Paper Writing Service http://StudyHub.vip/A-Practical-Guide-For-Academic-Proposal 👈

1. 1
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
A practical guide for academic proposal writing
Dr. Redmond Shamshiri
University of Florida
ramin.sh@ufl.edu
http://plaza.ufl.edu/ramin.sh/
September, 2011

2. 2
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
EXAMPLE #1:
Providing supervisory committee members with one paragraph
research description, and Gant chart
Title of Research:
Retrieval of chlorophyll concentration from oil palm leaf reflectance spectra
using wavelet analysis
Program: Ph.D, M.Sc, etc.., Electrical Engineering, etc..etc..
Semester Intake: Feb. 2011
Expected date of Graduation: Feb.2014
Name of Supervisor: Prof. Dr. First Name, Last Name
Summary of Research:
The dynamics of foliar chlorophyll concentrations have
considerable significance for plant–environment interactions, ecosystem
functioning and crop growth. Hyperspectral remote sensing has a valuable
role in the monitoring of such dynamics. This study focuses upon improving
the accuracy of chlorophyll quantification by applying wavelet analysis to
reflectance spectra of oil palm leaves. Leaf-scale radiative transfer models
will be used to generate very large spectral data sets with which to develop
and rigorously test refinements to the approach and compare it with
existing spectral indices. The results are expected to demonstrate that by
decomposing leaf spectra, the resultant wavelet coefficients can be used to
generate accurate predictions of chlorophyll concentration, despite wide
variations in the range of other biochemical and biophysical factors that
influence leaf reflectance. Wavelet analysis would also outperform
predictive models based on untransformed spectra and a range of spectral
indices. This project will discusses the possibilities for further refining the
wavelet approach and for extending the technique to the sensing of a
variety of vegetation properties at a range of spatial scales.

3. 3
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
EXAMPLE #2:
Providing supervisory committee members with a complete research
proposal
Project title:
Retrieval of chlorophyll concentration from oil palm leaf reflectance
spectra using wavelet analysis
Project objectives:
This study focuses upon improving the accuracy of chlorophyll
quantification by applying wavelet analysis to reflectance spectra of oil
palm leaves. This project will also discuss the possibilities for further
refining the wavelet approach and for extending the technique to the
sensing of a variety of vegetation properties at a range of spatial scales. An
application of this research would be on variable rate application, where an
image processing method to assess oil palm leaf chlorophyll level is
required. Specific objectives are as follow:
o To address the chlorophyll spectral band accurately in the oil palm leaf
reflectance spectra
o To identify appropriate wavelet functions for this task
o To determine the optimum wavelet coefficients (at particular scales
and positions) that provide the greatest sensitivity to chlorophyll
o To investigate whether reflectance spectra or first or seconds
derivatives reflectance spectra provide the most effective inputs for
wavelet analysis
o To determine whether discrete or continuous wavelet transformations
result in more accurate chlorophyll estimates
o To develop a low cost yet more accurate real time sensor for
determination of nitrogen content in oil palm leaves to be replaced
with the high cost conventional methods.
The results are expected to demonstrate that by decomposing leaf
spectra, the resultant wavelet coefficients can be used to generate accurate
predictions of chlorophyll concentration, despite wide variations in the
range of other biochemical and biophysical factors that influence leaf
reflectance. Wavelet analysis would also outperform predictive models
based on untransformed spectra and a range of spectral indices. The
ultimate goal of this research is to further the development of this
technology through the use of a newer mathematical technique. The
wavelet transformation has been developed to convert signals and images
into frequency and spatial or time based domains (Marchant, 2003). This
technique has never been used to detect the nitrogen content of oil palm
leaves, but its properties make it a useful tool in analysis of images and
signals with transient information. Wavelet transform has a unique
property in that it can detect spatial features in a signal as well as the
frequency information that the Fourier transform can detect.
Research backgrounds
The dynamics of foliar chlorophyll concentrations have
considerable significance for plant–environment interactions, ecosystem
functioning and crop growth. Hyperspectral remote sensing has a valuable
role in the monitoring of such dynamics. The chlorophylls are essential

4. 4
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
pigments in the process of photosynthesis; therefore, it has been argued
that they are the most important organic molecules on Earth (Davies,
2004). Changes in the total concentration of foliar chlorophyll and the
relative proportions of chlorophyll a and chlorophyll b are brought about
by a variety of physiological stresses, leaf development and senescence.
Such pigment variations relate directly to the rate of primary production.
Furthermore, the chlorophylls contain a large proportion of total leaf
nitrogen therefore measurements of chlorophyll concentration can provide
an accurate indirect assessment of plant nutrient status (Moran et al.,
2000). Hence, information concerning the spatial and temporal dynamics of
leaf chlorophyll is of considerable value from a scientific viewpoint,
particularly in investigations of plant–environment interactions, and from
an applied perspective in agriculture, forestry and environmental
management. Wavelets are functions that decompose a complex signal into
component sub-signals. When applied to reflectance spectra, a model can
be established between wavelet coefficients that are assigned to the
component sub-signals and the concentrations of chemical constituents. In
the context of the remote sensing of foliar chlorophyll, wavelet analysis has
the potential to capture much more of the information contained with
reflectance spectra than previous analytical approaches which have tended
to focus on using a small number of optimal wavebands while discarding
the majority of the spectrum.
There are many commercial chlorophyll meters available which basically
measure the greenness of the plant. However chlorophyll meters are
subject to variability resulting from changes in light intensity from shade,
cloud cover, and sun intensity. Due to these concerns Kruse et al., (2004)
cautioned while the chlorophyll content can be related to the nitrogen
status in the plant, you should be careful basing fertility programs on these
readings. There are two potential problems associated with using plant
color to determine its nitrogen needs. First, if a nitrogen deficiency is
apparent, then it may be too late to apply fertilizer and expect a positive
plant response resulting in increased yield. The other major problem is that
different plants exhibit different colors in response to similar nutrient
deficiencies, i.e. there is a lack of nutrient deficiency-specific color. For
example, maize leaves become yellow-green in color if either nitrogen or
moisture deficiencies exist. This makes it difficult to distinguish the
particular deficiency for a plant based solely on color. Also, standards for
multiple plants may be required as the same nutrient deficiencies manifest
in varying colors.
Hypothesis
The hypothesis of this research is that the wavelet transform can
improve the detection of the spatial features in multi-spectral images for
determining the nitrogen content of oil palm. This research will study this
hypothesis through the use of the Matlab (Mathwork Inc) Wavelet CIR
images Toolbox. Color infrared images of oil palm leaves will be segmented
and then transformed into one-dimensional signals. These signals then will
undergo a wavelet transform to bring out specific features known to
nitrogen deficient of oil palm leaves. The final results are expected to show
a correlation factor between the transformed data and SPAD-502Plus
Chlorophyll Meter (Konica Minolta Inc) data taken of the plants as a
baseline for nitrogen stress level.

5. 5
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
Research questions
The first question to be addressed in this research is that which
family of wavelet is most appropriate for detection of nitrogen in oil palm
leaves spectra? It may be possible to select a priori the optimum family or
specific wavelet for a particular task, on the basis of mathematical criteria
or by using other factors such as the correspondence between the shape of
the wavelet and the shape of the absorption feature of interest. However,
Cocchi et al. (2003) have demonstrated the difficulty in developing an a
priori rule for identifying the most appropriate wavelet. Therefore, in the
present study, to assess the sensitivity of the analysis to the selection of the
wavelet used for decomposition, all seven compactly supported wavelet
families available within MATLAB will be tested. Hence, 53 individual
wavelet functions to be used are: the Haar wavelet, Daubechies wavelets
(shortened to db subsequently with orders ranging from to db to
db ; Symlets sym to sym8 ; Coiflets coif ' to coif ; Biorthogonal
wavelets bior . to bior .8 ; Reverse biorthogonal wavelets rbio . to
rbio .8 ; and the Discrete Meyer pseudo-wavelet dmey which
approximates the Meyer wavelet and allows discrete wavelet analysis.
Therefore, for each of the input reflectance and derivative spectra, a set of
continuous transformations and a set of discontinuous transformations will
be performed using each wavelet function in turn.
Literature review
There has been extensive research done on the multi-spectral
analysis of images. Multi-spectral images contain a number (two or more)
of monochrome images (Gonzalez and Woods, 2002). Each monochrome
image contains the amount of light reflected off the objects in the picture
for a particular spectral band. A variety of techniques on multi-spectral
images have been used for a multitude of purposes.
Several studies using hyperspectral data of vegetation have already
demonstrated the benefits of wavelet analysis for spectral smoothing and
noise removal (Bruce & Li, 2001; Schmidt & Skidmore, 2004), vegetation
classification (Henry et al., 2004; Hsu & Tseng, 2000; Huang et al., 2001;
Koger et al., 2003), discriminating nitrogen treatments (Reum & Zhang,
2005) and quantifying forest leaf area index (Pu & Gong, 2004). Indeed,
recent work by the authors has demonstrated the potential of wavelet
analysis for retrieving foliar nitrogen content (Ferwerda & Jones, 2006)
and photosynthetic pigment concentrations (Blackburn, 2007) from leaf
and canopy reflectance spectra but further research is needed to develop
the approach.
Kleynen et al. (2005) and Kim et al. (2002) used multi-spectral imaging to
inspect apples and it has been used to predict maturity in un-ripened
tomatoes (Hahn, 2002). It was also used in the inspection of poultry
carcasses (Lawrence et al., 2001; Park and Chen, 2001; Park et al., 1998)
and to detect the chlorophyll content of potatoes (Borhan et al., 2004).
Multi-spectral analysis has also been used in row crops for nitrogen
detection in corn (Kim and Reid, 2002; Noh et al., 2005), nitrogen detection
in rice (Huang et al., 2003), weed detection in cotton fields (Alchanatis et
al., 2005), and leaf surface wetness detection in corn (Ramalingam et al.,
2003).

6. 6
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
Research Methodology
The methodology of this research is to relate the reflectance of the
oil palm leaves to the amount of nitrogen in the plant. The SPAD-502Plus
measures the absorbances of the leaf in the red and near-infrared regions.
Using these two absorbances, the meter calculates a numerical SPAD value
which is proportional to the amount of chlorophyll present in the lead. The
chlorophyll present in the plant leaves is closely related to the nutritional
condition of the plant. The chlorophyll content will increase in proportion
to the amount of nitrogen present in the leaf. For a particular plant species,
a higher SPAD value indicates a healthier plant.
This wavelet analysis first removes unnecessary information from the
image and then converts the image into a one-dimensional (1-D) signal
representing the reflectance of the oil palm leaves. The obtained data is
further processed using wavelet transform to find specific features that
correspond to oil palm leaf chlorophyll stress. To implement wavelet
analysis, the 1-D signal will be deconstructed into some packets of narrows
frequency bands to find the lowest level approximations at different levels.
The maximum wavelet coefficients will be identified for interested signal
bands and then compared to SPAD meter readings which are used as the
ground-truth oil palm nitrogen level. Analysis results will indicate that
which wavelet packet at which level of deconstruction would have the
highest linear regression coefficient for oil palm nitrogen levels.
Milestones
Task Planned date Actual
date
Progress
Report
1. Discussion with supervisory committee April.01.2012
2. Proposal writing March.20.2012
3. Preliminary studies and literature
review
July.01.2012
4. Studying and purchasing Materials &
instrumentations
Sep.01.2012
5. Data collection Nov.01.2012
6. Laboratory analysis of data Jan.01.2013
7. Wavelet analysis of data April.01.2012
8. Calibration and validations June.01.2013
9. Writing the result and reports Sep.01.2013
10. Thesis submission and defense Feb.01.2014
Project activities,
There are two categories of activities associated with this project, the
research activity which leads to a Ph.D, and the transfer of the research
results to customers/beneficiaries

7. 7
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
Research activity Gant chart
Duration: 126 weeks, approximately 32 months (Normal time)
In this project, there are 10 tasks, labeled A through J. Each task has three
time estimates: the optimistic time estimate (O), the most likely or normal
time estimate (M), and the pessimistic time estimate (P). The expected time
(TE) is computed using the formula (O + 4M + P) ÷ 6.
Activity Predecessor
Time estimates (Weeks)
Expected time
(Weeks)
Opt. (O)
Normal
(M)
Pess. (P)
A. Discussion with supervisory
committee
— 1 2 3
2
B. Proposal writing — 2 4 6 4
C. Preliminary studies and
literature review
A 4 8 12
8
D. Studying and purchasing
Materials & instrumentations
A 8 24 32
22.6
E. Data collection D 12 16 24 16.6
F. Laboratory analysis of data E 12 16 24 16.6
G. Wavelet analysis of data B,C 12 16 24 16.6
H. Calibration and validations F,G 8 12 16 12
I. Writing the result and reports H 12 16 20 16
J. Thesis submission and
defense
I 8 12 16
12
Total 79 126 177 126.6667

8. 8
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
Optimistic time: The activity time if everything progresses in an ideal
manner
Most probably time: The most likely activity time under normal conditions
Pessimistic time: The activity time if we encounter significant breakdowns
and/or delays
Arcs indicate project activities
Nodes correspond to the beginning and ending activities
Event: The completion of all the activities that lead into a node is referred
to as an event
Path: A path is a sequence of connected activities that leads from the
starting node to the completion node.
ES: Earliest start time for a particular activity
EF: Earliest finish time for a particular activity
T: Expected activity time for the activity
EF= ES+T
Latest finish time:
The latest finish time for an activity entering a particular node is equal to
the smallest of the latest start times for all activities leaving the node
Activity Earliest
Start (ES)
Earliest
Finish (EF)
Latest
Start (LS)
Latest Finish
(LF)
Slack
(LS-ES)
Critical
Path?
A 0 2 0 2 0 YES
B 0 4 38 42 38
C 2 10 34 42 32
D 2 26 2 26 0 YES
E 26 42 26 42 0 YES
F 42 58 42 58 0 YES
G 10 26 42 58 32
H 58 70 58 70 0 YES
I 70 86 70 86 0 YES
J 86 98 86 98 0 YES
The critical path is ADEFHIJ.
1. What is the total time to complete the project?
The project can be completed in 98 weeks if the individual activities are
completed on schedule.
2. What are the scheduled start and finish dates for each specific
activity?
The detailed activity schedule that shows the earliest start, latest start,
earliest finish and latest finish times for each activity
3. Which activities are critical and must be completed exactly as
scheduled in order to keep the project on schedule?
The seven activities, A, D, E, F, H, I, J as the critical activities for the project
4. How long can non critical activities be delayed before they cause
a delay in total project?
The slack time available for all activities as shown in table

9. 9
Dr. Redmond Shamshiri Practical tutorial to proposal writing Sep.2011
Transfer of the research results to customers activities:
This set of activity includes:
Activit
y
Description Predecessors
A R&D product design -
B Plan market research -
C Routing (manufacturing engineering) A
D Build prototype model A
E Prepare marketing brochure A
F Cost estimates industrial
(engineering)
C
G Preliminary product testing D
H Market survey B, E
I Pricing and forecast report H
J Final report F,G,I