Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching methodology for professional programmes in ODL: An exploratory study of the Management Programme of IGNOU

ODL system since its inception has gone a long way in terms of imparting education to the learners through different modes of teaching. Many professional and skill based programmes like MBA, MCA, etc. are being offered by various open distance learning institutes throughout the globe. Most of these programmes across the globe are offered online. The reason to this is the increased competition in higher education, which has stressed upon a need to look beyond the traditional mode of teaching. IGNOU also offers such programmes but most of them still have the Self Learning Materials (SLMs) as the main source of imparting education to the learners. Most of the studies, which have been done, are based on web-based learning. Shen etal (2008) have tried to explore the need to enhance skills of Application Software via Web-Enabled ProblemBased Learning and Self-Regulated Learning. This study was basically related to the computer software education in vocational schools. In another study Merrill & Gilbert (2008) stressed upon the need for peer interaction in a problem based learning (PBL) instructional form. The need of the hour is to study the effectiveness of the SLMs used for professional programmes. An effort has been made in this paper to explore the efficacy of SLMs for Management Programme and what measures can be taken up to develop course materials with more of problem based learning instructional forms. The research methodology is based on primary as well as secondary data. A random survey has been conducted. The feedback and suggestions have been taken from the learners who have passed out the programme in 2009. The concentration of this paper is more on the PBL as a teaching methodology for professional programme. This is an exploratory study and will try to highlight the use of SLMs in an innovative form.
Formal Methods for Real-Time Computing

From the Publisher: Focusing on formal methods, this book offers a complete coverage of new developments with emphasis on practical application. Starting with a survey of formal methods for real-time systems, topics include program specification, formal methods, analyzing, and design of real-time systems. The book is essential reading for developers of safety critical systems and researchers in the field.
Trajectory Planning and Simulation of 6R Robot Based on B-splines

The trajectory is planned with cubic uniform B-splines for the 6R industrial robot aimed to assure motion is smooth and trajectory is continuous in this paper.The inverse calculated process of cubic B-spline was analysed.The trajectory is gained in joint space for the PUMA560 robot.The kinematic parameters(trajectory,velocity and acceleration) are simulated and validated by software of CATIA and ADAMS.
Study of Interactive Teaching Mode Based on Game in Chinese as a Foreign Language

The application study of game in teaching is emerging with the development of Information Technology,but how to improve teaching through educational games is very difficulty.The author combined with the current Chinese study heat,constructed game-interactive teaching and learning mode.The purpose is to improve the development of Chinese learning as a foreign language,as well as to give some help for the study of educational games in teaching.
Population Dynamics and Life History of Marine Cladocera in the Maryland Coastal Bays, U.S.A.

ABSTRACT Oghenekaro, E.U. and Chigbu, P., 2019. Population dynamics and life history of marine Cladocera in the Maryland coastal bays, U.S.A. Journal of Coastal Research, 35(6), 1225–1236. Coconut Creek (Florida), ISSN 0749-0208. There is a paucity of information on the biology of Cladocera in coastal lagoons of the NE United States. The influence of abiotic and biotic factors on the abundance, distribution, and reproductive aspects of Cladocera were studied in Maryland coastal lagoons, a shallow, largely polyhaline, lagoonal system, with samples collected at 13 sites from February to December 2012. Pseudevadne tergestina (0–498 individuals [ind.] m–3), Evadne nordmanni (0–246 ind. m–3) and Pleopis polyphemoides (0–138 ind. m–3) were the most abundant species; Evadne spinifera and Podon intermedius were rare. Cladocera were first observed in June at a mean abundance of 37.9 ± 20.7 ind. m–3, which by July increased to 57.9 ± 57.8 ind. m–3. Total Cladocera densities ranged from 0 to 752 ind. m–3, which were lower than densities previously reported in river-dominated estuaries of the United States. Pseudevadne tergestina occurred mainly near the northern inlet; P. polyphemoides and E. nordmanni had more widespread distribution. Brood sizes ranged from 1 to 17 (E. nordmanni) and 1 to 19 (P. tergestina). The decline in Cladocera abundance in late summer when bay anchovy (Anchoa mitchilli) abundance was high suggests a high predation effect by the fish species, although temperature and salinity were also identified as important factors that influenced Cladocera abundance and distribution in the bays.
A Deep Learning Odyssey: An Invitation for Actuaries to Join this Journey.

While Deep Learning has shown remarkable success in the area of unstructured data like image classification, text analysis and speech recognition, there is very little literature on Deep Learning performed on structured/relational data. This investigation also focuses on applying Deep Learning on structured data because actuaries are more comfortable with structured data then unstructured data and so it can be a useful imitative step for other actuaries to apply Deep Learning to solve their own business problems. After extensive investigations, it does seem that Deep Learning has the potential to do well in the area of structured data. More specifically, we review Deep Learning, which is the most promising candidate so far for anomaly detection and for classification. We investigate class imbalance as it is a challenging problem for anomaly detection. In this report, Deep Multilayer Perceptron (MLP) was implemented using Theano in Python and experiments were conducted to explore the effectiveness of hyper-parameters. It was seen that increasing the depth of the neural network helped in detecting minority classes. Cost-sensitive learning technique was also observed to work quite well to deal with the class imbalance. One of the objectives of this project was to see the performance of dropout to regularize and avoid over-fitting in the network. Our conclusion is that while dropout with techniques worked for unstructured data, adding dropout to structured data does not seem to give any improvement. We also situate Deep Learning in its proper strategic context; highlight its applications and limitations as well as on-going developments in Deep Learning.
A Computational Visual Neuroscience Model for Object Recognition

In this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired HMAX_S architecture model for feature extraction and Extreme Learning Machine (ELM) as a classifier. HMAX model is a feed-forward hierarchical structure resembling the ventral pathway in the visual cortex of the brain and ELM is a powerful neural network, which randomly chooses hidden nodes and specifies analytically the single-hidden layer. ELM theories conjecture that this randomness may be true for biological learning in animal brains. It should be noted that the principle reason of using ELM is mainly as a result of its biological structure in order to imitate the biological object recognition system of mammalians and partly for its incredible speed which drastically lessens the runtime. Classification results are reported in Caltech101 dataset, at the focal point with its combinatorial framework serving considerable improvements over latest studies in both classification rate (96.39%) and the low runtime (0.417s).
Analysis of spatiotemporal variability in temperature extremes in the Yellow and Yangtze River basins during 1961–2014 based on high‐density gauge observations

The Yellow and Yangtze River basins are the most important basins in China due to their environment and natural and socioeconomic conditions, and they are highly sensitive to extreme climate events. In this study, we used daily maximum and minimum temperature data gathered from 892 meteorological stations to analyse the spatiotemporal variations in temperature extremes in the Yellow and Yangtze River basins during 1961–2014 and identified correlations with the geographic location and atmospheric circulation patterns. The results indicated that annual mean maximum and minimum temperatures (TX m and TN m), most warm extremes and some cold extremes (minimum of the minimum temperature and maximum temperature, TN n and TX n) generally showed low values in the northwestern region of the Yellow and Yangtze basins, while the diurnal temperature range (DTR) and some cold extremes (ice days, ID0 and frost days, FD0) were generally higher in the northwest of the two basins. TX m, TN m, all warm extremes and some cold extremes (TN n and TX n) showed significant increasing trends in both basins. The increase in the extreme temperature was between 0.17–0.38°C/decade in the Yellow basin, while the interval was 0.15–0.38°C/decade in the Yangtze basin. DTR decreased by −0.07 days/decade in the former and −0.05 days/decade in the latter. Furthermore, warm event days increased by 1.38–3.52 and 1.42–2.65 days/decade in the Yellow and Yangtze basins, respectively. At the same time, the cold event days decreased by −1.90 to −3.47 and −0.20 to −2.70 days/decade in the respective basins. In terms of the spatial patterns, most stations that displayed significant trends were located over the Loess Plateau in the Yellow River basins; Sichuan basin and the lower reaches of the Yangtze River basins. Moreover, almost all of the temperature extremes exhibited the largest trends in spring and winter in the two basins. Most temperature extremes showed strong correlations with longitude and altitude and the Atlantic Multidecadal Oscillation (AMO) displayed significant correlations with almost all of the temperature extremes, except DTR, TN n and TX n; the East Atlantic/Western Russia (EA/WR) was also related to many temperature extremes. Finally, most temperature observations showed abrupt changes in the 1990s, and the timing in the Yangtze River basin was generally later than that in the Yellow River basin.
Fusing DCT and LBP Features for Face Recognition

A novel method by fusing discrete cosine transform(DCT)and local binary pattern(LBP)features is proposed for face recognition in this research.The primary information of the face image was centralized in a small number of DCT coefficients,which were used as the frequency feature of the face.The face was divided regularly into small regions,from which LBP code histograms were computed and concatenated into a spatial global histogram used as descriptor vector of the face.The descriptor vector was dimensionally reduced by PCA.Then,the DCT features and the LBP features were fused posterior to the normalization.The experiments on ORL face database show the improvability of the proposed scheme on the methods using just single DCT or LBP feature for face recognition.
Intimation of Black Hole Attack with AODV Routing Protocol in WSN using Ns-3 Simulator

This paper breaks down the blackhole assault which is one of the conceivable assaults in specially appointed systems. In a blackhole assault, a vindictive node imitates a destination node by sending a ridiculed course answer parcel to a source node that starts a course disclosure. By doing this, the malevolent nodes can deny the activity from the source nodes. With a specific end goal to keep this sort of assault, it is significant to identify the variation from the norm happens amid the assault. In traditional plans, peculiarity discovery is accomplished by characterizing the typical state from static preparing information. Be that as it may, in versatile specially appointed systems where the system topology progressively changes, such static preparing strategy couldn't be utilized effectively.
Multiple Description Coding for Region of Interest Based on SPIHT

A new multiple description SPIHT coding for region of interest(ROI) is proposed to combat packet loss.The method codes the SPIHT bit stream into multiple descriptions,each with a variable number of fully coded original trees and partially coded redundant trees.After reordering the zero trees of wavelet according to their importance,the method assign different coding rates of(original) trees and redundant trees to each description.The more important parts of each tree are sent in more description than the less important parts,and in order to further improve the error resilience of ROI,more redundant trees are assigned to ROI than non-ROI.The relationship(among) zero trees,redundant trees and their coding rates are analyzed,and the algorithm of sorting process and assigning strategy of redundant trees are also presented in detail.Simulation results show that the method can further improve the reconstructed quality of ROI in the case of packet loss compared to other ROI coding methods.
Global Ionospheric Metal Ion Transport With SAMI3

The first global simulation of metal ion dynamics in both the E and F regions is presented. The Naval Research Laboratory ionosphere/plasmasphere model SAMI3 has been upgraded to include the metal ions Mg+ and Fe+ with full ion transport (but not deposition or chemistry). The combination of the neutral wind and global electric field dramatically redistributes the metal ions in the ionosphere. High‐density layering can occur in the low to middle latitude, as well as the polar cap, E regions; this can be a significant factor in the development of sporadic E. Both the wind and electric field are important in the low‐ to middle‐latitude ionosphere, while the electric field is more important than the wind at high latitudes. Additionally, metal ions can be lifted to high altitudes ( ≳ 600 km) in the low‐ to middle‐latitude ionosphere.
Sub-pixel center detection algorithm for cross laser image

High-precision detection of laser spot center is the critical technique in computer vision detection field.In this paper,we detect the center of cross laser images obtained from two cameras.To simplify the following processing and eliminate the interference in the background,the image is segmented to binary image with maximum variance between clusters.Then,the center dots on cross laser lines are detected as the samples,and processed to get the sub-pixel center of cross laser with least square fitting.Finally,we analyze the result and get rid of the centers generated because of arris on the object surface.Experiment results show the processing speed of our method described in this paper can meet the need of real-time system,which has good robust and high location precision.
Co-Operative Resource Allocation: Building an Open Cloud Market Using Shared Infrastructure

In this paper we present DRIVE, a distributed service-based system designed to facilitate an open economic market for federating Cloud providers. To address the challenges associated with market ownership and operation we propose the use of a co-operative (co-op) infrastructure in which the services that make up DRIVE are hosted across participants’ resources. To prevent malicious behavior we use cryptographic, secure and privacy preserving allocation protocols as a means of establishing trust in the allocation infrastructure. We investigate through simulation the effect of different strategies, pricing functions, and penalty models on allocation performance and revenue, and show that the overhead of running DRIVE's services on commodity infrastructure is modest.
An Algorithm of Real-Time Paper Currency Recongnition

Paper currency sorting is an important operation in the bank, and the technique for Chinese paper currency sortor is still a lacuna in China A real time paper currency recognition method is proposed in this paper, which extracts the directional block characteristics and the size information of a currency image as the recognition features A modified Kohonen network is constructed for the recognition of Chinese paper currency The experiment reveals that this method achieves a high recognition rate and fast recognition speed The proposed method has been applied to a practical sorting system
Classification Tree Analysis (Gini-Index) Smoke Detection using Himawari_8 Satellite Data Over Sumatera-Borneo Maritime Continent Sout East Asia

Classification tree analysis (CTA) automatic smoke detection was proposed using Himawari_8 Satellite data over Sumatera and Borneo Island Maritime Continent. Day Natural color and aerosol RGB composite were used to make Region of Interest (ROI) sampling of Cumulonimbus (Cb) top, low-mid cloud, smoke, bare soil, cirrus cloud, vegetation, and water. CTA - Gini index supervised classification being constructed with two different band collection as input. The result shows that CTA model 2, using 21 bands collection as input, has better overall accuracy value (about 0.75). Then the CTA model 1 only has an overall accuracy of about 0.63. The future research is still open in comparing the different impurity methods of CTA model, ensemble model of smoke detection using several CTA output models, and also a diurnal or seasonal variation of smoke using CTA model detection.
A current tuneable fully differential transconductor dedicated for filtering applications

In this paper, a current tuneable fully differential transconductor cell (gm-C) dedicated for filtering applications is proposed. The architecture used for this new cell is based on a cross-coupled differential pairs input stage with folded cascode output stage in order to realise a high DC-gain integrator. This approach is preferred to the use of the negative resistance load (NRL) approach, since it is more stable and does not require another NRL circuit tuning when the overall transconductance of the cell is modified. Realisation techniques for a gm-C high-order continuous-time filter are also discussed which allow full programmability of the centre frequency and the quality factor of the filter. Finally, a design example of a 4th-order bandpass filter is presented with 3.3 V 0.35 /spl mu/m CMOS technology available through the Canadian Microelectronics Corporation (CMC).
Characterising sand channel from seismic data using linear programming (l1-norm) sparse spike inversion technique: a case study from offshore, Canada

In this study, a linear programming (l1-norm) sparse spike inversion (LPSSI) technique is used to estimate acoustic impedance distribution in the subsurface of the Blackfoot Field, Alberta, Canada. The aim of study is to determine high-resolution subsurface rock properties from the low-resolution seismic data and characterise the clastic Glauconitic channel. There are many traditional post-stack seismic inversion techniques available to estimate rock properties from seismic data, but LPSSI is a relatively simple and quick to compute subsurface model that can be used for qualitative as well as quantitative interpretation. The technique is applied in two steps; first, composite traces near to well locations are extracted and inverted for acoustic impedance, and comparison with well log impedance is used to optimise the LPSSI parameters. Analysis of the composite traces indicates that the algorithm has good performance with high correlation (0.97). In the second step, LPSSI is applied to the Blackfoot seismic data to estimate the distribution of acoustic impedance in the subsurface. Analysis of inverted acoustic impedance shows a low impedance anomaly ranging from 6500 to 8500 m/s*g/cc at the 1060–1075 ms time interval, which is characterised as a clastic Glauconitic sand channel. Thereafter, to confirm the sand channel, another important rock property, porosity, is estimated in the inter-well region using multi-attribute analysis. Analysis of the porosity shows the presence of a high porosity (15–22%) zone in the 1060–1075 ms time interval which coincides with the low impedance zone and confirms the presence of the sand channel.
Research of Feature Selection of Red Cell and White Cell of Urinary Sediment Images Based on Genetic Algorithm Embedded with Multi-criteria

Based on the feature selection issue of red cells and white cells, one new method (NMGA) for it was proposed. That is: according to the feature selection about red cells and white cells, the modified genetic algorithm was adopted to deal with it. The modified genetic algorithm used gene-fixing technology (gradually fix the features during several generations), and combined the Niche technology together to enhance the performance of the genetic algorithm. Besides, voting mechanism was introduced with multi-criteria during each evolution operation. The effect of these experiments show that this algorithm performs better than simple genetic algorithm (SGA), the features are reduced by this algorithm, and the complexity of the classifier is reduced apparently.
High Efficiency DC-DC Converter for EV Battery Charger Using Hybrid Resonant and PWM Technique

The battery charger plays an important role in the development of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). This thesis focuses on the DC-DC converter for high voltage battery charger and is divided into four chapters. The background related to EV battery charger is introduced, and the topologies of isolated DCDC converter possibly applied in battery charge are sketched in Chapter 1. Since the EV battery charger is high voltage high power, the phase-shifted full bridge and LLC converters, which are popularly used in high power applications, are discussed in detail in Chapter 2. They are generally considered as high efficiency, high power density and high reliability, but their prominent features are also limited in certain range of operation. To make full use of the advantages and to avoid the limitation of the phase-shifted full bridge and LLC converters, a novel hybrid resonant and PWM converter combining resonant LLC half-bridge and phase shifted full-bridge topology is proposed and is described in Chapter 3. The converter achieves high efficiency and true soft switching for the entire operation range, which is very important for high voltage EV battery charger application. A 3.4 kW hardware prototype has been designed, implemented and tested to verify that the proposed hybrid converter truly avoids the disadvantages of LLC and phase-shifted full bridge converters while maintaining their advantages. In this proposed hybrid converter, the utilization efficiency of the auxiliary transformer is not that ideal. When the duty cycle is large, LLC converter charges one of the capacitors but the energy stored in the capacitor has no chance to be transferred to the output, resulting in the low utilization