Limitations to Realize Quantum Zeno Effect in Beam Splitter Array -- a Monte Carlo Wavefunction Analysis

Effects of non-ideal optical components in realizing quantum Zeno effect in an all-optical setup are analyzed. Beam splitters are the important components in this experimental configuration. Nonuniform transmission coefficient, photon absorption and thermal noise are considered. Numerical simulation of the experiment is performed using the Monte Carlo wavefunction method. It is argued that there is an optimal number of beam splitters to be used for maximizing the expected output in the experiment.Comment: To be published in the Journal of the Physical Society of Japa

Effective Continued Fraction Dimension versus Effective Hausdorff Dimension of Reals

We establish that constructive continued fraction dimension originally defined using $s$-gales is robust, but surprisingly, that the effective continued fraction dimension and effective (base-$b$) Hausdorff dimension of the same real can be unequal in general. We initially provide an equivalent characterization of continued fraction dimension using Kolmogorov complexity. In the process, we construct an optimal lower semi-computable $s$-gale for continued fractions. We also prove new bounds on the Lebesgue measure of continued fraction cylinders, which may be of independent interest. We apply these bounds to reveal an unexpected behavior of continued fraction dimension. It is known that feasible dimension is invariant with respect to base conversion. We also know that Martin-L\"of randomness and computable randomness are invariant not only with respect to base conversion, but also with respect to the continued fraction representation. In contrast, for any $0 < \varepsilon < 0.5$, we prove the existence of a real whose effective Hausdorff dimension is less than $\varepsilon$, but whose effective continued fraction dimension is greater than or equal to $0.5$. This phenomenon is related to the ``non-faithfulness'' of certain families of covers, investigated by Peres and Torbin and by Albeverio, Ivanenko, Lebid and Torbin. We also establish that for any real, the constructive Hausdorff dimension is at most its effective continued fraction dimension

Machine Tool Crack Detection using Operational Modal Characteristics

Machine tool crack detection is to detect any damages in the tool during machining and to predict the breakage of the tool by identifying the appearance of small cracks in the tool during machining. Machine tool state monitoring is critical for controlling the work piece quality and production continuity in the case of mass production. For assessing tool breakage, machine tool vibration monitoring is a suitable means. The results obtained in FE analysis are validated with experimental data. This work gives a methodology for online crack detection in machine tools. This work presents an experimental technique for measuring modal parameters of a rectangular aluminium plate with cantilever boundary condition using only the output data, with the intention to apply the technique to machine tools. Operational modal analysis is be used for damage detection by determining the depth as well as the position of tool cracks. The results obtained are validated with finite element analysis. To locate the crack, 3D graphs of the normalized frequency in terms of the crack depth and location are plotted. The intersection of these contours gives crack location and crack depth. Out of several case studies conducted the results of one of the case study is presented to demonstrate the applicability and efficiency of the method suggested

Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods

The "Cardio Vascular Diseases (CVDs)" had already attained worrisome proportions in both advanced and emerging nations in recent times. Physically inactive behaviors, altered eating, and occupational routines, and reduced daily fitness were all recognized as crucial contextual elements, in addition to genetics. Considering CVDs have such a significant morbidity and mortality, accurate and early diagnosis of cardiac disease by "ElectroCardioGram (ECG)" allows clinicians to decide suitable therapy for a multitude of cardiovascular disorders. The interpretation of ECG signal is an important bio-signal processing area that involves the application of computer science and engineering to detect and visualize the functional status of the heart. Therefore, in the present work, a detailed study on ECG signals denoising and abnormalities detection using different techniques were performed. Annoying distortions and noisy particles are common in ECG signals. The "Biased Finite Impulse Response (BFIR)" preprocessing filtering is employed in this research to eliminate the noises in the raw ECG signals. The "Nonlinear-Hamilton" segmentation method is employed to segment the 'R' peak signals.&nbsp; To decrease the extraneous features included in the segmented ECG data, the innovative "Enhanced Principal Component Analysis (EPCA)" was applied for feature extraction. A unique "Enhanced version of the Support Vector Machine (ESVM)" framework with a "Weighting Kernel" based technique is proposed for classifying the ECG data. The 'Q', 'R', and 'S' waves in the given ECG data will be identified by this framework, allowing it to characterize the cardiac rhythm. The evaluation metrics of the EPCA-ESVM proposed method is comparatively analyzed with our previous approach EPSO. To estimate the results for the dataset from MIT-BIH it was experimented with by the EPSO and the EPCA-ESVM methods focused upon different parameters such as Accuracy, F1-score, etc. The final findings of the EPCA-ESVM method were good than the EPSO method in which the accuracy is higher even though unbalanced data were present

Survey on Encryption Techniques in Delay and Disruption Tolerant Network

Delay and disruption tolerant network (DTN) is used for long area communication in computer network, where there is no direct connection between the sender and receiver and there was no internet facility. Delay tolerant network generally perform store and forward techniques as a result intermediate node can view the message, the possible solution is using encryption techniques to protect the message. Starting stages of DTN RSA, DES, 3DES encryption algorithms are used but now a day\u27s attribute based encryption (ABE) techniques are used. Attribute based encryption technique can be classified in to two, key policy attribute based encryption (KPABE) and cipher policy attribute based encryption (CPABE). In this paper we perform a categorized survey on different encryption techniques presents in delay tolerant networks. This categorized survey is very helpful for researchers to propose modified encryption techniques. Finally the paper compares the performance and effectiveness of different encryption algorithms

Enlightening the concept of Snayugatavata and its management through Ayurvedic aspect in correlation to Tennis Elbow

In Ayurveda, Snayugata Vata is explained under the concept of Vatavyadhi. The Vata Dosha vitiation occurs which settles down in the Snayu of the Sharira. In Snayugata Vata, there is the Shoola, Kampa, Stambha in the Kurpara Sandhi. Tennis elbow is the condition in which there is a torn tendon of the elbow joint due to excessive work load which leads to the pain in elbow joint and loss of strength in holding the things. The incidence rate of tennis elbow is 4.5 per 1000 persons in a year. It not only affects the tennis players but also the other persons those who are working with the heavy tools and even play without proper techniques. According to the Lakshanas, we can correlate it to Snayugatavata in Ayurveda and tennis elbow in Modern science. According to different Acharyas Snehana Karma, Swedana Karma, Upanaha, Bandhana, Agnikarma are explained under the treatment modalities for Snayugata Vata which is cost effective and thereby beneficial for the welfare of the mankind

Stock Price Prediction using Bat Algorithm

There is no such thing as a safe path of investment in the stock market because it is highly unpredictable, which has been a major concern of investors globally. As a result, stock market or stock price prediction has been a hot topic for scholars and researchers and a popular topic for investors worldwide