Decomposition of color wavelet with higher order statistical texture and convolutional neural network features set based classification of colorectal polyps from video endoscopy

Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0o, 45o, 90o, 135o). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases

Electrical switching studies on Ge-​Te-​Tl chalcogenide glasses: Effect of thallium on the composition dependence of switching voltages

Bulk Ge17Te83-​xTlx glasses (0 ≤ x ≤ 13)​, have been found to exhibit memory type elec. switching. The switching voltages (also known as threshold voltage - Vth) of Ge17Te83-​xTlx glasses decrease with increasing Tl content. The rate of decrease of Vth is greater at lower concns. and Vth falls at a slower rate for higher Tl concns. (x ≥ 6)​. The addn. of Tl to the Ge-​Te network fragments the covalent network and introduces ionic nature to it; the redn. in network connectivity decreases switching voltages with Tl content. The decrease in the glass transition temps. of Ge17Te83-​xTlx glasses with increasing Tl concn. supports the idea of decrease in network connectivity with Tl addn. The more metallic nature of Tl also contributes to the obsd. redn. in the switching voltages of Ge17Te83-​xTlx glasses with Tl content. Further, there is an interesting correlation seen between the threshold voltage Vth and the av. bond energy, as a function of Tl content. The switching voltages of Ge17Te83-​xTlx glasses have been found to decrease with sample thickness almost linearly. The set-​reset studies indicate that the Ge17Te81Tl2 sample can be switched for >10 cycles, whereas other glasses could not be reset beyond 2 switching cycles

Optimal Prefix and Suffix Queries on Texts

International audienceIn this paper, we study a restricted version of the position restricted pattern matching problem introduced and studied by Makinen and Navarro [V. Makinen, G. Navarro, Position-restricted substring searching, in: J.R. Correa, A. Hevia, M.A. Kiwi (Eds.), LATIN, in: Lecture Notes in Computer Science, vol. 3887, Springer, 2006, pp. 703-714]. In the problem handled in this paper, we are interested in those occurrences of the pattern that lies in a suffix or in a prefix of the given text. We achieve optimal query time for our problem against a data structure which is an extension of the classic suffix tree data structure. The time and space complexity of the data structure is dominated by that of the suffix tree. Notably, the (best) algorithm by Makinen and Navarro, if applied to our problem, gives sub-optimal query time and the corresponding data structure also requires more time and space