Experience and Thoughts
Saturday, August 16, 2014
Direct Download Link for Microsoft Visual Studio 2013 with update 3
http://download.microsoft.com/download/4/3/0/4307E0E6-7F2D-4885-86E6-AE8804F07AC6/vs2013.3_ult_enu.iso
Wednesday, August 6, 2014
Zarori tha (Back to love) Rahat Fateh Ali Khan
لفظ کتنے ہی تیرے پیروں سے لپٹے ہوں گے
تو نے جب آخری خط میرا جلایا ہو گا
تو نے جب پھول کتابوں سے نکالے ہوں گے
دینے والا بھی تجھے یاد تو آیا ہو گا
تیری آنکھوں کے دریا کا اترنا بھی ضروری تھا
محبت بھی ضروری تھی بچھڑنا بھی ضروری تھا
ضروری تھا کہ ہم دونوں طوافء آرزو کرتے
مگر پھر آرزؤں کا بکھرنا بھی ضروری تھا
بتاؤ یاد ہے تم کو وہ جب دل کو چرایا تھا
چرائی چیز کو تم نے خدا کا گھر بنایا تھا
وہ جب کہتے تھے میرا نام تم تسبح میں پڑھتے ہو
محبت کی نمازوں کو قضا کرنے سے ڈرتے ہو
مگر اب یاد آتا ہے وہ باتیں تھیں محض باتیں
کہیں باتوں ہی باتوں میں مکرنا بھی ضروری تھا
وہی ہیں صورتیں اپنی وہی میں ہوں وہی تم ہو
مگر کھویا ہوا ہوں میں مگن تم بھی کہیں گم ہو
محبت میں دغا کی تھی سو کافر تھے سو کافر ہیں
ملیں ہیں منزلیں پھر بھی مسافر تھے مسافر ہیں
تیرے دل کے نکالے ہم کہاں بھٹکے کہاں پہنچے
مگر بھٹکے تو یاد آیا بھٹکنا بھی ضروری تھا
محبت بھی ضروری تھی بچھڑنا بھی ضروری تھا
ضروری تھا کہ ہم دونوں طوافء آرزو کرتے
مگر پھر آرزؤں کا بکھرنا بھی ضروری تھا
تیری انکھوں کے دریا کا اترنا بھی ضروری تھا
Wednesday, July 2, 2014
Development of the Modern Tanks
Development of the Modern Tanks
The tank is an all terrain AFV designed primarily to engage enemy forces by the
use of direct fire in the frontal assault role. Though several configurations have been tried,
particularly in the early experimental days of tank development, a standard, mature design
configuration has since emerged to a generally accepted pattern. This features a main
artillery gun, mounted in a fully rotating turret atop a tracked automotive hull, with
various additional machine guns throughout.
Philosophically, the tank is, by its very nature, a purely offensive weapon. Being a
protective encasement with at least one gun position, it is essentially a pillbox or small
fortress (though these are static fortificationsof a purely defensive nature) that can move
toward the enemy - hence its offensive utility.
Historically, tanks are divided into 3 categories: Light Tanks (small, thinly
armoured, weakly gunned, but highly mobile tanks intended for the armoured
reconnaissance role), Medium Tanks (mid-sized, adequately armoured, respectably
gunned, fairly mobile tanks intended to provide an optimum balance of characteristics for
manoeuvre combat, primarily against other tanks), and Heavy Tanks (large, thickly
armoured, powerfully gunned, but barely mobile tanks intended for the breakthrough role
against fortified lines, particularly in support of infantry formations). Other designations
(such as Cavalry Tank, Cruiser Tank, and Infantry Tank) have been used by various
countries to denote similar roles.
A modern main battle tank incorporates advances in automotive, artillery, and
armour technology to combine the best characteristics of all three historic types into a
single, all around type. It is distinguished by its high level of firepower, mobility and
armour protection relative to other vehicles of its era. It can cross comparatively rough
terrain at high speeds, but is fuel, maintenance, and ammunition-hungry which makes it
logistically demanding. It has the heaviest armour of any vehicle on the battlefield, and
carries a powerful weapon that may be able to engage a wide variety of ground targets. It
is among the most versatile and fearsome weapons on the battlefield, valued for its shock
action against other troops and high survivability.
The tank is an all terrain AFV designed primarily to engage enemy forces by the
use of direct fire in the frontal assault role. Though several configurations have been tried,
particularly in the early experimental days of tank development, a standard, mature design
configuration has since emerged to a generally accepted pattern. This features a main
artillery gun, mounted in a fully rotating turret atop a tracked automotive hull, with
various additional machine guns throughout.
Philosophically, the tank is, by its very nature, a purely offensive weapon. Being a
protective encasement with at least one gun position, it is essentially a pillbox or small
fortress (though these are static fortificationsof a purely defensive nature) that can move
toward the enemy - hence its offensive utility.
Historically, tanks are divided into 3 categories: Light Tanks (small, thinly
armoured, weakly gunned, but highly mobile tanks intended for the armoured
reconnaissance role), Medium Tanks (mid-sized, adequately armoured, respectably
gunned, fairly mobile tanks intended to provide an optimum balance of characteristics for
manoeuvre combat, primarily against other tanks), and Heavy Tanks (large, thickly
armoured, powerfully gunned, but barely mobile tanks intended for the breakthrough role
against fortified lines, particularly in support of infantry formations). Other designations
(such as Cavalry Tank, Cruiser Tank, and Infantry Tank) have been used by various
countries to denote similar roles.
A modern main battle tank incorporates advances in automotive, artillery, and
armour technology to combine the best characteristics of all three historic types into a
single, all around type. It is distinguished by its high level of firepower, mobility and
armour protection relative to other vehicles of its era. It can cross comparatively rough
terrain at high speeds, but is fuel, maintenance, and ammunition-hungry which makes it
logistically demanding. It has the heaviest armour of any vehicle on the battlefield, and
carries a powerful weapon that may be able to engage a wide variety of ground targets. It
is among the most versatile and fearsome weapons on the battlefield, valued for its shock
action against other troops and high survivability.
ACHIEVING CUSTOMER SATISFACTION THROUGH BUSINESS PROCESS ANALYSIS
ACHIEVING CUSTOMER SATISFACTION THROUGH BUSINESS
PROCESS ANALYSIS:
ABSTRACT
The aim of the study is to check the customer satisfaction through business process analysis
in all Mobile telecommunication Companies of Pakistan. A major aspect of this research
study is considering the customer a part of the company while providing the right
compensation and evaluation plans to encourage employees to serve the customers better.
In this Research Study, all Mobile Telecommunication Companies are taken as whole
business processes, in order to find the issues, which are required to be analyzed and
improved to achieve customer satisfaction.
For identifying the issues related to the Customer Satisfaction, this research starts from
studying the companies’ business processes which is followed by a survey. A comparison
and analysis is done among the business processes of the companies based on the survey
conducted. **full research paper will be provided on demand with authors permission.
MULTIPLE OBJECTS TRACKING USING PARTICLE FILTER
MULTIPLE OBJECTS TRACKING USING PARTICLE
FILTER WITH PROBABILISTIC DATA ASSOCIATION
PROCESS :
The extended Kalman filter (EKF) and unscented Kalman filter (UKF) are
standard techniques for performing recursive nonlinear estimation. However, these
methods are far from being optimal. Non-Gaussian and non-linear estimation problems
are resolved in an optimal way (approximately) using recursive Monte Carlo methods or
Particle filters with better performance as compared to that of the EKF and UKF. In
multiple targets tracking, a difficulty arises in keeping track of the individual objects as
they come close to each other and then separate and also when new objects come into
view and/or existing ones disappear. Association of the available measurements to the
correct objects is the fore most important aspect in multiple objects tracking problems.
The data association process is helpful in such cases that helps tracker keep correct track
of individual objects. This thesis presents a tracking method based on the particle filter
and the probabilistic data association (PDA)hypothesis calculations. This algorithm is
used to estimate the position and size of multiple targets in noisy video sequences, thus
showing that it is able to answer the data association problem. The uncertainty of the
measurement origin can be handled by the proposed algorithm. A data association
technique based on the joint probabilistic data association (JPDA) is utilized. A
comparison of tracking with classical Kalman Filter with PDAF, SSRLS with PDAF and
Particle filter with JPDA is carried out. Performance comparison of tracking using
Particle filters with and without JPDA is also demonstrated through simulations.
In most of the practical applications, it is observed that the recorded videos have
some noise which may be due to bad weather (light, wind, etc.) or due to problems in
sensors. Moreover, when a video is transmitted or stored from one data storage location
to another loss of some information and addition of some noise generally takes place. A
comparison of tracking with various noise levels is also made through simulations.
FILTER WITH PROBABILISTIC DATA ASSOCIATION
PROCESS :
The extended Kalman filter (EKF) and unscented Kalman filter (UKF) are
standard techniques for performing recursive nonlinear estimation. However, these
methods are far from being optimal. Non-Gaussian and non-linear estimation problems
are resolved in an optimal way (approximately) using recursive Monte Carlo methods or
Particle filters with better performance as compared to that of the EKF and UKF. In
multiple targets tracking, a difficulty arises in keeping track of the individual objects as
they come close to each other and then separate and also when new objects come into
view and/or existing ones disappear. Association of the available measurements to the
correct objects is the fore most important aspect in multiple objects tracking problems.
The data association process is helpful in such cases that helps tracker keep correct track
of individual objects. This thesis presents a tracking method based on the particle filter
and the probabilistic data association (PDA)hypothesis calculations. This algorithm is
used to estimate the position and size of multiple targets in noisy video sequences, thus
showing that it is able to answer the data association problem. The uncertainty of the
measurement origin can be handled by the proposed algorithm. A data association
technique based on the joint probabilistic data association (JPDA) is utilized. A
comparison of tracking with classical Kalman Filter with PDAF, SSRLS with PDAF and
Particle filter with JPDA is carried out. Performance comparison of tracking using
Particle filters with and without JPDA is also demonstrated through simulations.
In most of the practical applications, it is observed that the recorded videos have
some noise which may be due to bad weather (light, wind, etc.) or due to problems in
sensors. Moreover, when a video is transmitted or stored from one data storage location
to another loss of some information and addition of some noise generally takes place. A
comparison of tracking with various noise levels is also made through simulations.
Sunday, June 29, 2014
Some Important Physics Formulas for Students
Some Important daily life Physics Formulae for Students
v = u + at
v² = u² + 2as
s = ut + ½at²
F = ma
F = Gm1m2/r2
a = v2/r
Static friction = FN mS
Dynamic friction = FN mD
MA = load/effort = number of supporting ropes in a frictionless pulley system
VR = distance moved by effort / distance moved by load = number of supporting ropes
Efficiency = work out / work in
Moment = F x perpendicular distance to pivot point
Momentum = mv
Smv before collision = Smv after collision
Impulse = Ft
PE = mgh
KE = ½mv2
Work done = Fd
Power = Fd/t
Dl = kF
Y = stress / strain = (F/A) / (Dl/l)
S = stress / strain = (perpendicular F/A) / (Dx/l)
B = stress / strain = DP / (DV/V)
f µ Ö (k/m)
T = 2p Ö (l/g)
r = m/V
Upward force on a body = weight of fluid displaced by that body
Hydrostatic pressure = rgh
r1A1v1 = r2A2v2
P1 + rgh1 + ½rv12 = P2 + rgh2 + ½rv22
Velocity of sound in a fluid, v = Ö (B/r)
v µ Ö (T/mw)
Velocity of sound in a solid = Ö (Y/r)
Velocity of a wave in a taut string = Ö (tension/[mass/length])
dB = 10log10(I/10-12)
Beat frequency = difference between the two frequencies
f' = f(C±Vd / C±Vs)
F = kq1q2/r²
E = F/q
Between two parallel plates, E = V/d
Concerning a point charge, E = kq/r²
V = Fd/q
V = IR
For resistors in series, RT = R1 + R2 + R3 + ...
For resistors in parallel, 1/RT = 1/R1 + 1/R2 + 1/R3 + ...
Resistivity = RA/l
C = q/V = permitivity x A/d
For capacitors in series, 1/CT = 1/C1 + 1/C2 + 1/C3 + ...
For capacitors in parallel, CT = C1 + C2 + C3 + ...
P = IV
If a.c., P = (Imax/Ö2) (Vmax/Ö2) = Imax Vmax/2
n1sin i = n2sin r
1/f = 1/u + 1/v
Lens power = 1/f where f is the focal length in m. Lens power is measured in diopters.
Two lenses in contact: 1/F = 1/f1 + 1/f2
E = hf
At = Aoe-kt
t½ = ln2/k
v = u + at
v² = u² + 2as
s = ut + ½at²
F = ma
F = Gm1m2/r2
a = v2/r
Static friction = FN mS
Dynamic friction = FN mD
MA = load/effort = number of supporting ropes in a frictionless pulley system
VR = distance moved by effort / distance moved by load = number of supporting ropes
Efficiency = work out / work in
Moment = F x perpendicular distance to pivot point
Momentum = mv
Smv before collision = Smv after collision
Impulse = Ft
PE = mgh
KE = ½mv2
Work done = Fd
Power = Fd/t
Dl = kF
Y = stress / strain = (F/A) / (Dl/l)
S = stress / strain = (perpendicular F/A) / (Dx/l)
B = stress / strain = DP / (DV/V)
f µ Ö (k/m)
T = 2p Ö (l/g)
r = m/V
Upward force on a body = weight of fluid displaced by that body
Hydrostatic pressure = rgh
r1A1v1 = r2A2v2
P1 + rgh1 + ½rv12 = P2 + rgh2 + ½rv22
Velocity of sound in a fluid, v = Ö (B/r)
v µ Ö (T/mw)
Velocity of sound in a solid = Ö (Y/r)
Velocity of a wave in a taut string = Ö (tension/[mass/length])
dB = 10log10(I/10-12)
Beat frequency = difference between the two frequencies
f' = f(C±Vd / C±Vs)
F = kq1q2/r²
E = F/q
Between two parallel plates, E = V/d
Concerning a point charge, E = kq/r²
V = Fd/q
V = IR
For resistors in series, RT = R1 + R2 + R3 + ...
For resistors in parallel, 1/RT = 1/R1 + 1/R2 + 1/R3 + ...
Resistivity = RA/l
C = q/V = permitivity x A/d
For capacitors in series, 1/CT = 1/C1 + 1/C2 + 1/C3 + ...
For capacitors in parallel, CT = C1 + C2 + C3 + ...
P = IV
If a.c., P = (Imax/Ö2) (Vmax/Ö2) = Imax Vmax/2
n1sin i = n2sin r
1/f = 1/u + 1/v
Lens power = 1/f where f is the focal length in m. Lens power is measured in diopters.
Two lenses in contact: 1/F = 1/f1 + 1/f2
E = hf
At = Aoe-kt
t½ = ln2/k
Saturday, June 21, 2014
Installing Visual Studio 2013 and Starting
Here I am sharing an interesting software visual studio 2013 , I've explained a little bit how to start.
Hope you enjoy this tutorial. Subscribe for more
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