Partial recursive functions in Martin-Lf Type Theory
基于Otsu准则及图像熵的阈值分割算法

(江苏大学电气信息工程学院,镇江 212013) 摘 要:在图像分割中,阈值的选取至关重要,在经典的 Otsu 准则基础上,结合图像熵提出了一种改进的局部递归的阈值选取及分割算 法。基于图像像素熵信息,运用递归思想局部搜索图像的最佳阈值,这样不但缩短了计算时间,而且具有较好的自适应特点。该算法在图 像背景不均匀或图像不是简单的单峰、双峰图像的情况下可以进行有效的分割,分割后的图像细节更加丰富,有利于分割后的特征提取。 对 Lena 图像进行了实验,获得了较好的分割结果。 关键词:图像分割;Otsu 准则;阈值;熵
0 ≤t ≤ L −1
Otsu 准则的推广
本文采用 IEEE 推荐的检测算法性能的 Lena 图像进行实 Lena 图像的大小为 256×256, 灰度级为 256 , 验, 如图 1 所示, 从 0~ 255。图 2 分析了 Lena 图像的灰度分布情况,图 3 为 经典 Otsu 准则的分割结果, 图 4 为本文所述方法的分割结果。
Hale Waihona Puke 熵函数的定义为 H(P)=H(P1,P2,…,Pn)=
∑ p log Pi。
i =1 i
n
在图像分割问题中,也可以运用熵的方法处理,由于目 标和背景的灰度分布是不同的,因此可以被视为 2 个独立的 信源。在 2.1 节中符号设定下,目标和背景的信源熵分别为
H A = −∑ pi log pi , H B = − ∑ pi log pi 。
2 σ 2 = p A (ω A − ω0 ) + pB (ωB − ω0 ) 2 显然, p A 、 pB 、 ω A 、 ω B 、 ω0 、 σ 2 都是关于灰度级 t 的
函数。 为了得到最优分割阈值,Otsu 把两类的类间方差作为判 别准则,认为使得 σ 2 值最大的 t * 即为所求的最佳阈值: t * = Arg Max [ p A (ω A − ω0 ) 2 + pB (ωB − ω0 ) 2 ]
噪声估计算法.doc

太原理工大学毕业设计(论文)任务书毕业设计(论文)题目;2004年Rangachari和Loizou提出了一种快速估讣方法,不仅使得带噪语音子带中语音出现概率讣算更准确,而且噪声谱的更新在连续时间内不依赖固怎时间的窗长,但是在语音或噪声能量过高时噪声的估计就会慢下来,而且如果时间大于0.5s时,就会削弱一些语音能量。
因此,噪声估计算法有待更进一步的改进。
2009年余力,陈颖琪提出了一种基于DCT变换的自适应噪声估计算法,采用DCT系数作为块均匀度的度量,较好地适应了高低噪声的情况,算法复杂度不大,能适用于各种实时图像视频处理系统。
理论分析及实验结果表明本算法不仅在低噪声的图片中表现出良好的性能,而且在高噪声的图片中依旧有效。
此外,还能适应不同质量的图像。
通过对基于最小统讣量的噪声估计方法和改进的最小统计量控制递归平均噪声估计算法研究发现这些噪声估计方法可以在语音存在段进行噪声估计,能够有效地跟踪非平稳噪声。
但是,这些算法在各个频带进行噪声估计,算法复杂度髙,噪声估计方差大。
于是在考虑各频带间的相关性上提岀了在巴克域进行噪声估计,减小了噪声估il•方差,提髙了噪声估计的准确性,并极大地减小算法运算量和存储量。
而且,在巴克域进行噪声估计更符合人耳听觉特性,增强语音具有更好的质量。
英他类似的方法还有低能量包络跟踪和基于分位数的估计方法,后者噪声的估计是基于带噪语音未平滑功率谱的分位数,而不是提取平滑功率谱的最小值,但是此方法il•算复杂度很高,且需要很大的内存来存储过去的功率谱值。
毕业设计(论文)主要内容:本文分为五章,具体章节内容如下:第一章绪论。
噪声估计算法研究的目的和意义及国内外研究的现状。
第二章几种经典的噪声估计的算法。
Martin的最小统计量的估计算法,Cohen和Berdu旳提出的基于最小统计量控制递归平均算法,通过比较最终提出改进的最小统计量控制递归平均算法,仿真结果表明,这种方法在非平稳噪声条件下,也具有较好的噪声跟踪能力和较小的噪声估计误差,可以有效地提髙语音增强系统的性能。
二阶非散度型椭圆方程解的局部正则性估计

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Applied Economics Letters,2006,13,569–574Club convergence inEuropean regionsRita De Siano a and Marcella D’Uva b,*a Department of Economic Studies,University of Naples‘Parthenope’,Via Medina40,80133Naples,Italyb Department of Social Sciences,University of Naples L’Orientale,Largo S.Giovanni Maggiore30,80134Naples,ItalyThis study investigates the‘club convergence’hypothesis applying the stochastic notion of convergence to groups of European regions.In order to avoid the group selection bias problem,the innovative regression tree technique was applied to select endogenously the most important variables in achieving the best identification of groups on the base of per capita income and productive specialization.Tests on stochastic convergence in each group evidences a strong convergence among the wealthiest regions of the European Union and a trend of weak convergence among the remaining groups,confirming Baumol’s hypothesis of convergence.I.IntroductionOver the past decade many authors have explored the evolution of output discrepancies,at both national and regional levels.In particular,starting with Baumol(1986)it has been widely hypothesized that convergence may hold not for all economies but within groups of them showing similar characteristics (Azariadis and Drazen,1990).This evidence is referred to as the‘club convergence’hypothesis which implies that a set of economies may converge with each other,in the sense that in the long run they tend towards a common steady state position, but there is no convergence across different sets. In seeking to test the club convergence hypothesis (Qing Li,1999;Feve and Le Pen,2000;Su,2003,for example)two main questions arise:(a)which frame-work of convergence to use,and(b)how to identify the economies belonging to each club.Initially,a cross-section notion of convergence was used in order to verify the existence of a negative relationship between initial per capita income and its growth rate. In contrast with this notion a stochastic definition of convergence(Carlino and Mills,1993)was proposed and explored by using time series analyses. According to this framework there is stochastic convergence if per capita income disparities between economies follow a stationary process.Bernard and Durlauf(1996)found that when economies show multiple long run equilibria,cross-sectional tests tend to spuriously reject the null hypothesis of no convergence and,as a consequence,represent a weaker notion of convergence than that of the time series.As regards the second point,two methods can be used in order to create different groups of economies.The first sorts of economies follows some a priori criteria(initial level of GDP,education, technology,capital accumulation,etc.)while the second follows an endogenous selection method (Durlauf and Johnson,1995).Finally,the switching regression with the contribution of additional infor-mation on the sample separation followed by Feve and Le Pen(2000)can be mentioned as an intermediate method in modelling convergence clubs. This study investigates the‘club convergence’hypothesis applying the stochastic notion of conver-gence to groups of European regions sorted accord-ing to their initial levels of per capita income and*Corresponding author.E-mail:mduva@unior.itApplied Economics Letters ISSN1350–4851print/ISSN1466–4291onlineß2006Taylor&Francis569/journalsDOI:10.1080/13504850600733473productive specialization(De Siano and D’Uva, 2004,2005)through the application of an innovative methodology known as Classification and Regression Tree Analysis(CART).Unlike other partitioning methods,CART allows a regression to be performed together with a classification analysis on the same ‘learning’dataset,without requiring particular speci-fication of the functional form for the predictor variables which are selected endogenously.The importance of similarities in the initial productive specialization has been highlighted by several theore-tical contributions(Jacobs,1969;Marshall,1980; Romer,1986;Lucas,1988;Helg et al.,1995;Bru lhart, 1998;Ottaviano and Puga,1998)which found that it can be crucial in determining both the nature and size of responses to external shocks.The paper is organized as follows:Section II introduces the methodology of the empirical analysis, Section III displays the dataset,Section IV shows the results of econometric analysis and Section V concludes.II.MethodologyThe empirical analysis is carried out in two parts:first regions are grouped through the classification and regression tree analyses(CART),then convergence is tested within‘clubs’using the time series analysis. CART methodology(Breiman et al.,1984)provides binary recursive partitioning using non-parametric approaches in order to construct homogeneous groups of regions using splitting variables which minimize the intra-group‘impurity’as predictors. The final outcome is a tree with branches and ‘terminal nodes’,as homogeneous as possible,where the average value of the node represents the predicted value of the dependent variable.In this analysis the regression is carried out through the least squares method using the regional GDP growth rate as dependent variable and initial GDP and specializa-tion indexes as explicative variables.In the second part of the study Carlino and Mills(1993)notion of stochastic convergence is applied in each group identified by CART methodology.It follows that if the logarithm of a region’s per capita income relative to the group’s average does not contain a unit root,the region converges.The model(Ben-David, 1994;Qing Li,1999)is the following:y j i,t ¼ iþ i tþ’y i,tÀ1þ"i,tð1Þwhere y j i,t is the log of region i per capita income inyear t,j is the region’s group and"is white noise errorwith0mean.Summing Equation1over j for eachgroup and dividing the outcome by the number ofregions within the group,the following equation isobtained:"y t¼" þ" tþ’"y tÀ1þ"tð2Þwhere"y t is the group’s average per capita incomein year t(the group superscript is suppressed).Subtracting Equation2from Equation1one has:RI i,t¼AþBtþ’RI i,tÀ1þ"tð3Þwhere RI i,t is the logarithm of region i per capitaincome relative to the group’s average at time t(y j i,tÀ"y t).For each region of the sample we apply theAugmented Dickey–Fuller(ADF)test(Dickey andFuller,1979)using the ADF regression ofEquation3:ÁRI t¼ þ tþ RI tÀ1þX kj¼1c jÁRI tÀjþ"tð4ÞAt this point,considering the low power of the ADFtest in the case of short time series,we run alsothe Kwiatkowski et al.(1992)test(KPSS)for trendstationarity.The null hypothesis of the KPSS test isthe trend stationarity against the unit root alter-native.If the KPSS statistic is larger than the criticalvalues the null hypothesis is rejected.The combinedanalysis of KPSS and ADF tests results leads on thefollowing possibilities(Qing Li,1999):.rejection by ADF tests and failure to reject byKPSS!strong convergence;.failure to reject by both ADF and KPSS!weakconvergence;.rejection by KPSS test and failure to rejectADF!no convergence;.rejection by both ADF and KPSS tests invitesto perform further analyses.III.Data DescriptionThis section presents the dataset used both to groupthe sample regions and to run the econometricanalysis.Data for GDP and employment are fromthe Eurostat New Cronos Regio database at NUTS2level.1Annual values for GDP per inhabitant in termsof Purchasing Power Parity(PPP)and the number of1According to EC Regulation No.1059/2003.570R.De Siano and M.D’Uvaemployees in the NACE92productive branches from1981to 2000are used.The sample consists of 123regions belonging to nine countries:11Belgian,8Dutch,29German,222French,20Italian,18Spanish,5Portuguese,2Greek,38British.4For each region (i )the following initial productivespecialization indexes (SP)were built for all theconsidered branches 5(j ):SP ij ¼E ij P n j ¼1ij P m i ¼1E ij P n j ¼1P mi ¼1ijð5Þwhere E indicates the number of employees.IV.Empirical ResultsThe main purpose of the study is to test the ‘clubconvergence’hypothesis across the European regions.In particular,the study aims to investigate whethera region’s per capita income converges to the averageof the group to which it belongs.In order to avoidthe group selection bias problem,the regressiontree technique was applied to select endogenouslythe most important variables in achieving thebest identification of groups (De Siano and D’Uva,2005).If the majority of regions in a groupconverges,the group may be considered a conver-gence ‘club’.The CART method allowed a tree to be built withfour terminal nodes including regions showing a morehomogeneous behaviour of per capita GDP growthrate and productive specialization.Results of CARTanalysis together with the stochastic convergence tests for each group are presented in what follows.The first group consists of 11regions (from Spain,Greece and Portugal)characterized by:the highest estimated mean value of GDP growth rate (126.08%)despite the lowest initial income level (average equal to 4144.3);strong specialization in the agriculture sector (the highest and equal to 3.75),construction branch (2.09)and food and beverages compartment (1.93);the minimum specialization in chemical,energy,and machinery branches and the highest in food-beverages-tobacco,mineral and construction.More than 80%of these regions display ‘weak’convergence while remaining regions show ‘strong’convergence (Table 1).The second group includes 23regions (mainly from Belgium,Spain,Italy and the United Kingdom)characterized by:an average GDP growth rate equal to 111.36%and the second highest initial income level (5788.78);strong specialization in agriculture (2.68)sector,food and beverage (1.26),construction (1.52)and energy (1.20)compartments;the highest specialization in chemical products (0.98);the second highest level of specialization in agricul-ture construction and energy.Almost all these regions present ‘weak’convergence (Table 2).The third group is formed by 21regions from Belgium,France,Germany,the Netherlands,Spain,the UK and Italy (only Abruzzo)characterized by:an estimate for the GDP growth rate of 106%and an average initial level of income equal to 6920.6;main specializations in manufacturing (1.03),mineral products (1.13),construction (1.22),food and beverage (1.45)and energy (1.21);the highest 2The analysis starts from 1984due to the lack of data in the respective regional labour statistics.3During the period 1983–1987there has been a different aggregation of Greek regions at NUTS2level.Kriti and Thessalia are the only regions which presents data for the period 1984–2000.4The geographic units for UK are at NUTS1level of Eurostat classification because of the lack of data for NUTS2units.5Agricultural-forestry and fishery,manufacturing,fuel and power products,non-metallic minerals and minerals,food-beverages-tobacco,textiles-clothing-leather and footwear,chemical products,metal products,machinery-equipment and electrical goods,various industries,building and construction,transport and communication,credit and insurance services.Table 1.Convergence test results of group 1Regions group 1ADF statistics KPSS statistics l ¼4Regions group 1ADF statistics KPSS statistics l ¼4Castilla-la ManchaÀ2.9780.099gr 43Kriti À4.05ÃÃ0.080ExtremaduraÀ3.320.097Pt11Norte À4.03ÃÃ0.126AndaluciaÀ2.630.094Pt12Centro (P)À2.290.123Ceuta y MelillaÀ1.770.123Pt14Alentejo À2.770.104CanariasÀ1.940.121Pt15Algarve À2.010.086ThessaliaÀ1.760.137Notes :ÃÃdenote statistical significance using unit root critical values at the 5%(À3.645).Club convergence in European regions571specialization in energy and manufacturing branches.Except for Abruzzo and Noord Brabant,which donot converge,all the other regions ‘weakly’convergeto the group’s average (Table 3).The fourth group contains 68regions (almost allGerman,French and Italian (North-Centre)andsome Belgian and Dutch)characterized by thelowest estimation of the GDP growth rate (97.8%),despite their highest initial GDP level (8893.9);thehighest specialization in the branches of the servicessector (1.16and 1.07,respectively)and in machinery(1.01);the lowest specialization in agriculture,foodand beverages,textile and construction activities.These regions present the highest percentage of‘strong’convergence to the group’s average (morethan 60%,Table 4).Table 5presents the summary of convergence testsresults (percentage are in parentheses).The main outcome of this study is the evidence of strong convergence among the wealthiest regions of the European Union.Besides,it appears that there is a trend of weak convergence also among the remaining groups (percentages are considerably over 80%).Therefore,Baumol’s hypothesis of conver-gence within clubs showing similar characteristics is confirmed.V.Conclusion This study tests the ‘club convergence’hypothesis applying the stochastic notion of convergence to groups of European regions.In order to avoid the group selection bias problem,the innovative regression tree technique was applied to selectTable 3.Convergence test results of group 3Regions group 3ADF statistics KPSS statistics l ¼4Regions group 3ADF statistics KPSS statistics l ¼4LimburgÀ1.680.116Abruzzo 2.600.153ÃÃHainautÀ0.800.091Friesland À3.620.142NamurÀ1.840.094Noord-Brabant À2.590.148ÃÃNiederbayernÀ1.270.104Limburg (NL)À2.980.128OberpfalzÀ1.400.097Yorkshire and The Humber À1.610.085TrierÀ1.430.119East Midlands À2.190.091Comunidad Foral de NavarraÀ2.750.071West Midlands À1.920.080La RiojaÀ1.770.119East Anglia À2.150.134BalearesÀ2.960.108South West À1.950.091LimousinÀ2.410.083Scotland 2.220.093Languedoc-RoussillonÀ3.390.105Notes :ÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146).Table 2.Convergence test results of group 2Regions group 2ADF statistics KPSS statistics l ¼4Regions group 2ADF statistics KPSS statistics l ¼4Vlaams BrabantÀ1.220.100Murcia À1.530.124Brabant WallonÀ1.600.111Molise À2.170.078Luxembourg1.190.122Campania À3.220.078Lu neburgÀ0.280.114Puglia À2.820.115GaliciaÀ1.690.140Basilicata À2.100.140Principado de AsturiasÀ1.550.146ÃÃCalabria À5.07ÃÃÃ0.106CantabriaÀ1.080.133Sicilia À2.980.142Aragon À1.580.142Sardegna À2.210.141Comunidad de MadridÀ1.380.091Lisboa e Vale do Tejo À2.620.141Castilla y Leon À2.580.138Wales À2.120.098Cataluna À1.550.097Northern Ireland À1.790.120Comunidad Valenciana À1.420.105Notes :ÃÃand ÃÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146)and 1%level (0.216)respectively,using unit root critical values at the 5%(À3.645)and 1%(À4.469).572R.De Siano and M.D’Uvaendogenously the most important variables inachieving the best identification of groups.Testson stochastic convergence in each group identifiedby CART evidence strong convergence among thewealthiest regions of the European Union and atrend of weak convergence among the remaininggroups.References Azariadis,C.and Drazen,A.(1990)Threshold externalities in economic development,Quarterly Journal of Economics ,105,501–26.Baumol,W.J.(1986)Productivity growth,convergence and welfare:what the long run data show,AmericanEconomic Review ,76,1072–85.Table 5.Convergence test resultsGroupsNo.of regions Strong convergence Weak convergence No convergence 1112(18,19)9(81,81)2231(4.35)21(91.3)1(4.35)32119(90.48)2(9.52)46843(63.23)20(29.41)4(5.88)Table 4.Convergence test results of group 4Regions group 4ADF statistics KPSS statistics l ¼4Regions group 4ADF statistics KPSS statistics l ¼4RegionBruxelles capitale À2.650.112Haute-Normandie À4.11ÃÃ0.102AntwerpenÀ2.770.102Centre (FR)À5.13ÃÃÃ0.099Oost-VlaanderenÀ3.150.078Basse-Normandie À3.86ÃÃ0.101West-VlaanderenÀ3.030.097Bourgogne À5.03ÃÃÃ0.113Licge À3.060.089Nord-Pas-de-Calais À4.37ÃÃ0.130StuttgartÀ4.22ÃÃ0.123Lorraine À4.41ÃÃ0.139KarlsruheÀ4.51ÃÃÃ0.088Alsace À4.13ÃÃ0.094FreiburgÀ5.11ÃÃÃ0.092Franche-Comte À5.20ÃÃÃ0.145Tu bingenÀ4.94ÃÃÃ0.104Pays de la Loire À4.34ÃÃ0.116OberbayernÀ4.17ÃÃ0.094Bretagne À4.41ÃÃ0.124MittelfrankenÀ3.79ÃÃ0.089Poitou-Charentes À4.74ÃÃÃ0.102UnterfrankenÀ0.420.140Aquitaine À3.290.104SchwabenÀ4.11ÃÃ0.084Midi-Pyre ne es À5.48ÃÃÃ0.103BremenÀ3.76ÃÃ0.121Rho ne-Alpes À4.93ÃÃÃ0.104HamburgÀ3.350.097Auvergne À4.43ÃÃ0.135DarmstadtÀ3.150.125Provence-Alpes-Co te d’Azur À5.10ÃÃÃ0.109GießenÀ3.020.088Corse À2.560.166ÃÃKasselÀ3.0120.094Piemonte À3.460.112BraunschweigÀ3.82ÃÃ0.116Valle d’Aosta À4.36ÃÃ0.080HannoverÀ3.96ÃÃ0.083Liguria À4.26ÃÃ0.117Weser-EmsÀ3.400.084Lombardia À4.04ÃÃ0.101Du sseldorfÀ3.94ÃÃ0.097Trentino-Alto Adige À3.84ÃÃ0.109Ko lnÀ3.96ÃÃ0.084Veneto À3.68ÃÃ0.106Mu nsterÀ4.04ÃÃ0.087Friuli-Venezia Giulia À4.20ÃÃ0.116DetmoldÀ4.06ÃÃ0.099Emilia-Romagna À3.120.136ArnsbergÀ3.98ÃÃ0.096Toscana À3.190.121KoblenzÀ3.88ÃÃ0.113Umbria À3.560.146ÃÃRheinhessen-PfalzÀ4.18ÃÃ0.107Marche À3.250.136SaarlandÀ4.35ÃÃ0.090Lazio À3.96ÃÃ0.098Schleswig-HolsteinÀ3.360.089Drenthe À1.850.134Pais VascoÀ3.630.159ÃÃUtrecht À2.400.155ÃÃI le de FranceÀ4.61ÃÃÃ0.110Noord-Holland À1.990.137Champagne ArdenneÀ3.79ÃÃ0.157ÃÃZuid-Holland À2.200.138Picardie À4.44ÃÃ0.142Zeeland À3.78ÃÃ0.093Notes :ÃÃand ÃÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146)and 1%level (0.216)respectively,using unit root critical values at the 5%(À3.645)and 1%(‘4.469).Club convergence in European regions573Ben-David, D.(1994)Convergence clubs and diverging economies,unpublished manuscript,University of Houston,Ben-Gurion University and CEPR. Bernard, A. B.and Durlauf,S.N.(1996)Interpreting tests of the convergence hypothesis,Journal of Econometrics,71,161–73.Breiman,L.,Friedman,J.L.,Olshen,R.A.and Stone,C.J.,(1984)Classification and Regression Trees,Wadsworth,Belmont,CA.Bru lhart,M.(1998)Economic geography,industrial location and trade:the evidence,World Economy,21, 775–801.Carlino,G.A.and Mills,L.O.(1993)Are US regional incomes converging?A time series analysis,Journal of Monetary Economics,32,335–46.De Siano,R.and D’Uva,M.(2004)Specializzazione e crescita:un’applicazione alle regioni dell’Unione Monetaria Europea,Rivista Internazionale di Scienze Sociali,4,381–98.De Siano,R.and D’Uva,M.(2005)Regional growth in Europe:an analysis through CART methodology, Studi Economici,87,115–28.Dickey,D.A.and Fuller,W.A.(1979)Distribution of the estimators for autoregressive time series with a unit root,Journal of The American Statistical Association, 74,427–31.Durlauf,S.N.and Johnson,P.A.(1995)Multiple regimes and cross-country growth behaviour,Journal of Applied Econometrics,10,365–84.Feve,P.and Le Pen,Y.(2000)On modelling convergence clubs,Applied Economic Letters,7,311–14.Helg,R.,Manasse,P.,Monacelli,T.and Rovelli,R.(1995) How much(a)symmetry in Europe?Evidence from industrial sectors,European Economic Review,39, 1017–41.Jacobs,J.(1969)The Economy of Cities,Jonathen Cape, London.Kwiatkowski, D.,Phillips,P. C. B.,Schmidt,P.and Shin,Y.(1992)Testing the null hypothesis of stationarity against the alternative of a unit root:how sure are we that economic time series have a unit root?,Journal of Econometrics,54, 159–78.Lucas,R. E.(1988)On the mechanics of economic development,Journal of Monetary Economics,22, 3–42.Marshall,A.(1980)Principles of Economics,Macmillan, London.Ottaviano,I.and Puga,D.(1998)Agglomeration in the global economy:a survey of the‘new economic geography’,World Economy,21,707–31.Qing,L.(1999)Convergence clubs:some further evidence, Review of International Economics,7,59–67. Romer,P.M.(1986)Increasing returns and long run growth,Journal of Political Economy,94, 1002–37.Su,J.J.(2003)Convergence clubs among15OECD countries,Applied Economic Letters,10,113–18.574R.De Siano and M.D’Uva。
第二讲 图灵机模型

182Leabharlann 1.1 基本图灵机例 2-3 设有M2=({q0, q1, q2, q3},{0, 1},{0, 1, B},δ,q0 , B ,{q3}),其中δ的定义如下: δ(q0, 0)= (q0, 0, R) δ(q0, 1)= (q1, 1, R) δ(q1, 0)= (q1, 0, R) δ(q1, 1)= (q2, 1, R) δ(q2, 0)= (q2, 0, R) δ(q2, 1)= (q3, 1, R)
1
主要内容、重难点
主要内容
–
图灵机作为一个计算模型,它的基本定义,即时描 述,图灵机接受的语言;图灵机的构造技术;图灵 机的变形;Church-Turing论题;通用图灵机。可 计算语言、不可判定性、P-NP问题)。
重点
–
图灵机的定义、图灵机的构造。
难点
– 图灵机的构造。
2
2.1 基本概念
19
2.1.1 基本图灵机
0 q0 q1 q2 q3 (q0, 0, R) (q1, 0, R) (q2, 0, R) 1 (q1, 1, R) (q2, 1, R) (q3, 1, R) B
20
2.1.1 基本图灵机
为了弄清楚M2接受的语言,需要分析它的工
作过程。 (1)处理输入串00010101的过程中经历的ID变 换序列如下: q000010101├ 0q00010101├ 00q0010101 ├ 000q010101├ 0001q10101├ 00010q1101 ├ 000101 q201├000101 0 q21├ 00010101q3
31
2.1.2 图灵机作为非负整函数的计算模型
图灵可计算的(Turing computable) 设有k元函数f(n1, n2,…, nk)=m,TM M=(Q, ∑, Γ, δ,q0 , B , F)接受输入串
Functional-coefficient regression models for nonlinear time series

from with
the \curse of dimensionality".
Ui taking values in <k and Xi
tLaketinfgUvai;luXesi;inYi<g1ip=.?T1ypbiecajlolyintklyis
strictly small.
stationary Let E(Y12)
transpose of a matrix or vector. The idea to model time series in such a form is not new; see,
for example, Nicholls and Quinn (1982). In fact, many useful time series models may be viewed
This paper adapts the functional-coe cient modeling technique to analyze nonlinear time series
data. The approach allows appreciable exibility on the structure of tted model without su ering
Ui and Xi consist of some lagged values of Yi. The functional-coe cient regression model has the
form
m(u; x) = Xp aj(u) xj;
(1.2)j=1来自where aj( )'s are measurable functions from <k to <1 and x = (x1; : : :; xp)T with T denoting the
融合卡方累积分布函数和PDE的曲波阈值去噪法_崔华_严玍伻

第41卷 第4期 JOURNAL OF XIDIAN UNIVERSITY V ol.41 No.4 ______________________________收稿日期:2013-09-04 网络出版时间: 基金项目:863资助项目(2012AA112312);交通部资助项目(201231849A70);省攻关计划资助项目(2011k06-21);doi :10.3969/j.issn.1001-2400.2014.04.019融合卡方累积分布函数和PDE 的曲波阈值去噪法崔华, 严玍伻( 长安大学 信息工程学院,陕西 西安 710064)摘要:针对硬阈值函数不连续导致视觉失真和软阈值函数存在恒定重构偏差的问题,依据噪声的曲波系数分布特性以及理想阈值函数特性,提出了基于卡方累积分布函数的新阈值函数。
进一步地,为了克服阈值去噪法固有的环绕效应和难以兼顾细节保持与去噪效果的性能缺陷,将偏微分方程(partial differential equation, PDE)去噪图像中包含的有益信息融合进新阈值函数去噪图像中,提出了新去噪方法。
理论分析和仿真结果一致表明,较软、硬阈值去噪法,本文采用卡方累积分布函数和PDE 改进的曲波阈值去噪方法可以有效地改善去噪效果和视觉质量。
关键词:图像去噪;曲波阈值法;卡方分布;偏微分方程中图分类号:TP751 文献标识码:A 文章编号:1001-2400(2014)04-0130-06Improved curvelet thresholding denoising method by Chi-Squaredcumulation distribution function and PDECUI Hua, YAN Gabeng(School of Information Engineering, Chang’an University, Xi’an 710064, China)Abstract: To circumventthe visual distortion due to the discontinuity of hard threshold function and the constant reconstruction deviationcaused by soft thresholding function, this paper presents a novel thresholdingfuction based on Chi-Squaredcumulationdistributionfunctionaccording to the distribution characteristics of curveletcoefficients of the noise and the ideal properties thehigh effectivecurveletthreshold functionsshould have. Further, in order to eliminate surround effectinherent in curveletthreshold denoising methods and achieve a better balance between detail conservation and noise reduction, useful information involved in a denoised image produced by partial differential equationdenoising method was fused with thatby novel curveletthreshold function into the proposed denoising method.Theoretical analysis and simulation results show that the proposed denoiding method outperforms the soft and hard threshold denoising methodsin terms ofthe denoising effect and visual quality.Key Words: image denoising; curveletthresholdingalgorithm; Chi-Squared distribution; partial differential equation1 引言曲波阈值去噪法[1]首先对图像做曲波变换[2],得到图像的曲波子带系数,然后按照一定的阈值函数对子带系数进行处理,最后对经过处理的子带系数进行逆曲波变换,重构图像即为去噪图2013-12-12 09:42像。
数字信号处理英语词汇

数字信号处理英语词汇AAbsolutely integrable 绝对可积Absolutely integrable impulse response 绝对可积冲激响应Absolutely summable 绝对可和Absolutely summable impulse response 绝对可和冲激响应Accumulator 累加器Acoustic 声学Adder 加法器Additivity property 可加性Aliasing 混叠现象All-pass systems 全通系统AM (Amplitude modulation ) 幅度调制Amplifier 放大器Amplitude modulation (AM)幅度调制Amplitude-scaling factor 幅度放大因子Analog-to-digital (A-to-D) converter 模数转换器Analysis equation 分析公式(方程) Angel (phase) of complex number 复数的角度(相位)Angle criterion 角判据Angle modulation 角度调制Anticausality 反因果Aperiodic 非周期Aperiodic convolution 非周期卷积Aperiodic signal 非周期信号Asynchronous 异步的Audio systems 音频(声音)系统Autocorrelation functions 自相关函数Automobile suspension system 汽车减震系统Averaging system 平滑系统BBand-limited 带(宽)限的Band-limited input signals 带限输入信号Band-limited interpolation 带限内插Bandpass filters 带通滤波器Bandpass signal 带通信号Bandpass-sampling techniques 带通采样技术Bandwidth 带宽Bartlett (triangular) window 巴特利特(三角形)窗Bilateral Laplace transform 双边拉普拉斯变换Bilinear 双线性的Bilinear transformation 双线性变换Bit (二进制)位,比特Block diagrams 方框图Bode plots 波特图Bounded 有界限的Break frequency 折转频率Butterworth filters 巴特沃斯滤波器C“Chirp” transform algorithm “鸟声”变换算法Capacitor 电容器 Carrier 载波Carrier frequency 载波频率Carrier signal 载波信号Cartesian (rectangular) form 直角坐标形式Cascade (series) interconnection 串联,级联Cascade-form 串联形式Causal LTI system 因果的线性时不变系统Channel 信道,频道Channel equalization 信道均衡Chopper amplifier 斩波器放大器Closed-loop 闭环Closed-loop poles 闭环极点Closed-loop system 闭环系统Closed-loop system function 闭环系统函数Coefficient multiplier 系数乘法器Coefficients 系数Communications systems 通信系统Commutative property 交换性(交换律)Compensation for nonideal elements 非理想元件的补偿Complex conjugate 复数共轭Complex exponential carrier 复指数载波Complex exponential signals 复指数信号Complex exponential(s) 复指数Complex numbers 复数Conditionally stable systems 条件稳定系统Conjugate symmetry 共轭对称Conjugation property 共轭性质Continuous-time delay 连续时间延迟Continuous-time filter 连续时间滤波器Continuous-time Fourier series 连续时间傅立叶级数Continuous-time Fourier transform 连续时间傅立叶变换Continuous-time signals 连续时间信号Continuous-time systems 连续时间系统Continuous-to-discrete-time conversion 连续时间到离散时间转换Convergence 收敛Convolution 卷积Convolution integral 卷积积分Convolution property 卷积性质Convolution sum 卷积和Correlation function 相关函数Critically damped systems 临界阻尼系统Crosss-correlation functions 互相关函数Cutoff frequencies 截至频率DDamped sinusoids 阻尼正弦振荡Damping ratio 阻尼系数Dc offset 直流偏移Dc sequence 直流序列Deadbeat feedback systems 临界阻尼反馈系统Decibels (dB) 分贝Decimation 抽取Decimation and interpolation 抽取和内插Degenerative (negative) feedback 负反馈Delay 延迟Delay time 延迟时间Demodulation 解调Difference equations 差分方程Differencing property 差分性质Differential equations 微分方程Differentiating filters 微分滤波器Differentiation property 微分性质Differentiator 微分器Digital-to-analog (D-to-A) converter 数模转换器Direct Form I realization 直接I 型实现Direct form II realization 直接II型实现Direct-form 直接型Dirichlet conditions 狄里赫利条件Dirichlet, P.L. 狄里赫利Discontinuities 间断点,不连续Discrete-time filters 离散时间滤波器Discrete-time Fourier series 离散时间傅立叶级数Discrete-time Fourier series pair 离散时间傅立叶级数对Discrete-time Fourier transform (DFT) 离散时间傅立叶变换Discrete-time LTI filters 离散时间线性时不变滤波器Discrete-time modulation 离散时间调制Discrete-time nonrecursivefilters 离散时间非递归滤波器Discrete-time signals 离散时间信号Discrete-time systems 离散时间系统Discrete-time to continuous-time conversion 离散时间到连续时间转换Dispersion 弥撒(现象)Distortion 扭曲,失真Distribution theory(property) 分配律Dominant time constant 主时间常数Double-sideband modulation (DSB) 双边带调制Downsampling 减采样Duality 对偶性EEcho 回波Eigenfunctions 特征函数Eigenvalue 特征值Elliptic filters 椭圆滤波器Encirclement property 围线性质End points 终点 Energy of signals 信号的能量Energy-density spectrum 能量密度谱Envelope detector 包络检波器Envelope function 包络函数Equalization 均衡化Equalizer circuits 均衡器电路Equation for closed-loop poles 闭环极点方程Euler, L. 欧拉Euler’s relation 欧拉关系(公式) Even signals 偶信号Exponential signals 指数信号Exponentials 指数FFast Fourier transform (FFT) 快速傅立叶变换Feedback 反馈Feedback interconnection 反馈联结Feedback path 反馈路径Filter(s) 滤波器Final-value theorem 终值定理Finite impulse response (FIR) 有限长脉冲响应Finite impulse response (FIR) filters 有限长脉冲响应滤波器Finite sum formula 有限项和公式Finite-duration signals 有限长信号First difference 一阶差分First harmonic components 基波分量 (一次谐波分量)First-order continuous-time systems 一阶连续时间系统First-order discrete-time systems 一阶离散时间系统First-order recursive discrete-time filters 一阶递归离散时间滤波器 First-order systems 一阶系统Forced response 受迫响应Forward path 正向通路Fourier series 傅立叶级数Fourier transform 傅立叶变换Fourier transform pairs 傅立叶变换对Fourier, Jean Baptiste Joseph 傅立叶(法国数学家,物理学家) Frequency response 频率响应Frequency response of LTI systems 线性时不变系统的频率响应Frequency scaling of continuous-time Fourier transform 连续时间傅立叶变化的频率尺度(变换性质) Frequency shift keying (FSK) 频移键控Frequency shifting property 频移性质Frequency-division multiplexing (FDM) 频分多路复用Frequency-domain characterization 频域特征Frequency-selective filter 频率选择滤波器Frequency-shaping filters 频率成型滤波器Fundamental components 基波分量Fundamental frequency 基波频率Fundamental period 基波周期GGain 增益Gain and phase margin 增益和相位裕度General complex exponentials 一般复指数信号Generalized functions 广义函数Gibbs phenomenon 吉伯斯现象Group delay 群延迟HHalf-sample delay 半采样间隔时延Hanning window 汉宁窗Harmonic analyzer 谐波分析议Harmonic components 谐波分量Harmonically related 谐波关系Heat propagation and diffusion 热传播和扩散现象Higher order holds 高阶保持Highpass filter 高通滤波器Highpass-to-lowpass transformations 高通到低通变换Hilbert transform 希尔波特滤波器Homogeneity (scaling) property 齐次性(比例性)IIdeal 理想的Ideal bandstop characteristic 理想带阻特征Ideal frequency-selective filter 理想频率选择滤波器Idealization 理想化Identity system 恒等系统Imaginary part 虚部Impulse response 冲激响应Impulse train 冲激串Incrementally linear systems 增量线性系统Independent variable 独立变量Infinite impulse response (IIR) 无限长脉冲响应Infinite impulse response (IIR) filters 无限长脉冲响应滤波器Infinite sum formula 无限项和公式Infinite taylor series 无限项泰勒级数Initial-value theorem 初值定理Inpulse-train sampling 冲激串采样Instantaneous 瞬时的Instantaneous frequency 瞬时频率Integration in time-domain 时域积分Integration property 积分性质Integrator 积分器Interconnection 互联Intermediate-frequency (IF) stage 中频级Intersymbol interference (ISI) 码间干扰Inverse Fourier transform 傅立叶反变换Inverse Laplace transform 拉普拉斯反变换Inverse LTI system 逆线性时不变系统Inverse system design 逆系统设计Inverse z-transform z反变换Inverted pendulum 倒立摆Invertibility of LTI systems 线性时不变系统的可逆性Invertible systems 逆系统LLag network 滞后网络Lagrange, J.L. 拉格朗日(法国数学家,力学家)Laplace transform 拉普拉斯变换Laplace, P.S. de 拉普拉斯(法国天文学家,数学家)lead network 超前网络left-half plane 左半平面left-sided signal 左边信号Linear 线性Linear constant-coefficient difference equations 线性常系数差分方程 Linear constant-coefficient differential equations 线性常系数微分方程Linear feedback systems 线性反馈系统Linear interpolation 线性插值Linearity 线性性Log magnitude-phase diagram 对数幅,相图Log-magnitude plots 对数模图Lossless coding 无损失码Lowpass filters 低通滤波器Lowpass-to-highpass transformation 低通到高通的转换LTI system response 线性时不变系统响应LTI systems analysis 线性时不变系统分析MMagnitude and phase 幅度和相位Matched filter 匹配滤波器Measuring devices 测量仪器Memory 记忆Memoryless systems 无记忆系统Modulating signal 调制信号Modulation 调制Modulation index 调制指数Modulation property 调制性质Moving-average filters 移动平均滤波器Multiplexing 多路技术Multiplication property 相乘性质Multiplicities 多样性NNarrowband 窄带Narrowband frequency modulation 窄带频率调制Natural frequency 自然响应频率Natural response 自然响应Negative (degenerative) feedback 负反馈Nonanticipatibe system 不超前系统Noncausal averaging system 非因果平滑系统Nonideal 非理想的 Nonidealfilters 非理想滤波器Nonmalized functions 归一化函数Nonrecursive 非递归Nonrecursive filters 非递归滤波器Nonrecursive linear constant-coefficient difference 非递归线性常系数差分方程 equations Nyquist frequency 奈奎斯特频率Nyquist rate 奈奎斯特率Nyquist stability criterion 奈奎斯特稳定性判据OOdd harmonic 奇次谐波Odd signal 奇信号Open-loop 开环Open-loop frequency response 开环频率响应Open-loop system 开环系统Operational amplifier 运算放大器Orthogonal functions 正交函数Orthogonal signals 正交信号Oscilloscope 示波器Overdamped system 过阻尼系统Oversampling 过采样 Overshoot 超量PParallel interconnection 并联Parallel-form block diagrams 并联型框图Parity check 奇偶校验检查Parseval’s rela tion 帕斯伐尔关系(定理)Partial-fraction expansion 部分分式展开Particular and homogeneous solution 特解和齐次解Passband 通频带Passband edge 通带边缘Passband frequency 通带频率Passband ripple 通带起伏(或波纹) Pendulum 钟摆Percent modulation 调制百分数Periodic 周期的Periodic complex exponentials 周期复指数Periodic convolution 周期卷积Periodic signals 周期信号Periodic square wave 周期方波Periodic square-wave modulating signal 周期方波调制信号Periodic train of impulses 周期冲激串Phase (angle) of complex number 复数相位(角度)Phase lag 相位滞后Phase lead 相位超前Phase margin 相位裕度Phase shift 相移Phase-reversal 相位倒置Phase modulation 相位调制Plant 工厂 Polar form 极坐标形式Poles 极点Pole-zero plot(s) 零极点图Polynomials 多项式Positive (regenerative) feedback 正(再生)反馈Power of signals 信号功率Power-series expansion method 幂级数展开的方法Principal-phase function 主值相位函数Proportional (P) control 比例控制Proportional feedback system 比例反馈系统Proportional-plus-derivative 比例加积分Proportional-plus-derivative feedback 比例加积分反馈Proportional-plus-integral-plus-differential (PID) control 比例,积分,微分控制Pulse-amplitude modulation 脉冲幅度调制Pulse-code modulation 脉冲编码调制Pulse-train carrier 冲激串载波QQuadrature distortion 正交失真Quadrature multiplexing 正交多路复用Quality of circuit 电路品质(因数) RRaised consine frequency response 升余弦频率响应Rational frequency responses 有理型频率响应Rational transform 有理变换RC highpass filter RC 高阶滤波器RC lowpass filter RC 低阶滤波器Real 实数Real exponential signals 实指数信号Real part 实部Rectangular (Cartesian) form 直角(卡笛儿)坐标形式Rectangular pulse 矩形脉冲Rectangular pulse signal 矩形脉冲信号Rectangular window 矩形窗口Recursive (infinite impulse response) filters 递归(无时限脉冲响应)滤波器 Recursive linear constant-coefficient difference equations 递归的线性常系数差分方程Regenerative (positive) feedback 再生(正)反馈Region of comvergence 收敛域right-sided signal 右边信号Rise time 上升时间Root-locus analysis 根轨迹分析(方法) Running sum 动求和SS domain S域Sampled-data feedback systems 采样数据反馈系统Sampled-data systems 采样数据系统Sampling 采样Sampling frequency 采样频率Sampling function 采样函数Sampling oscilloscope 采样示波器Sampling period 采样周期Sampling theorem 采样定理Scaling (homogeneity) property 比例性(齐次性)性质Scaling in z domain z域尺度变换Scrambler 扰频器Second harmonic components 二次谐波分量Second-order 二阶Second-order continuous-time system 二阶连续时间系统Second-order discrete-time system 二阶离散时间系统Second-order systems 二阶系统sequence 序列Series (cascade) interconnection 级联(串联)Sifting property 筛选性质Sinc functions sinc函数Single-sideband 单边带Single-sideband sinusoidal amplitude modulation 单边带正弦幅度调制 Singularity functions 奇异函数Sinusoidal 正弦(信号)Sinusoidal amplitude modulation 正弦幅度调制Sinusoidal carrier 正弦载波Sinusoidal frequency modulation 正弦频率调制Sliding 滑动 Spectral coefficient 频谱系数Spectrum 频谱Speech scrambler 语音加密器S-plane S平面Square wave 方波Stability 稳定性Stabilization of unstable systems 不稳定系统的稳定性(度)Step response 阶跃响应Step-invariant transformation 阶跃响应不定的变换Stopband 阻带 Stopband edge 阻带边缘Stopband frequency 阻带频率Stopband ripple 阻带起伏(或波纹) Stroboscopic effect 频闪响应Summer 加法器Superposition integral 叠加积分Superposition property 叠加性质Superposition sum 叠加和Suspension system 减震系统Symmetric periodic 周期对称Symmetry 对称性Synchronous 同步的Synthesis equation 综合方程System function(s) 系统方程TTable of properties 性质列表Taylor series 泰勒级数Time 时间,时域Time advance property of unilateral z-transform 单边z变换的时间超前性质 Time constants 时间常数Time delay property of unilateral z-transform 单边z变换的时间延迟性质 Time expansion property 时间扩展性质Time invariance 时间变量Time reversal property 时间反转(反褶)性Time scaling property 时间尺度变换性Time shifting property 时移性质Time window 时间窗口Time-division multiplexing (TDM) 时分复用Time-domain 时域Time-domain properties 时域性质Tracking system (s) 跟踪系统Transfer function 转移函数transform pairs 变换对Transformation 变换(变形) Transition band 过渡带Transmodulation (transmultiplexing) 交叉调制Triangular (Barlett) window 三角型(巴特利特)窗口Trigonometric series 三角级数Two-sided signal 双边信号Type l feedback system l 型反馈系统UUint impulse response 单位冲激响应Uint ramp function 单位斜坡函数Undamped natural frequency 无阻尼自然相应Undamped system 无阻尼系统Underdamped systems 欠阻尼系统Undersampling 欠采样Unilateral 单边的Unilateral Laplace transform 单边拉普拉斯变换Unilateral z-transform 单边z变换Unit circle 单位圆Unit delay 单位延迟Unit doublets 单位冲激偶Unit impulse 单位冲激Unit step functions 单位阶跃函数Unit step response 单位阶跃响应Unstable systems 不稳定系统Unwrapped phase 展开的相位特性Upsampling 增采样V Variable 变量WWalsh functions 沃尔什函数Wave 波形 Wavelengths 波长Weighted average 加权平均Wideband 宽带Wideband frequency modulation 宽带频率调制Windowing 加窗zZ domain z域Zero force equalizer 置零均衡器Zero-Input response 零输入响应Zero-Order hold 零阶保持Zeros of Laplace transform 拉普拉斯变换的零点Zero-state response 零状态响应z-transform z变换z-transform pairs z变换对。
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Anton Setzer
Dept. of Computing Science, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK. Tel.: +44 1792 513368, Fax: +44 1792 295651, a.g.setzer@, /~csetzer/
Hale Waihona Puke Here f (n)↓ expresses that f (n) is defined and evalf (n, p) computes, depending on n : N and a proof p : f (n)↓, the value f (n). In the literature, f (·)↓ is often referred to as the accessibility predicate for f . If we define for arguments a, b of f that a ≺ b if and only if the call of f (b) recursively calls f (a), then f (a)↓ if and only if a is in the accessible part of ≺. The approach by Bove/Capretta can be seen as a general method of determining the accessible part of ≺ for a large class of recursively defined functions. If we take the definition of f as it stands, we see that the definitions of S f (·)↓ and evalf refer to each other. f (·)↓ has two constructors defined0 f , definedf corresponding to the two rewrite rules, and we obtain the following introduction and equality rules: defined0 evalf (0, defined0 f : f (0)↓ , f) = 0 , S definedf : (n : N, p : f (n)↓, q : f (evalf (n, p))↓) → f (n + 1)↓ , evalf (n + 1, definedS f (n, p, q )) = evalf (evalf (n, p), q ) . The constructor definedS m, f has arguments n : N, p : f (n)↓, and if f (n) S a proof q : f (m)↓. Then p := definedf (n, p, q ) proves f (n + 1)↓ and we have evalf (n+1, p ) = evalf (m, q ). We observe that definedS f refers to evalf (n, p), so we have to define simultaneously f (·)↓ inductively, while defining evalf recursively. This is an instance of an IIRD, as introduced by Dybjer [Dyb00, Dyb94]. We will see below that such kind of IIRD can be reduced to inductive definitions. Bove and Capretta face the problem that they cannot define a data type of partial recursive functions (unless using impredicative type theory) and therefore cannot deal with partial recursive functions depending on other partial recursive functions as an argument. A simple example would be to define depending on a partial recursive function f : N N (e.g. f as above) g : List(N) List(N) , g (l) : map(f, l) .
Supported by EPSRC grant GR/S30450/01.
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Anton Setzer
This function is constantly zero, but we want to represent it directly in MartinL¨ of type theory, so that we can prove for instance, that it is in fact constantly zero. In order to do this, Bove and Capretta introduce f (·)↓ : N → Set , evalf : (n : N, p : f (n)↓) → N .
1
Introduction
A problem when developing computability theory in Martin-L¨ of type theory is that the function types only contain total functions, therefore partial recursive functions are not first class objects. One approach to overcome this problem has been taken by Bove and Capretta (e.g. [BC05a, BC05b]), who have shown how to represent partial recursive functions by indexed inductive-recursive definitions (IIRD), and in this article we will investigate their approach. In order to illustrate it, we make use of a toy example. We choose a notation which is closer to that used in computability theory. Assume the partial recursive function f : N N defined by f (0) : 0 , f (n + 1) : f (f (n)) .
Here map(f, [n0 , . . . , nk ]) : [f (n0 ), . . . , f (nk )]. In order to define the above directly, we need to define map, depending on an arbitrary partial recursive function f . More complex examples of this kind are discussed in [BC05a]. In this article we will show how to overcome this restriction by introducing a data type of partial recursive functions. This will be based on the closed formulation of IIRD, as developed by P. Dybjer and the author. In order to have that all functions represented by an IIRD correspond directly to a partial recursive function, without using search functions, we will impose restrictions on the set of IIRD used. The data type given in this article will define exactly those restricted IIRD. We will then show that the functions given by those indices are all partial
Abstract. In this article we revisit the approach by Bove and Capretta for formulating partial recursive functions in Martin-L¨ of Type Theory by indexed inductive-recursive definitions. We will show that all inductiverecursive definitions used there can be replaced by inductive definitions. However, this encoding results in an additional technical overhead. In order to obtain directly executable partial recursive functions, we introduce restrictions on the indexed inductive-recursive definitions used. Then we introduce a data type of partial recursive functions. This allows to define higher order partial recursive functions like the map functional, which depend on other partial recursive functions. This data type will be based on the closed formalisation of indexed inductive-recursive definitions introduced by Dybjer and the author. All elements of this data type will represent partial recursive functions, and the set of partial recursive functions will be closed under the standard operations for forming partial recursive functions, and under the total functions. Keywords: Martin-L¨ of type theory, computability theory, recursion theory, Kleene index, Kleene brackets, partial recursive functions, inductive-recursive definitions, indexed induction-recursion.