From dbba277175deec60e0207f9d42cae02e32483cb0 Mon Sep 17 00:00:00 2001 From: Ash Tyndall Date: Fri, 19 Jun 2015 19:35:20 +0800 Subject: [PATCH] Examiner 1 corrections --- conclusion/conclusion.pdf | Bin 110218 -> 110404 bytes conclusion/conclusion.tex | 4 ++-- design/design.pdf | Bin 2114417 -> 2114394 bytes design/design.tex | 8 ++++---- evaluation/evaluation.pdf | Bin 4611712 -> 4612029 bytes evaluation/evaluation.tex | 16 ++++++++-------- introduction/introduction.pdf | Bin 89327 -> 89354 bytes introduction/introduction.tex | 4 ++-- litreview/litreview.pdf | Bin 156727 -> 156865 bytes litreview/litreview.tex | 8 ++++---- thesis/thesis.pdf | Bin 7175878 -> 7176443 bytes thesis/thesis.tex | 4 ++-- 12 files changed, 22 insertions(+), 22 deletions(-) diff --git a/conclusion/conclusion.pdf b/conclusion/conclusion.pdf index 7d8e42241730dac13607504d49d703eba27ac81b..72e58256f14295d987467800455d7a7efed86e3d 100644 GIT binary patch delta 7200 zcmai&RX~&tpl#{y2I^o?pjUko)-{E~ 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Each of these experiments had the sensing system recording at the same height and the same angle. This data did contain some elements of variability, such as both sitting and standing occupants. However, further exploration of how the results differ based on the sensor's viewing angle or distance from the ground would provide valuable information. -Priorities for new experiments include exploring more than three people, investigating classification using classes that encompass ranges of people (e.g. ``1-2 persons'', ``2-4 persons'', ``4-8 persons'', ``8+ Person''), and investigating how placing the sensing system at different angles affects the accuracy of the collected data. +Priorities for new experiments include exploring more than three people, investigating classification using classes that encompass ranges of people (e.g. ``1-2 persons'', ``2-4 persons'', ``4-8 persons'', ``8+ Person''), and investigating how placing the sensing system at different angles affects the accuracy of the collected data. Additionally, investigating the limits of the sensor's ability to distinguish large numbers of people with the limited number of pixels available. \subsection{Different Feature Vectors} Exploring how different subsets of the three current features, or possibly new features derived from the thermal capture, affect the accuracy of the machine learning algorithms may demonstrate interesting results. We believe that experimenting with features that represent the abstract ``shape'', or ``roundness'' of connected components is a particularly promising area of research. @@ -69,7 +69,7 @@ \subsection{Improving Robustness} \subsection{Field-Of-View Modifications} Several different techniques could be used to improve upon the field-of-view limitations of the \mlx, and exploring them and their cost/complexity implications would be useful. The first of these is applying a lens to the sensor, effectively expanding the field-of-view, but at the cost of distorting the image. Compensating for this distortion while maintaining accuracy presents an intriguing problem. -In another direction, using a motor with the \mlx to ``sweep'' a room, and thereby constructing a larger image of the space could also resolve the field-of-view issues. However, this approach also presents problems in stitching the images together in a sensible way. Problems include the lens distortion caused by rotating the sensor and cases where a fast-moving object may be represented multiple times in a stitched capture. +In another direction, using a motor with the \mlx to ``sweep'' a room, and thereby constructing a larger image of the space could also resolve the field-of-view issues. However, this approach also presents problems in stitching the images together in a sensible way. Problems include the lens distortion caused by rotating the sensor, potential thermal distortions caused by the motor, increased energy consumption, and cases where a fast-moving object may be represented multiple times in a stitched capture. \subsection{New Sensors} During this project, an updated version of our sensor, the MLX90621, was released. This version (at a similar price point) doubles the field-of-view in both the horizontal and vertical directions, addressing many of the problems encountered with the size of detection area in low-ceiling rooms. This version offers nearly complete backwards compatibility with the MLX90620. Updating the project code-base to support it and re-running the experiments with the increased field-of-view to determine how much of an improvement it is would be interesting. diff --git a/design/design.pdf b/design/design.pdf index 3c1d205fd5ae4dc30f7d5d3546eba98e44de26fe..743816836974b0909ee35e3baa24cd46fe28f1e9 100644 GIT binary patch delta 8089 zcmajkRZN`?ur6TSVd3t@r8ogM-JL>lDGtTmT@NScWZ&+K zCz(v%`*)JL4E+y!`9mx%3=e-woHYpp;I6IUy2^{zwXb!uqjnmpg>|bfCrhuCev=r{ zJS({t#4>Hz6yG8#|9#cSsG|j>*X1b=1t%^fqN=P3DmTAfY2O(bJj@$P?~AW zm7vQ7_R+g-9LZ&-RAx$u1nIia!wf;*`o$L9JPA6E8s@T1p~@vKpMPjn%=3^M)i$oR z$$x5J-?1hpT7C-$8gl+fa#`amJ~#eoAW6^uYZ$X03F`JVBwrv|G^OWX=YGXQy0V|k z)t6phS7Gq#6OF>j-x0)q9&$GZ-qpOFzSlJzr;Xcopm~F!;a5&x?bT>z$(zjG$=1qP z2qt@E;-%h}RJ=Vw@;`VG8ozTuNF?VO^A16x{g;(*MG- za=?z=01G3&WTxo^I;=_~E;>tt^zigR_Q)*hZ+9AFIK16&=`bHX+{Ws)!83$fx8vprqzlewAfLbgQT%K{ zc}4|QKLD%#(}ORf~zM|;rPFYQ4h(5j)o zO8HnV7UobM`=3rOv=(dokPhWp0<;v3MKJ|or%*M=ryB)##4;a@`~rM_D+b$V*+}e+ 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loB;~sh=_=okMCt~U!u3u)orS3>KaGXRkq2<;EeRQ{V%oi4O0LB diff --git a/evaluation/evaluation.tex b/evaluation/evaluation.tex index 46a2373..4188ccc 100644 --- a/evaluation/evaluation.tex +++ b/evaluation/evaluation.tex @@ -57,7 +57,7 @@ \subsection{Sensitivity} This phenomenon has several possible causes. One likely explanation is that each individual pixel detects objects radiating at larger angles of incidence to be colder than they actually are. As the object enters a pixel's effective field of view, it will radiate into the pixel at an angle that is at the edge of the pixel's ability to sense, with this angle slowing decreasing until the hot object is directly radiating into the pixel's sensor, causing a peak in the temperature reading. As the object leaves the individual element's field of view, the same happens in reverse. -This phenomena is not anticipated to impact our intended use case, as it only strongly affects objects at pixel or sub-pixel size. In our experimental conditions the sensor will not be sufficiently distant that humans could be detected as such sizes. +This phenomena is not anticipated to impact our intended use case, as it only strongly affects objects at pixel or sub-pixel size. In our experimental conditions the sensor will not be sufficiently distant that humans could be detected as such sizes, thus the accuracy of their readings will not be compromised by this phenomenon. While it is possible that smaller radiant objects (such as pets) could be affected by this, the effect should not cause their mis-identification as an occupant. \begin{figure} \centering @@ -105,13 +105,13 @@ \subsection{Executing Weka Tests} Secondly, they use a $k$-Nearest Neighbours (KNN) where $k=5$, with the distance between features being determined by their Euclidean distance. For determining the class label, higher weightings are given to training points inversely to their distance from the point being classified. -Weka's ``iBk'' function is used to perform a KNN calculation, configuring \texttt{distanceWeighting} to be ``Weight by 1-distance'' and \texttt{KNN} to be 5, to make the classification as similar in function to the ThermoSense approach as is possible. ThermoSense does not specify what validation technique they used, so we elected to use a 10-fold cross-validation. +Weka's ``iBk'' function is used to perform a KNN calculation, configuring \texttt{distanceWeighting} to be ``Weight by 1-distance'' and \texttt{KNN} to be 5, to make the classification as similar in function to the ThermoSense approach as is possible. ThermoSense does not specify what validation technique they used, so we elected to use a 10-fold cross-validation. We limit the scope of our study to $k = 5$. Thirdly, they use a Linear Regression model of $y = \beta_A A + \beta_S S + \beta $, whereby $A$ is the number of active pixels, $S$ is the size of the largest connected component, and the $\beta$ values represent the corresponding coefficients. They opt to exclude the third feature, the number of connected components, as their testing indicates that excluding it minimizes the Root Mean Squared Error (RMSE) further. -We use Weka's ``LinearRegression'' function, and exclude the number of connected components (\texttt{numconnected}) attribute from the feature vector list, as ThermoSense does, to attempt to match this approach. +We use Weka's ``LinearRegression'' function, and exclude the number of connected components (\texttt{numconnected}) attribute from the feature vector list, as ThermoSense does, to attempt to match this approach. We limit the scope of our study to Linear Regressions that exclude the number of connected components. -In addition to the above three techniques, we use our own techniques (included in \Fref{tab:methods:params}). Our techniques are predominately common in use, with their general theory described in Appendix \ref{chap:appendix:classification}. One algorithm that isn't as common that we have chosen is the KStar (K*) algorithm, which we describe in further detail here: +In addition to the above three techniques, we use our own techniques (included in \Fref{tab:methods:params}). Our techniques are predominately common in use, with their general theory described in Appendix \ref{chap:appendix:classification}. One algorithm that is not as common that we have chosen is the KStar (K*) algorithm, which we describe in further detail here: The K* algorithm, developed by Cleary and Trigg~\cite{cleary1995k} presents a different approach to $k$-nearest Neighbours type algorithms. With K* the distance used to compare similar points is not the Euclidean distance, but rather an entropic distance, a measure of how much effort is required to convert one example into another. This has several positive effects; it makes the algorithm more robust to missing values and enables the classifier to output either a numeric or nominal result. @@ -239,7 +239,7 @@ \subsection{Classification} \label{tab:results:set1} \end{table} -Significant care was taken to ensure that the same classification parameters were used between our experiments and those performed in ThermoSense to provide as accurate as possible a comparison between our results. However, there were some ambiguities with the ThermoSense results that have made it more difficult to determine which parameters to choose. In particular, with reference to the $k$-Nearest Neighbours tests (KNN), it was ambiguous within the ThermoSense paper as to if they had elected to use a nominal classification or a numeric classification for this data. +Significant care was taken to ensure that the same classification parameters were used between our experiments and those performed in ThermoSense to provide as accurate as possible a comparison between our results. However, there were some ambiguities with the ThermoSense results that have made it more difficult to determine which parameters to choose. In particular, with reference to the $k$-Nearest Neighbours tests (KNN), it was ambiguous in the ThermoSense paper as to whether this data used a nominal classification or a numeric classification. Because of this, four tests were performed overall to replicate the ThermoSense results as closely as possible; KNN tests for both numeric and nominal representations of data, a Multi-Layer Perceptron numeric test (MLP) and a Linear Regression numeric test (Lin Reg). With these tests we found that our prototype did not achieve comparable results. ThermoSense reported correlation coefficients ($r$) of around 0.9 for their MLP and Lin Reg tests, however we could not replicate these results, with our best being 0.69 and 0.59 respectively. We were also unable to replicate the low Root Mean Squared Errors (RMSEs) reported by ThermoSense, with their RMSEs for KNN, MLP and Lin Reg being 0.346, 0.385 and 0.409 respectively, while ours were 0.364 (KNN Nominal Case), 1.123 (KNN Numeric Case), 0.592 (MLP) and 0.525 (Lin Reg). Our numeric KNN test performed worse than the 0-R benchmark for numeric tests, indicating an exceedingly poor classification result, with it achieving an RMSE of 1.123 vs. the 0-R's 0.651. @@ -292,9 +292,9 @@ \subsection{Classification} In the case of KNN, a selection of the nearest neighbours may not be indicative of the feature vector's class membership. A similar idea applies in the case of SVM, the aforementioned plots suggest that there may be no clear hyperplane through which the different classes of data can be separated. -The success of entropy-based methods suggests that the separation of classes is less of an issue when distance is measured with entropy, as in the case of K*, or when entropy is used to divide the dataset, as in the case of C4.5. In \Fref{fig:correctplots} we can see a comparison of the predictions of the SVM algorithm against that of K*. For the central part of the graph (20 $<$ Active pixels $<$ 35), the predictive power of both algorithms appears similar. However in the left and right parts of the graph, we can see that SVM fails to correctly separate the classes. This suggests that SVM's use of support vectors to split the feature space is ill-suited to the overlapping features of the dataset, and that K*'s entropy-based approach is able to find more suitable splits. +The success of entropy-based methods suggests that the separation of classes is less of an issue when distance is measured with entropy, as in the case of K*, or when entropy is used to divide the dataset, as in the case of C4.5. In \Fref{fig:correctplots} we can see a comparison of the predictions of the SVM algorithm against that of K*. For the central part of the graph (20 $<$ Active pixels $<$ 35), the predictive power of both algorithms appears similar. However in the left and right parts of the graph, we can see that SVM fails to correctly separate the classes. This suggests that SVM's use of support vectors to split the feature space is ill-suited to the overlapping features of the dataset, and that K*'s entropy-based approach is able to find more suitable splits in the less immediately separable datasets analysed herein. -Due to the white box nature of decision trees, it is possible for us to further example the tree generated by the C4.5 algorithm to determine how sensible the branches are. We can see from \Fref{lst:tree} that C4.5 considers the number of active pixels to be the strongest indicator of the number of occupants. This makes intuitive sense, as the number of active pixels should be the most general descriptor of the number of occupants. This is followed by the size of largest connected component, and the number of connected components. Overall the decisions made by the C4.5 tree appear sensible, although in some places it appears the tree had few cases to form branches with, which could be rectified with an increase in the size of the dataset. +Due to the white box nature of decision trees, it is possible for us to further examine the tree generated by the C4.5 algorithm to determine how sensible the branches are. We can see from \Fref{lst:tree} that C4.5 considers the number of active pixels to be the strongest indicator of the number of occupants. This makes intuitive sense, as the number of active pixels should be the most general descriptor of the number of occupants. This is followed by the size of largest connected component, and the number of connected components. Overall the decisions made by the C4.5 tree appear sensible, although in some places it appears the tree had few cases to form branches with, which could be rectified with an increase in the size of the dataset. Our worst selected technique was Naive Bayes. It is not unusual that it performed so poorly, as the ``Naive'' part of the technique is an assumption of independence between the different features input, which is clearly false with our features. All three of our features relate to the same underlying graph and are most definitely correlated with each other. @@ -415,7 +415,7 @@ \subsection{Energy Efficiency} \label{tab:lives} \end{table} -As discussed in \fref{subsec:energy}, the prototype initially appears comparatively power hungry, as the ThermoSense system reported a power consumption of $\sim$4.8~mA, approximately $10\times$ less. However, making fair comparisons is difficult, as their data is incomplete. The 4.8~mA figure they quote is an average consumption figure taking into account all aspects of the wake/sleep cycle. No breakdown on wake times, sleep or wake current was provided, making direct comparisons on those metrics difficult. We examined the datasheets of their components and estimate a voltage of 3.3~V to generate our average milliwatt figure. +As discussed in \fref{subsec:energy}, the prototype initially appears comparatively power hungry, as the ThermoSense system reported a power consumption of $\sim$4.8~mA, $1/10$ of the size. However, making fair comparisons is difficult, as their data is incomplete. The 4.8~mA figure they quote is an average consumption figure taking into account all aspects of the wake/sleep cycle. No breakdown on wake times, sleep or wake current was provided, making direct comparisons on those metrics difficult. We examined the datasheets of their components and estimate a voltage of 3.3~V to generate our average milliwatt figure. For our prototype sensing system, we restricted our adherence to low-power consumption to an architectural level, rather than making specific hardware and software changes. For our comparisons, we have elected to use a 50~watt-hour~(Wh) battery as a measure, as there exist inexpensive batteries in that class weighing around 300~grams \cite{AdafruitBattery} that provide a good balance between capacity and weight. 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