Managing Large Enrollment Courses in Hybrid Instruction Mode

While Indian education system is still debating on values of Gurukal system to imperial western education; the world moves on to the hybrid teaching learning system. Though the western world started hybrid teaching in early 1990’s, it took us good 30 years to follow the Westroes. Even when we have initiated the process in few institutions there is much to understand and do before we actually get to see the success of Hybrid online teaching and learning. This paper set to study the hitches and glitches in Hybrid Instruction system of teaching and learning for large enrollment courses. This new instructional methodology ask for redesigning the entire traditional teaching and learning practices where motivation of the felicitator is of prime concern that whether they are motivated enough to come out of their comfort zones

A novel cost-effective Pressure Sensor based Smart Car park system

With the increase in number of people using vehicles for transportation since last decade, traffic congestion is a major problem that requires to be solved effectively. Smart car park system is considered as one of the strategic solutions to this problem, which involves use of sensors to collect data. This paper proposes a novel low-cost smart car monitoring system to detect number of incoming and outgoing cars in and/or out of the car park using pressure sensors. This system also provides the data for the number of spaces available in the car park. Additionally, the paper also demonstrates the algorithm used to process the data obtained by the sensors to use it as a useful information

Evaluation of Data Sets and Algorithms for Brain Tumor Detection Using MRI Images: A Python-Based Approach

This study is to evaluate the work of the data sets and major algorithms that are in-volved in the brain tumor detection system using the MRI image with the help of the python concept. So basically, is an easy manner if we require to define the phenomena of the brain tumor it could be the abnormal condition that causes the problem of cancer, here the abnormal condition refer to the growth in cell body in not a very suitable way for the brain tissue. In the respective paper, we proposed an algorithm to segment brain tumor from 2D Magnetic Resonance Image of the brain by a CNN. When this algorithm is applied to MRI images, a brain tumor diagnosis can be made more quickly and accu-rately, which makes it easier to give patients treatment. These predictions enable the radiologist to make quick decisions as well. In the proposed work, the performance of a self-defined Convolution Neural Network (CNN) is evaluated. For the purpose of faster and efficient accuracy, we will implement the proposed method using the “TenserFlow” and “keras” in “Python

Design and Implementation of Low‑Cost Real‑Time Energy Logger for Industrial and Home Applications

With the significant increase in energy demands in the last decade, the issues of unnecessaryenergy usage have increased rapidly. Therefore, there is an immediate need to providea cheap and easily accessible monitoring tool for the energy consumed by an applianceused in homes and industries. Instead of monitoring the total power consumption of thehouses and/or industries, it is useful to monitor the power consumption of the individualappliance, which in turn, helps in saving the overall energy usage and thereby makes itcost-effective. This paper presents a cost-efficient design and implementation of a monitoringsystem that can precisely measure the current and voltage of each appliance. Thedesign provides tracking of device activity in a real-time environment for the industries andhelps in adopting to the green initiative. The design comprises of Arduino based microcontrollerand Raspberry Pi, that performs precise measurements of current and voltageof the device, followed by measuring the power consumed by the device. This paper presentstwo different system designs, one for the single-phase measurements and the otherfor the DC measurements. The single-phase measurement device comprises of 10-bit ADCwhereas, the 24 V DC measurement device comprises of a 12-bit ADC, which provideshigher measurement accuracy compared to other systems available in the market. Theimplemented design uses the EmonCMS web application to accumulate and envision themonitored data. It provides a flexible and user-friendly solution to monitor the measureddata easily on any android or iOS devices

Device Identification Using Discrete Wavelet Transform

This paper investigates the effectiveness of employing measured hardware features mapped into the frequency domain for devices identification. The technique is to utilize Discrete Wavelet Transform (DWT) coefficients as distinguishing features. The DWT coefficients address the degree of relationship between the investigated features and the wavelet function at different occurrences of time. Therefore, DWT coefficients carry useful temporal information about the transient activity of the investigated wavelet features. We study the impacts of utilizing different wavelet functions (Coiflets, Haar and Symlets) on the performance of the device identification system. This system yields 92.5 % of accuracy using Sym6 wavelet. A comparison is made of the accuracy of wavelet features and raw features with standard classifiers

Spektralna analiza poopćene trokutaste i Welchove prozorske funkcije korištenjem frakcijske Fourierove transformacije

The paper presents a new closed-form expression for the fractional Fourier transform of generalized Triangular and Welch window functions. Fractional Fourier Transform (FrFT) is a parameterized transform having an adjustable transform parameter which makes it more flexible and superior over ordinary Fourier transform in several applications. It is an important tool used in signal processing for spectral analysis. The analysis of generalized Triangular and Welch window functions in fractional Fourier domain establishes a direct relationship between their FrFTs and fractional angle. Based on the mathematical model obtained, it is observed that adjustable spectral parameters of these functions can be obtained by modifying the fractional angle. The various values of spectral parameters such as half main-lobe width, side lobe fall-off rate and maximum side-lobe level with change in order of fractional Fourier transform are also obtained for these functions.U radu je prikazan novi izraz za zatvoreni oblik frakcijske Fourierove transformacije poopćene trokutaste i Welchove prozorske funkcije. Frakcijska Fourierova transformacija (FrFT) parametrizirana je transformacija s podesivim parametrom transformacije koja je u određenim primjenama fleksibilnija i superiornija u odnosu na uobičajenu Fourierovu transformaciju. Ističe se kao važan alat u obradi signala i spektralnoj analizi. Analiza poopćene trokutaste i Welchove prozorske funkcije u području frkacijske Fourierove transformacije uspostavlja izravni odnos između FrFT-a i frakcijskog kuta. Koristeći dobiveni matematički model, uočeno je da se podesivi spektralni parametri ovih funkcija mogu izvesti mijenjanjem frakcijskog kuta. Različite vrijednosti spektralnih parametara, kao što su polovica širine spektralnog vrha, stopa snižavanja amplituda viših harmonika ili najveća amplituda viših harmonika, odnosno njihova ovisnost u odnosu na red frakcijske Fourierove transformacije, također se mogu odrediti upotrebom ovih funkcija

Robust Device Authentication Using Non-Standard Classification Features

This paper investigates the use of novel hardware features derived from the physical and behavioral characteristics of electronic devices to identify such devices uniquely. Importantly, the features examined exhibit non-standard and multimodal distributions which present a significant challenge to model and characterize. Specifically, the potency of four data classification methods is compared whilst employing such characteristics, proposed model Multivariate Gaussian Distribution (MVGD -address multimodality), Logistic Regression (LogR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Performance is measured based on its accuracy, precision, recall and f measure. The experimental results reveal that by addressing multimodal features with proposed model Multivariate Gaussian Distribution classifier, the overall performance is better than the other classifiers

Influence of non-genetic factors on first lactation and lifetime performance traits in Nili-Ravi buffaloes

The objective of this research was to evaluate the effects of various non-genetic factors on first lactation and lifetime productivity in Nili-Ravi buffaloes, including season of birth, period of birth, age at first calving, season of first calving, period of first calving, and number of lactations completed. The livestock data on first lactation and lifetime performance traits relevant to 501 Nili-Ravi buffaloes were collected from the history sheet of the animal for a period from 1983 to 2017 (i.e., 35 years) and maintained at the Indian Council of Agricultural Research—Central Institute for Research on Buffaloes (ICAR-CIRB) sub-campus, in Nabha, Punjab, India. To evaluate the least-squares means (LSMs) and the effect of non-genetic factors on performance traits, a least-squares analysis model was applied. The overall LSM for age at first calving (AFC) was 45.03 ± 0.40 months and ranged from 34 to 54 months. The results indicated that the effect of AFC was highly significant (p ≤ 0.01) on first lactation total milk yield (FLTMY), first lactation standard milk yield (305 days or less) (FLSMY), first peak milk yield (FPY), and first lactation length (FLL) in the Nili-Ravi breed of buffaloes. FLTMY, FLSMY, and FPY were highest (2,250.08 ± 48.16 kg, 1,944.68 ± 31.20 kg, and 9.32 ± 0.16 kg/day, respectively) for animals with an AFC of 42–48 months. Furthermore, FLTMY, FLSMY, and FLL were highest (2,411.02 ± 68.68 kg, 2,008.81 ± 44.49 kg, and 357.43 ± 8.13 days, respectively) in animals that first calved in the autumn season. However, the lowest first dry period (FDP), first service period (FSP), and first calving interval (FCI) (110.63 ± 7.42 days, 125.48 ± 9.04 days, and 443.63 ± 9.00 days, respectively) were found for animals that first calved in the rainy season. The overall LSM for herd life (HL), productive life (PL), productive days (PDs), unproductive days (UDs), total lifetime milk yield (total LTMY), standard lifetime milk yield (standard LTMY), milk yield per day of productive life (MY/PL), milk yield per day of productive days (MY/PDs), and milk yield per day of herd life (MY/HL) were estimated as 3,779.84 ± 31.86 days, 2,078.55 ± 24.32 days, 1,552.74 ± 20.06 days, 525.81 ± 12.44 days, 10,229.71 ± 195.31 kg, 9,203.64 ± 173.52 kg, 4.86 ± 0.08 kg/day, 6.46 ± 0.08 kg/day, and 2.66 ± 0.04 kg/day, respectively. The effect of AFC on HL, PDs, UDs, total LTMY, and standard LTMY was highly significant (p ≤ 0.01). Furthermore, the effect of season of first calving on HL, PL, and PDs was significant (p ≤ 0.05). In addition, the effect of the period of first calving on HL, PDs, standard LTMY, and total LTMY was significant (p ≤ 0.05). In these contexts, it can be concluded that the buffaloes of the Nili-Ravi breed with an AFC of 42–48 months performed better than animals with a later AFC in terms of production, reproduction, and lifetime traits

Estimation of Gestational Age, Using Neonatal Anthropometry: A Cross-sectional Study in India

Prematurity is a significant contributor to neonatal mortality in India. Conventionally, assessment of gestational age of newborns is based on New Ballard Technique, for which a paediatric specialist is needed. Anthropometry of the newborn, especially birthweight, has been used in the past to predict the gestational age of the neonate in peripheral health facilities where a trained paediatrician is often not available. We aimed to determine if neonatal anthropometric parameters, viz. birthweight, crown heel-length, head-circumference, mid-upper arm-circumference, lower segment-length, foot-length, umbilical nipple distance, calf-circumference, intermammary distance, and hand-length, can reliably predict the gestational age. The study also aimed to derive an equation for the same. We also assessed if these neonatal anthropometric parameters had a better prediction of gestational age when used in combination compared to individual parameters. We evaluated 1,000 newborns in a cross-sectional study conducted in Guru Teg Bahadur Hospital in Delhi. Detailed anthropometric estimation of the neonates was done within 48 hours after birth, using standard techniques. Gestational age was estimated using New Ballard Scoring. Out of 1,250 consecutive neonates, 1,000 were included in the study. Of them, 800 randomly-selected newborns were used in devising the model, and the remaining 200 newborns were used in validating the final model. Quadratic regression analysis using stepwise selection was used in building the predictive model. Birthweight (R=0.72), head-circumference (R=0.60), and mid-upper arm-circumference (R=0.67) were found highly correlated with gestation. The final equation to assess gestational age was as follows: Gestational age (weeks)=5.437 7W\u20130.781 7W2+2.815 7HC\u20130.041 7HC2+0.285 7MUAC\u201322.745 where W=Weight, HC=Head-circumference and MUAC=Mid-upper arm-circumference; Adjusted R=0.76. On validation, the predictability of this equation is 46% (\ub11 week), 75.5% (\ub12 weeks), and 91.5% (\ub13 weeks). This mathematical model may be used in identifying preterm neonates