Resonant Electro-Optic Frequency Comb

High speed optical telecommunication is enabled by wavelength division multiplexing, whereby hundreds of individually stabilized lasers encode the information within a single mode optical fiber. In the seek for larger bandwidth the optical power sent into the fiber is limited by optical non-linearities within the fiber and energy consumption of the light sources starts to become a significant cost factor. Optical frequency combs have been suggested to remedy this problem by generating multiple laser lines within a monolithic device, their current stability and coherence lets them operate only in small parameter ranges. Here we show that a broadband frequency comb realized through the electro-optic effect within a high quality whispering gallery mode resonator can operate at low microwave and optical powers. Contrary to the usual third order Kerr non-linear optical frequency combs we rely on the second order non-linear effect which is much more efficient. Our result uses a fixed microwave signal which is mixed with an optical pump signal to generate a coherent frequency comb with a precisely determined carrier separation. The resonant enhancement enables us to operate with microwave powers three order magnitude smaller than in commercially available devices. We can expect the implementation into the next generation long distance telecommunication which relies on coherent emission and detection schemes to allow for operation with higher optical powers and at reduced cost

Universal consistency of the kk-NN rule in metric spaces and Nagata dimension. II

We continue to investigate the $k$ nearest neighbour learning rule in separable metric spaces. Thanks to the results of C\'erou and Guyader (2006) and Preiss (1983), this rule is known to be universally consistent in every metric space $X$ that is sigma-finite dimensional in the sense of Nagata. Here we show that the rule is strongly universally consistent in such spaces in the absence of ties. Under the tie-breaking strategy applied by Devroye, Gy\"{o}rfi, Krzy\.{z}ak, and Lugosi (1994) in the Euclidean setting, we manage to show the strong universal consistency in non-Archimedian metric spaces (that is, those of Nagata dimension zero). Combining the theorem of C\'erou and Guyader with results of Assouad and Quentin de Gromard (2006), one deduces that the $k$-NN rule is universally consistent in metric spaces having finite dimension in the sense of de Groot. In particular, the $k$-NN rule is universally consistent in the Heisenberg group which is not sigma-finite dimensional in the sense of Nagata as follows from an example independently constructed by Kor\'anyi and Reimann (1995) and Sawyer and Wheeden (1992).Comment: Latex 2e, 15 page

Damage and Degradation Study of FRP Composites

The present experimental study aims at assessing the different effects of the varying environments on the mechanical properties of FRP composites. The mechanical performance of a composite material is decisively controlled by the state of fiber-matrix interface . Its properties influence the integrity of composite behavior because of its role in transferring stress between the fiber and the matrix. The factors affecting the interface are too complex to be precisely concluded. Fibrous composites are increasingly being used in many casual as well as critical applications owing to various desirable properties including high specific strength, high specific stiffness and controlled anisotropy. But unfortunately polymeric composites are susceptible to heat and moisture when operating in changing environmental conditions. Samples of several Glass-Epoxy composites were manufactured using the traditional hand layup method where the stacking of the plies were alternate and the weight fraction of fiber and matrix was kept at 40-60%.Specimens were cut according to the ASTM D 2344-84(1989) standards. Some of the specimens were kept in the As-cured condition so as to obtain the base properties. Experimental studies have been carried out to study the effects of thermal ageing, liquid nitrogen temperature, thermal shocks, sea and distilled water. Also, tests have been performed to study the effect of ultraviolet rays and microwave conditions on the mechanical behavior of Glass-epoxy composites. The specimens were divided into groups. One group was subjected to cryogenic conditions at -750C for 3 hours and 6 hours. Another group was subjected to elevated temperature at +750C for 5 hours and 10 hours. A separate group samples were immersed in the two mediums separately namely sea water , distilled water at their boiling temperatures .Of the remaining samples a group of samples were kept in a microwave oven for 60 , 90 and 120 secs. whereas the other part of it was kept in a ultraviolet chamber for a period of 100 hrs. Thermal shocks of two types, up-cycle (lower to higher temperature immersion) and down-cycle (higher to lower temperature immersion) were applied The aged samples were subjected to 3-point short beam shear tests. The tests were performed at room temperature with 1 mm/min and 500 mm/min crosshead speeds. The weakening effects were sensitive to loading rate. The ILSS(shear strength) values were then compared with the base values of as cured specimen SEM analysis was done to ascertain the mode of failure

Performance Evaluation of Greenhouse Having Passive or Active Heating in Different Climatic Zones of India

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Performance Evaluation of Greenhouse Having Passive or Active Heating in Different Climatic Zones of India. Manuscript EE 06 011. Vol. IX. May, 2007

A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks

Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system

Evolution to Big Data Analytics Techniques and Challenging Issues in Data Mining With Big Data

Big Data is another term used to recognize the datasets that because of their enormous size and multifaceted nature. Big Data are currently quickly growing in all science and engineering domains, including physical, natural and biomedical sciences. Big Data mining is the capacity of separating helpful information from these huge datasets or floods of data, that because of its volume, changeability, and velocity, it was impractical before to do it. The Big Data challenge is getting one of the most energizing open doors for the following years. In the present time of digitization, we take a shot at the variety of data. Colossal measure of data will be prepared by Google, Microsoft and Amazon. Regular routine these organization prepared huge measure of data. In such way we have to require some approach to adjust the innovation in with the end goal that every one of the data will be prepared adequately. Big Data is a developing concept that depicts imaginative systems and innovations to break down enormous volume of complex datasets that are exponentially produced from different sources and with different rates. Data mining procedures are giving extraordinary guide in the region of Big Data examination, since managing Big Data are big difficulties for the applications. Big Data examination is the capacity of removing valuable information from such colossal datasets. This paper exhibits a writing survey that incorporate the significance, difficulties and applications of Big Data in different fields and the various methodologies utilized for Big Data Analysis utilizing Data Mining procedures. The discoveries of this audit give important information to the analysts about the primary patterns in research and examination of Big Data utilizing diverse investigation domains. This examination paper incorporates the information about what is big data, Data mining, Data mining with big data, Challenging issues and its related work

Exploring sexual dimorphism in canines of contemporary North Indian populations using machine learning algorithms

Objectives: Dentition is considered an excellent source for biological profiling in contemporary and archaeological populations with forensic anthropological, genetic, and dental perspectives. Dental dimorphism is well established and can be reflected in measurements and indices. The goal of this study is to use the discriminant function and receiver operating curve analysis to estimate sex and to make useful classification models for estimating sex based on the canine field of the mandibular and maxillary jaws. Materials and Methods: A total of six variables of the maxillary and mandibular canines (width of left and right canines and intercanine distances) were measured on 200 adult subjects of the contemporary Haryanvi population (M/F 100:100, 18–60 years) using digital sliding calipers and indices calculated. A discriminant function and receiver operating characteristic (ROC) analysis was applied on collected data using SPSS 21.0. Results: All variables were sexually dimorphic (p < 0.001). In stepwise analysis, maxillary intercanine distance provided an accuracy of 84%. In ROC analysis, maxillary intercanine distance emerged as an excellent variable with the maximum area under the curve (AUC) and the highest sexing accuracy (86.0%). Discussion: We proved the feasibility of employing machine learning to improve sex prediction. Probable causes of discrepancies in sex classification using different models are discussed. When applying models based on only canine teeth (without attachment to the tooth socket), forensic anthropologists and archaeologists should be more careful

Exploring sexual dimorphism in canines of contemporary North Indian populations using machine learning algorithms

Objectives: Dentition is considered an excellent source for biological profiling in contemporary and archaeological populations with forensic anthropological, genetic, and dental perspectives. Dental dimorphism is well established and can be reflected in measurements and indices. The goal of this study is to use the discriminant function and receiver operating curve analysis to estimate sex and to make useful classification models for estimating sex based on the canine field of the mandibular and maxillary jaws. Materials and Methods: A total of six variables of the maxillary and mandibular canines (width of left and right canines and intercanine distances) were measured on 200 adult subjects of the contemporary Haryanvi population (M/F 100:100, 18–60 years) using digital sliding calipers and indices calculated. A discriminant function and receiver operating characteristic (ROC) analysis was applied on collected data using SPSS 21.0. Results: All variables were sexually dimorphic (p < 0.001). In stepwise analysis, maxillary intercanine distance provided an accuracy of 84%. In ROC analysis, maxillary intercanine distance emerged as an excellent variable with the maximum area under the curve (AUC) and the highest sexing accuracy (86.0%). Discussion: We proved the feasibility of employing machine learning to improve sex prediction. Probable causes of discrepancies in sex classification using different models are discussed. When applying models based on only canine teeth (without attachment to the tooth socket), forensic anthropologists and archaeologists should be more careful