ICEET 2016 Conference Participation as Author and Session Chair

The ICEET 2016 conference was held at Lahore 7-8 April [PRESENTATION]. Some photos from this event are courtesy of conference organizers.

Best Session Paper Award at SMIC 2016 Dubai 9 January

Unable to get visa for Dubai due delay in passport renewal, however, the presentation was delivered on Skype (thanks to Dr. Aqeel Ur Rehman for his kind assistance). The presentation is available [LINK].

Pictures from Bioinformatics Workshop at Virtual University held at Lahore on 05 January 2016

On Tuesday 05 Jan 2016, I have attend the workshop, "Bioinformatics Workshop Hands on Training on Analysis of Biological Data using R," at Lahore. Few pictures (courtesy of Dr. Muhammad Tariq Pervez of Virtual University) from this workshop are posted here for record.

Lecture Slides: GIS and Remote Sensing Applications for Environment

The course, "GIS and Remote Sensing Applications for Environment," was offered in Fall 2015 semester at GSESIT, Hamdard University.

Lecture # 01 Introduction
Lecture # 02 Basic Concepts
Lecture # 03 Aerial Photography
Lecture # 04 Photogrammetry - 1
Lecture # 05 Photogrammetry - 2
Lecture # 06 Sensing Systems
Lecture # 07 Earth Resource Satellites
Lecture # 08 Visual Image Interpretation
Lecture # 09 Essential Digital Image Processing
Lecture # 10 GIS Demysti ed
Lecture # 11 Coordinate Space and Map Projection with an Introduction to Geostatistics
Lecture # 12 Remote Sensing and GIS Integration
Lecture # 13 Future Trends and Challenges

Presentation: An Introduction to LaTeX for Engineers and Scientists

Finally, the initial version of this presentation is ready. This is an introduction level presentation on LaTeX -- the professional document preparation system. In addition to this presentation, I have created a project on overleaf for a practical demonstration. There are many useful online resources, my choices for interested LaTeX learners are Free & Interactive Online Introduction to LaTeX (three parts by Dr. John Lees-Miller) and TeXample. 

The Invasive Weed Optimization Algorithm: An Implemenation

I am working on implementation of the invasive weed optimization (IWO) algorithm since one year or so. The selection of this algorithm has linkage to my family business background which is very closely related with manufacture and maintain tools to support agriculture, vegetation. The original IWO algorithm was proposed in a paper by Mehrabian and Lucas (2006) with title, "a novel numerical optimization algorithm inspired from weed colonization." I have made a number of attempts to write a program code in MATALB. The basic working version is recently completed and pasted below. The algorithm has four major steps: (1) initialization, (2) reproduction, (3) spatial dispersal, and (4) competitive exclusion. The program code is pasted below.

% created by dr tariq javid as of 02-december-14
% last updated on 21-november-15
% f1 = sphere, f2 = griewant, f3 = rastrigin
clear all, clc
% --- user input
init_pop = input('number of initial population [10]: ');
iter_max = input('maximum number of iterations [100]: ');
dimn_prb = input('problem dimension [2]: ');
plnt_max = input('maximum number of plant population [15]: ');
seed_max = input('maximum number of seeds [5]: ');
seed_min = input('minimum number of seeds [0]: ');
nonl_mid = input('nonlinear modulation index [3]: ');
stdv_ini = input('initial value of standard deviation [3]: ');
stdv_fin = input('final value of standard devition [0.001]: ');
srch_ini = input('a. initial search area - lower [-40]: ');
srch_fin = input('b. initial search area - upper [-30]: ');
% --- default value
if isempty(init_pop), init_pop = 10   ; end
if isempty(iter_max), iter_max = 100  ; end
if isempty(dimn_prb), dimn_prb = 2    ; end
if isempty(plnt_max), plnt_max = 15   ; end
if isempty(seed_max), seed_max = 5    ; end
if isempty(seed_min), seed_min = 0    ; end
if isempty(nonl_mid), nonl_mid = 3    ; end
if isempty(stdv_ini), stdv_ini = 3    ; end
if isempty(stdv_fin), stdv_fin = 0.001; end
if isempty(srch_ini), srch_ini = -40  ; end
if isempty(srch_fin), srch_fin = -30  ; end
% --- function definition
f1 = @(x,y) x.^2 + y.^2; % for default values
% f2 = @(x,y) (x.^2 + y.^2)/4000 - cos(x/sqrt(2)) .* cos(y/sqrt(2)) + 1;
% f3 = @(x,y) 20 + x.^2 + y.^2 - 10 * (cos(2*pi*x) + cos(2*pi*y));
% --- initialize weeds as random numbers
weed_ini = srch_ini + (srch_fin - srch_ini).*rand(init_pop,dimn_prb)
% --- preparation of next step
temp_one = (stdv_ini - stdv_fin); temp_two = iter_max .^ nonl_mid ;
weed_pop = weed_ini;
figure, axis([-50 10 -50 10]), hold on
plot(weed_ini(:,1),weed_ini(:,2),'r^')
for iter_idx = 1:iter_max % main for loop
    temp_thr = ((iter_max - iter_idx) .^ (nonl_mid)) / temp_two;
    iter_now = temp_thr .* temp_one + stdv_fin;
    % --- evaluate fitness of weeds
    for weed_idx = 1:length(weed_pop)
        comp_fit(weed_idx) = f1(weed_pop(weed_idx,1),weed_pop(weed_idx,2));
    end
    % --- determine number of seeds as per fitness
    fit_max = min(comp_fit); fit_min = max(comp_fit);
    slope = (seed_max - seed_min) / (fit_max - fit_min);
    seed_cnt = zeros(size(comp_fit));
    for seed_idx = 1:length(weed_pop)
        seed_tmp = floor(slope * (comp_fit(seed_idx) - fit_min) + seed_min);
        seed_cnt(seed_idx) = seed_tmp;
    end
    % --- spatial dispersal
    for weed_idx = 1:length(weed_pop)
        if seed_cnt(weed_idx) ~= 0 % to avoid nan and inf unexpected termination
            weed_tmp = iter_now .* randn(seed_cnt(weed_idx),dimn_prb);
            for seed_idx = 1:seed_cnt(weed_idx)
                weed_tmp(seed_idx,1) = weed_tmp(seed_idx,1) + weed_pop(weed_idx,1);
                weed_tmp(seed_idx,2) = weed_tmp(seed_idx,2) + weed_pop(weed_idx,2);
            end
            weed_pop = cat(1,weed_pop,weed_tmp);
        end
    end
    % --- evaluate fitness of new population
    for weed_idx = 1:length(weed_pop)
        comp_fit(weed_idx) = f1(weed_pop(weed_idx,1),weed_pop(weed_idx,2)); 
    end
    % --- competitive exclusion
    while length(weed_pop) > plnt_max
    % --- i is index of maximum value i.e. worse fit
       [~, i] = max(comp_fit);
       comp_fit(i)   = [];    % remove maximum value from fitness
       weed_pop(i,:) = [];    % remove worse fit weed from population
    end
    % --- plot result for each iteration
    plot(weed_pop(:,1),weed_pop(:,2),'b*');
end
weed_pop
for weed_idx = 1:length(weed_pop)
    comp_fit(weed_idx) = f1(weed_pop(weed_idx,1),weed_pop(weed_idx,2));
end
[~, i] = min(comp_fit);
weed_idx = i
weed_sol = [weed_pop(i,1),weed_pop(i,2)]
weed_fit = comp_fit(i)
plot(weed_pop(i,1),weed_pop(i,2),'r^');
legend('initial,final','progress',2)
grid on, hold off

15 Attributes of a Good Research Paper

The list of attributes is as follows. For an example, read [Paper].

1. Motivation
2. Importance
3. Problem/Challenge
4. Reason ... why challenging?
5. Benefits 
6. Method
7. Related Work
8. Significance ... how different?
9. Contribution
10. Confidence
11. Compare
12. Discuss Results
13. Limitations ... with possible solutions
14. Future Direction
15. Well Connected ... relevant references