65 research outputs found
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Research renaissance: a new funding strategy for business schools
No full textAs the highest business school UK research income earner for the third year running*, Professors Sambit Bhattacharya, Chirantan Chatterjee, and Paul Nightingale from the University of Sussex Business School propose a 10-year plan for boosting research income based on their experiences.</p
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Growth Miracles and Growth Debacles: Exploring Root Causes
No full textIn this fascinating book, Sambit Bhattacharyya presents a detailed account of the socio-economic processes that create broad variations in living standards across the globe. The author examines the world's economic history over the last five centuries, replete with growth miracles and growth debacles: growth in Britain was steady, yet China lost her early advantage; North America settler colonies performed significantly better than those of Asia and Africa; Australia and Argentina were notably similar at the start of the twentieth century but delivered strikingly different growth outcomes. The book argues that these differences in growth rate are best explained by an interplay of factors, namely economic, political and geographical. In conclusion it presents long-run comparative growth narratives for Africa, China, India, the Americas, Russia and Western Europe. Presenting a unique and original analytical framework to explain economic growth and decline, and bridging empirical growth literature and economic history, this book will prove a stimulating read for both academic and professional economists, and scholars of economic history and economic growth. Other social scientists including sociologists, political scientists and economic historians will also find the book to be of great value
psambit9791/jDSP: v1.0.0 (December 27, 2021)
No full textRelease Notes
MAJOR FEATURE ADDITION:
Paul wavelet & Chrip Signal under Generate() class
CWT and ICWT functionality with support for 3 wavelet types: Ricker, Morlet and Paul.
STFT & Inverse STFT (using both FFT and DFT)
Complex Convolution
Deconvolution & Complex Deconvolution (full & same only) (uses DFT and QRDecomposition) [long signals may take longer processing time]
Adaptive Filters
LMS
NLMS
RLS
GNGD
AP
Patches:
Added matToComplex() functions for converting 2D arrays into a list of complex numbers.
reverse() function for Complex list
Added get_window() & apply_window() under _Window
Backward incompatible changes made with this commit to accommodate additional functionalities:
returnAbsolute() ➔ getMagnitude()
returnComplex2D() ➔ getFull()
returnComplex() ➔ getComplex()
Additional methods added to DiscreteFourier class:
getPhaseRad() ➔ Returns the phase in radians
getPhaseDeg() ➔ Returns the phase in degrees
getMagPhaseRad() ➔ Returns the magnitude and the phase (radians)
getMagPhaseDeg() ➔ Returns the magnitude and the phase (degrees)
FIRWin1 now uses enum
Added FFT and InverseFFT classes.
Added a _Fourier interface which is implemented by FFT and DFT.
Added an _InverseFourier interface which is implemented by IFFT and IDFT.
All transformations can now be done by calling transform() using the object of given class
Penggabungan Sumber Internet Load Balancing Dua ISP Di Mikrotik Dengan Metode PCC Guna Memberikan Akses Internet Untuk Penggunaan Chrome Book (Studi Kasus Di SMP Negeri 1 Sambit)
No full textThe need for internet access is currently very high, both to find information, articles and the latest knowledge. Many schools have integrated the internet network into the teaching and learning process. It is hoped that students can easily find material and understand lessons, namely SMP Negeri 1 Sambit, an educational institution that has made it one of the main sources of internet access in the teaching and learning process, namely by using Chrome Books as learning media. SMP Negeri 1 Sambit wants a stable and reliable internet connection. Therefore a solution emerged to combine the two ISPs (Internet Service Provider) and make the proxy a network link. The author uses the PCC (Per Connection Classifier) method, which is a method that can be used in Load Balancing. With this PCC method, it can be used to group connection traffic that goes through or in and out of the router into several groups and divides the load on both internet connection lines so that overload does not occur.
Keywords: ISP (Internet Service Provider), Dual internet connection, Mikrotik, PCC (Per Connection Classifier), Chrome Book
Data Analytics in Web-based Education in the Higher-education Classroom
No full textAttention span of students in a classroom is very short. To overcome this, different active learning methodologies have been used in the past. Active learning keeps the students busy and engaged throughout the lecture. It breaks the lecture into certain time intervals by intermixing breaks, demonstrations and questions after each interval. For using active learning, clickers and laptops are commonly used in higher education classroom. Most experiments in higher education classroom studying different characteristics of students like learning performance and attention, use clickers and laptop. But, most of these experiments are in a controlled setting, not scalable and compromise the privacy of students. We overcome these problems in an active learning setup in the higher education classroom where we use a web-mediated teaching tool called ASQ. ASQ is a web application that helps to give presentation in a classroom where the presenter has control over the flow of the presentation. ASQ also allows the presenter to interleave the presentation with questions, videos and other interactive JavaScript components. Anyone can anonymously join a presentation in ASQ using a web browser. ASQ tracks the activity of every student interaction by generating event logs each second. In the previous work using ASQ, it has been shown that these logs could be used to infer the attention level of students in the classroom. The goal of this thesis is to gather insights about the fine-grained study behaviour of students in a higher education classroom by analyzing these event logs.We investigate (i) the effect of lecture elements (like the difficulty, relative positioning and spacing of questions; and duration of discussion in the slides) on study behaviour (like attention level, performance and reaction time while answering questions) of students; (ii) the relationship that might exist between attention percentage of students and their participation in the in-class questions; (iii) if students are taking external help when answering questions during the lecture and the relationship that might exist between their tendency to take external help with the difficulty of questions. We conduct our study in a classroom of around 300 students, for 15 lectures in the Web and Database Technology course at TU Delft taught by 2 instructors. We find significant effect of (i) spacing of questions on reaction time and instructor on performance; (ii) length of discussion time associated with a slide on the attention level of students which agrees with past studies; (iii) relative positioning of questions on the performance of students. However, we do not find significant effect of difficulty of questions on performance and reaction time of students while answering these questions. We also find significant effect that students with more attention percentage participate more in the in-class questions. Finally, we find that students take external help while answering questions but the tendency to take external help does not depend on the difficulty of questions
JDSP
No full textjDSP is a library of signal processing tools aimed at providing functionalities as available in scipy-signal package for Python. The goal is to provide easy-to-use APIs for performing complex operation on signals eliminating the necessity of understanding the low-level complexities in the processing pipeline
psambit9791/jDSP: v0.4.0 (August 31, 2020)
No full textRelease Notes
Added new features to Generate Signal
Gaussian Pulse
Unit Impulse
Sawtooth
Added Principal Component Analysis (PCA) under transform
New Utilities:
Function to get Absolute Value of 1D and 2D Arrays
Matrix Transpose Function
Matrix Multiplication Function
Added Scalar Arithmetic Operation on 1-D Arrays as a Function
Added Linestyle to Plots
Patches:
Linspace issue for ArrayOutOfBoundsException fixed
Linspace issue for not counting span between start and stop for calculation fixed
Updated ECG dataset to "resources" folder for consistenc
psambit9791/jDSP: v0.8.0 (September 27, 2021)
No full textRelease Notes
MAJOR FEATURE ADDITION: Added new module for Splines
Akima Spline
B-Spline
Cubic Spline
Quadratic Spline
Polyphase Resampling
Patches:
Added flattenMatrix() function to convert 2D matrices to arrays
Added FIRWin1(int numTaps, double beta, boolean direct_kaiser) to directly create filters using beta paramete
psambit9791/pysnr: First Release
No full textFirst Release
This is the first release of PySNR. This aims to provide noise analysis functionalities as listed below:
[x] SNR (Signal to Noise Ratio)
[x] THD (Total Harmonic Distortion)
[x] SINAD (Signal to Noise and Distortion Ratio)
[x] TOI (Third Order Intercept)
[x] SFDR (Spurious Free Dynamic Range
psambit9791/jdsp: v3.0.0 (January 1, 2024)
No full text<h3>Release Notes</h3>
<p><strong>MAJOR FEATURE ADDITION</strong>:</p>
<ul>
<li><p>New module for ICA</p>
</li>
<li><p>New module for Discrete Sine/Cosine Transform (Forward and Inverse)</p>
</li>
<li><p>New module for Fast Sine/Cosine Transform (Forward and Inverse)</p>
</li>
<li><p>New functions for polynomial fitting</p>
<ul>
<li>polyfit()</li>
<li>polyval()</li>
<li>polyder()</li>
<li>polyint()</li>
</ul>
</li>
<li><p>New functions for generating random numbers</p>
<ul>
<li>Ability to set seed, mean and standard deviation</li>
<li>Can be between 0 and 1, integers from a range or from a normal distribution</li>
<li>Supports single digit, 1D, 2D and 3D matrices as outputs</li>
</ul>
</li>
<li><p>Added 'interp' mode for Savgol filter</p>
</li>
<li><p>New functions in Utilities:</p>
<ul>
<li>Get sign of a number or an array of numbers (equivalent to numpy.sign())</li>
<li>Get a submatrix from an existing matrix</li>
</ul>
</li>
</ul>
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