Non Identical Duplicate Video Detection using SIFT Method

Non-Identical Duplicate video detection is a challenging research problem. Non-Identical Duplicate video are a pair of videos that are not exactly identical but are almost similar.In this paper, we evaluate two methods - Keyframe -based and Tomography-based methods to determine the Non-Identical Duplicate videos. These two methods make use of the existing scale based shift invariant (SIFT) method to find the match between the key frames in first method, and the cross-sections through the temporal axis of the videos in second method.We provide extensive experimental results and the analysis of accuracy and efficiency of the above two methods on a data set of Non- Identical Duplicate video-pair.

Ultrasonic guided wave sensing properties of PVDF thin film with inter digital electrodes

Ultrasonic strain sensing performance of the large area PVDF with Inter Digital Electrodes (IDE) is studied in this work. Procedure to obtain IDE on a beta-phase PVDF is explained. PVDF film with IDE is bonded on a plate structure and is characterized for its directional sensitivity at different frequencies. Guided waves are induced on the IDE-PVDF sensor from different directions by placing a piezoelectric wafer actuator at different angles. Strain induced on the IDE-PVDF sensor by the guided waves in estimated by using a Laser Doppler Vibrometer (LDV) and a wave propagation model. Using measured voltage response from IDE-PVDF sensor and the strain measurements from LDV the piezoelectric coefficient is estimated in various directions. The variation of 11 e at different angles shows directional sensitivity of the IDE-PVDF sensor to the incident guided waves. The present study provides an effective technique to characterize thin film piezoelectric sensors for ultrasonic strain sensing at very high frequencies of 200 kHz. Often frequency of the guided wave is changed to alter the wavelength to interrogate damages of different sizes in Structural Health Monitoring (SHM) applications. The unique property of directional sensitivity combined with frequency tunability makes the IDE-PVDF sensor most suitable for SHM of structures.

Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.