Analysis of temporal jitter in a copper vapor laser system

Temporal jitter in a magnetic pulse compression based copper vapor laser (CVL) system is analyzed by considering ripple present in the input dc power supply and ripple present in the magnetic core resetting power supply. It is shown that the jitter is a function of the ratio of operating voltage to the designed voltage, percentage ripple, and the total propagation delay of the magnetic pulse compression circuit. Experimental results from a CVL system operating at a repetition rate of 9 kHz are presented.     

Effect of powder treatment on the crystallization behaviour and phase evolution of Al2O3–High ZrO2 nanocomposites

Al₂O₃–ZrO₂ composites containing nominally equal volume fraction of Al₂O₃ and ZrO₂ have been synthesized through combined gel-precipitation technique. Subsequently the gels were subjected to three different post gel processing treatments like ultrasonication, ultrasonication followed by water washing and ultrasonication followed by alcohol washing. It was observed that while in unwashed samples crystallization took place at low temperature, crystallization was delayed in the washed gels. The phase transition of ZrO₂ in the calcined gels followed the sequence; amorphous → cubic ZrO₂ → tetragonal ZrO₂ → monoclinic ZrO₂. On the other hand, phase transition in alumina followed the sequence amorphous to γ-Al₂O₃, the transition taking place at 650 °C. No α-Al₂O₃ could be detected even after calcination at 950 °C. However, all the sintered samples had α-Al₂O₃. In spite of high linear shrinkage (19–21%) during sintering, the sintered sample had density of only above 70% for all the four varieties of the powders. However, in spite of the low sintered density of the pellets, 31% tetragonal zirconia could be retained after sintering at 1400 °C and it reduced to about 16% at 1600 °C.     

Passive Three Dimensional Face Recognition Using Iso-Geodesic Contours and Procrustes Analysis

We introduce a new model for personal recognition based on the 3-D geometry of the face. The model is designed for application scenarios where the acquisition conditions constrain the facial position. The 3-D structure of a facial surface is compactly represented by sets of contours (facial contours) extracted around automatically pinpointed nose tip and inner eye corners. The metric used to decide whether a point on the face belongs to a facial contour is its geodesic distance from a given landmark. Iso-geodesic contours are inherently robust to head pose variations, including in-depth rotations of the face. Since these contours are extracted from rigid parts of the face, the resulting recognition algorithms are insensitive to changes in facial expressions. The facial contours are encoded using innovative pose invariant features, including Procrustean distances defined on pose-invariant curves. The extracted features are combined in a hierarchical manner to create three parallel face recognizers. Inspired by the effectiveness of region ensembles approaches, the three recognizers constructed around the nose tip and inner corners of the eyes are fused both at the feature-level and the match score-level to create a unified face recognition algorithm with boosted performance. The performances of the proposed algorithms are evaluated and compared with other algorithms from the literature on a large public database appropriate for the assumed constrained application scenario.     

The categories L-\mathbf{Top}_{0} and L-\mathbf{Sob} as epireflective hulls

We show that the category \(L\) - \(\mathbf{Top}_{0}\) of \(T_{0}\) - \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}\) of \(L\) -topological spaces and the category \(L\) - \(\mathbf{Sob}\) of sober \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}_{0}\) .     

Combined effect of microwave and ultrasonication treatments on the quality and stability of sugarcane juice during cold storage

The main objective of this study was to investigate the combined effect of microwave (MW) treatment (2450 MHz for 120 s), ultrasonication (US) (24 KHz, 20 °C for 20 min) and combined treatment (MW-US) on the quality and stability of sugarcane juice (SCJ) during 21 days of storage at 4 °C. The effect of the treatments and storage time on physicochemical, bioactive compounds (total phenolic, flavonoids and ascorbic acid content) and microbial analysis of SCJ. No significant (P < 0.05) changes were observed in °Brix, while there was an increase in pH and a decrease in titratable acidity in all treatments. Compared to US and MW, MW-US treatments was more effective in preserving colour attributes, total phenolic and flavonoids contents, ascorbic acid and antioxidant capacities of the SCJ during storage. The results regarding the microbial count indicate that more microbial safety and longer shelf life was achieved by MW-US. MW-US treatment is an effective technology for improving the safety and shelf life of SCJ by minimising quality changes, retaining bioactive compounds and reducing microbial growth during storage.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.     

Ontology based E-learning framework: A personalized,adaptive and context aware model

Multimedia Tools and Applications - Enhancing the degree of learner productivity, one of the major challenges in E-Learning systems, may be catered through effective personalization, adaptivity and...