Electrochemical Impedance Spectroscopic Analysis of Sensitization-Based Solar Cells

Electrochemical impedance spectroscopy (EIS) is a very powerful tool for elucidation of charge transfer and transport processes in sensitization-based solar cells (e.g., dye-sensitized solar cells [DSSCs], quantum dot-sensitized solar cells [QDSSCs], and perovskite solar cells [PSCs]). EIS measures the electrochemical response to small amplitude AC signals over a wide range of frequencies. Analysis of the EIS response provides information about the corresponding parameters of the cells. Here, we review the fundamentals of EIS, charge transport kinetic processes, and equivalent circuit models of sensitization-based solar cells and use these concepts to explain the EIS spectra of DSSCs, QDSSCs, and PSCs. This review will be very useful for understanding the fundamental charge transfer and transport processes in different sensitization-based solar cells and the use of an equivalent circuit model to interpret the observed charge transfer and reactions.     

A metaheuristic analysis of heat transfer rates through porous fins of tapered and step profiles: a comparative study

Neural Computing and Applications - The advanced high-end gadgets and sophisticated machines require effective mechanism of transferring heat away from the sources. In a large number of...     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

CRIM’s content-based audio copy detection system for TRECVID 2009

We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385?h of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with two different audio fingerprints and show that mapping each test frame to the nearest query frame (nearest-neighbor fingerprint) results in robust audio copy detection. The most difficult task in TRECVID 2009 was to detect audio copies using predetermined thresholds computed from 2008 data. We show that the nearest-neighbor fingerprints were robust to even this task and gave actual minimal normalized detection cost rate (NDCR) of around 0.06 for all the transformations. These results are close to those obtained by using the optimal threshold for each transform. This result shows the robustness of the nearest-neighbor fingerprints. These nearest-neighbor fingerprints can be efficiently computed on a graphics processing unit, leading to a very fast search.     

Determination of satellite nadir and azimuth angles for a tilting sensor

The procedure for the determination of satellite nadir and azimuth angles has beenverifiedusing the Coastal Zone Color Scanner (CZCS) geolocation algorithm formulated by Wilson and co-workers in 1981. The error involved in the determination of satellite nadir and azimuth angles for positive and negative tilt of the mirror using the above method is discussed. A simple method to determine nadir and azimuth angles for a tilting mirror is proposed. The nadir and azimuth angles after roll, pitch and yaw of the satellite are presented.     

An efficient clustering scheme using support vector methods

Support vector clustering involves three steps—solving an optimization problem, identification of clusters and tuning of hyper-parameters. In this paper, we introduce a pre-processing step that eliminates data points from the training data that are not crucial for clustering. Pre-processing is efficiently implemented using the R*-tree data structure. Experiments on real-world and synthetic datasets show that pre-processing drastically decreases the run-time of the clustering algorithm. Also, in many cases reduction in the number of support vectors is achieved. Further, we suggest an improvement for the step of identification of clusters.     

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.     

Groundwater potential modelling in a soft rock area using a GIS

A Geographical Information System (GIS) integration tool is proposed to demarcate the groundwater potential zone in a soft rock area using seven hydrogeologic themes: lithology, geomorphology, soil, net recharge, drainage density, slope and surface water bodies. Except for net recharge and slope, the other five themes are derived from remote sensing data. IRS-1B LISS-II data was used for a 631 km² area in Midnapur District, West Bengal, India. While slope was calculated using topographic sheets, net recharge was obtained from annual water table fluctuation data. Each feature of all the thematic maps was evaluated according to its relative importance in the prediction of groundwater potential. The evolved GIS-based model of the study area was found to be in strong agreement with available borehole and pumping test data.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

An ultra-low power high-performance CMOS temperature sensor with an inaccuracy of − 0.3 °C/ + 0.1 °C for aerospace applications

Microsystem Technologies - In the current paper, a new temperature sensor with improved temperature inaccuracy and with very low power consumption has been designed for avionic industry. The...