheap micro-controller tracks the phase and the amplitude for maximum IM3-reduction. This adaptive process reduces the intermodulation by 20 dB over a frequency range of 120 MHz (Fig. 3, top right) which is - to our knowledge - the largest bandwidth for an adaptive predistortion circuit operating atFig. 3: Measurements ofIM3 without (top left),adaptive (top right) andmanually adjustedpredistortion circuit(left).REFERENCES[1]S. Hunziker, Analysis and Optimization of fiber opticSCM-Links, Ph.D. thesis (in German) ETH Zurich, Nr.12604, 1998.[2]L. Fock et. al., "Reduction of semiconductor laserintensity noise by feedforward compensation:experiment and theory, J. Lightwave Technol., vol. 10,no. 12, pp. 1919-1925, Dec. 1992.[3]J. Namiki, "An Automatically Controlled Predistorterfor Multilevel Quadrature Amplitude Modulation,IEEE Trans Comm., vol. 31, no. 5, pp. 707-712, May1983.。