標(biāo)題: Titlebook: Hypergeometric Orthogonal Polynomials and Their q-Analogues; Roelof Koekoek,Peter A. Lesky,René F. Swarttouw Book 20101st edition Springer [打印本頁] 作者: burgeon 時(shí)間: 2025-3-21 17:38
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues影響因子(影響力)
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues影響因子(影響力)學(xué)科排名
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues網(wǎng)絡(luò)公開度
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues被引頻次
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues被引頻次學(xué)科排名
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues年度引用
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues年度引用學(xué)科排名
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues讀者反饋
書目名稱Hypergeometric Orthogonal Polynomials and Their q-Analogues讀者反饋學(xué)科排名
作者: 思鄉(xiāng)病 時(shí)間: 2025-3-21 22:47
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwlarity order is greater than or equal to five independent of the wavelet decomposition level. For the fifth and sixth levels of wavelet decomposition, it is found that the energy measure always performed better than the bispectral phase entropy measure independent of the regularity of the filter ban作者: Adulterate 時(shí)間: 2025-3-22 02:38
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwctions. The proposed methods are introduced that can be used in different situations such as mobile devices, telemedicine, cloud computing, and finally supporting preventive and supportive treatment of CVDs. Since the performance of proposed algorithms is very important, the time duration, as an add作者: dapper 時(shí)間: 2025-3-22 05:33
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwatory levels of the EEG signals. To reduce the feature space, the Kruskal-Wallis test based feature ranking is applied. The selected features after feature ranking are used in random forest (RF) classifier for classifying normal from healthy subjects and seizure from epileptic subjects using EEG sig作者: 從容 時(shí)間: 2025-3-22 09:57
he within-subject variability evaluation for outliers’ removal and reliable curves’ selection. The patterns are then used for deep learning of discriminative classifiers. The proposed pre-processing method encompasses steps of gait events detection, normalization, outlier detection, and cycles’ sele作者: 煉油廠 時(shí)間: 2025-3-22 15:48 作者: 向外供接觸 時(shí)間: 2025-3-22 18:24 作者: infatuation 時(shí)間: 2025-3-22 23:49
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwte array (FPGA), allowing rapid testing and deployment of hardware systems. Moreover, the novel neuromorphic platform has the potential to efficiently accelerate neural networks as well, which was not possible with the FPGA..In this chapter, we conduct a systematic review of hardware-accelerated neu作者: 改正 時(shí)間: 2025-3-23 02:59
Roelof Koekoek,Peter A. Lesky,René F. Swarttouw of life for such patients or providing better treatment. The framework makes use of various features from various signal signatures with contribution from time domain, frequency domain, and inter-channel statistics. Next, we conducted a comparative analysis of SVM, 3-NN, and ensemble learning with 作者: obstinate 時(shí)間: 2025-3-23 07:03
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwomprises both the biosignal formation path from the biosignal source at the physiological level to biosignal propagation in the body, and the biosignal sensing path from the biosignal transmission in the sensor applied on the body up to its conversion to an electric signal. Namely, the optical senso作者: 制定法律 時(shí)間: 2025-3-23 13:11 作者: armistice 時(shí)間: 2025-3-23 16:54 作者: Isolate 時(shí)間: 2025-3-23 19:42 作者: 朋黨派系 時(shí)間: 2025-3-24 00:07 作者: 鞏固 時(shí)間: 2025-3-24 05:07 作者: guzzle 時(shí)間: 2025-3-24 06:57
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwination level during sleep. The arising biosignals are discussed within the scope of vital physiologic phenomena to foster their understanding and comprehensive analysis.978-3-642-43753-3978-3-642-24843-6Series ISSN 1618-7210 Series E-ISSN 2197-5647 作者: VICT 時(shí)間: 2025-3-24 11:52
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwination level during sleep. The arising biosignals are discussed within the scope of vital physiologic phenomena to foster their understanding and comprehensive analysis.978-3-642-43753-3978-3-642-24843-6Series ISSN 1618-7210 Series E-ISSN 2197-5647 作者: 魔鬼在游行 時(shí)間: 2025-3-24 18:32 作者: SPURN 時(shí)間: 2025-3-24 21:02 作者: gentle 時(shí)間: 2025-3-25 02:06 作者: 煩擾 時(shí)間: 2025-3-25 07:12 作者: Radiation 時(shí)間: 2025-3-25 09:16 作者: 關(guān)心 時(shí)間: 2025-3-25 11:44
Orthogonal Polynomial Solutions in ,(,+,) of Real Difference Equationsnd . with degree[..]=. of polynomials where .∈{1,2,3,…} or .→∞. The polynomials ..(.) and ..(.) are called dual polynomials with respect to the sequences of eigenvalues . and . when ..(..)=..(..) for all .,.=0,1,2,…,..作者: construct 時(shí)間: 2025-3-25 16:46
Orthogonal Polynomial Solutions in ,(,+,) of Complex Difference Equationsse that the coefficients are complex. In that case we look for polynomial solutions ..(.(.+.)) with .∈? and .∈?. The three-term recurrence relations (7.4.1) and (7.4.2) still hold with . replaced by .. Again we set .=.+. with .∈? and .∈?. Then we have . For .=?2. the imaginary part cancels and we have .(.+.)=?..?... Now we define 作者: 即席 時(shí)間: 2025-3-25 21:43 作者: 謙虛的人 時(shí)間: 2025-3-26 03:37
Hypergeometric Orthogonal Polynomials and Their q-Analogues978-3-642-05014-5Series ISSN 1439-7382 Series E-ISSN 2196-9922 作者: 共同確定為確 時(shí)間: 2025-3-26 08:06 作者: Glycogen 時(shí)間: 2025-3-26 09:12
Springer Monographs in Mathematicshttp://image.papertrans.cn/h/image/430637.jpg作者: 手術(shù)刀 時(shí)間: 2025-3-26 15:30 作者: Herd-Immunity 時(shí)間: 2025-3-26 16:59 作者: 假設(shè) 時(shí)間: 2025-3-26 22:09 作者: 時(shí)代錯(cuò)誤 時(shí)間: 2025-3-27 03:20 作者: 毛細(xì)血管 時(shí)間: 2025-3-27 06:57
Orthogonal Polynomial Solutions in ,, of ,-Difference EquationsIn the case that .=0, .>0 and .≠1 we might replace . by .. in (3.2.1) and then replace . by ... Then we have . In this case the eigenvalue problem reads (cf.?(10.1.1)) . for .=0,1,2,…. This can also be written in the symmetric form . for .=0,1,2,…, with . and . The regularity condition (2.3.3) implies that .≠0.作者: 建筑師 時(shí)間: 2025-3-27 12:38
Orthogonal Polynomial Solutions in , of Complex ,-Difference EquationsIt is also possible to obtain real polynomial solutions of the (complex) .-difference equation (12.2.1) . with argument . where .∈??{0} and .,.∈??{0}. By using .=.+., .=.+.. with .,.,.,.∈?, we find that the imaginary part of . equals . This is equal to zero for all .∈? and .∈? if 作者: indecipherable 時(shí)間: 2025-3-27 14:42
https://doi.org/10.1007/978-3-642-05014-5Askey scheme; Eigenvalue; Hypergeometric function; basic hypergeometric functions; differential equation作者: 厭惡 時(shí)間: 2025-3-27 21:12 作者: grotto 時(shí)間: 2025-3-28 00:28
Orthogonal Polynomial Solutions in ,,+,, of Real ,-Difference Equationsdifferent. This implies by using theorem?3.7 that there exists a sequence of dual polynomials. In this case we have . with .=0 and ..=1=... Furthermore we have by using (11.2.2) . if we choose .=?1 in (11.2.1).作者: Arable 時(shí)間: 2025-3-28 04:48
Definitions and Miscellaneous Formulas,s function .(.) is constant between its (countably many) jump points then we have the situation of positive weights .. on a countable subset . of ?. Then the system . is orthogonal on . with respect to these weights as follows: 作者: sorbitol 時(shí)間: 2025-3-28 09:15 作者: motivate 時(shí)間: 2025-3-28 10:26 作者: barium-study 時(shí)間: 2025-3-28 14:47 作者: SHRIK 時(shí)間: 2025-3-28 19:33
Book 20101st edition. The fa- lies of orthogonal polynomials in these two schemes generalize the classical orth- onal polynomials (Jacobi, Laguerre and Hermite polynomials) and they have pr- erties similar to them. In fact, they have properties so similar that I am inclined (f- lowing Andrews & Askey [34]) to call all 作者: Outwit 時(shí)間: 2025-3-29 01:01 作者: defendant 時(shí)間: 2025-3-29 05:52
Polynomial Solutions of Eigenvalue Problems,∈??{?1,0}, .∈? and (.,.)≠(1,0). This class of operators includes the .-derivative operator . (.=0), the difference operator Δ (.=1 and .=1) and also the differentiation operator . as a limit case (.→1 and .=0). In order to avoid the latter limiting process, we introduce the operator . in a second wa作者: 多節(jié) 時(shí)間: 2025-3-29 09:15 作者: 傻瓜 時(shí)間: 2025-3-29 15:25 作者: 歸功于 時(shí)間: 2025-3-29 16:43 作者: 錯(cuò)事 時(shí)間: 2025-3-29 23:27
Hypergeometric Orthogonal Polynomialsgonal polynomials we state the most important properties such as a representation as a hypergeometric function, orthogonality relation(s), the three-term recurrence relation, the second-order differential or difference equation, the forward shift (or degree lowering) and backward shift (or degree ra作者: Apogee 時(shí)間: 2025-3-30 02:30
Orthogonal Polynomial Solutions in ,,+,, of Real ,-Difference Equations2)) . with .∈{1,2,3,…} or .→∞, where . with . where .,.,.,..,..∈?, .>0, .≠1 and .≠0. If the regularity condition (11.2.4) holds all eigenvalues . are different. This implies by using theorem?3.7 that there exists a sequence of dual polynomials. In this case we have . with .=0 and ..=1=... Furthermor作者: 假 時(shí)間: 2025-3-30 07:16 作者: 凹處 時(shí)間: 2025-3-30 09:48
around the world. Moreover, different types of heart diseases are the most common causes of mortality. This chapter, therefore, investigates a cardiac disorders database (ECG) with 17 classes (normal sinus rhythm, the rhythm of the pacemaker, and fifteen arrhythmias) using a novel classification me作者: FRET 時(shí)間: 2025-3-30 14:11
Roelof Koekoek,Peter A. Lesky,René F. Swarttouwpsy. In this chapter two band biorthogonal wavelet filter banks are used for classification of nonseizure and seizure EEG signals, and their classification accuracy has been evaluated. The energy or the bispectral phase entropies of the wavelet subbands can be used to discriminate nonseizure and sei作者: PANG 時(shí)間: 2025-3-30 20:25 作者: 滲入 時(shí)間: 2025-3-31 00:33 作者: amphibian 時(shí)間: 2025-3-31 02:01
on angle variations during locomotion, is to assist diagnosis of knee joint pathologies. These signals are informative but of high dimensionality and high within-subject variability, serious difficulties which are often referred to as the curse of dimensionality. In general, current machine learning
