Sequential land cover classification

Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research effort in land cover classification has gone into the development of increasingly robust and accurate (and also increasingly complex) classifiers by constructing–often in an ad hoc manner–multispectral, multitemporal, multisource classifiers using modern machine learning techniques such as artificial neural networks, fuzzy-sets, and expert systems. However, the focus has always been (almost exclusively) on increasing the classification accuracy of newly developed classifiers. We would of course like to perform land cover classification (i) as accurately as possible, but also (ii) as quickly as possible. Unfortunately there exists a tradeoff between these two requirements, since the faster we must make a decision, the lower we expect our classification accuracy to be, and conversely, a higher classification accuracy typically requires that we observe more samples (i.e., we must wait longer for a decision). Sequential analysis provides an attractive (indeed an optimal) solution to handling this tradeoff between the classification accuracy and the detection delay–and it is the aim of this study to apply sequential analysis to the land cover classification task. Furthermore, this study deals exclusively with the binary classification of coarse resolution MODIS time series data in the Gauteng region in South Africa, and more specifically, the task of discriminating between residential areas and vegetation is considered.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

Uncovering temporal structure in hippocampal output patterns.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory

Land cover separability analysis of MODIS time series data using a combined simple harmonic oscillator and a mean reverting stochastic process

It is proposed that the time series extracted from moderate resolution imaging spectroradiometer satellite data be modeled as a simple harmonic oscillator with additive colored noise. The colored noise is modeled with an Ornstein–Uhlenbeck process. The Fourier transform and maximum-likelihood parameter estimation are used to estimate the harmonic and noise parameters of the colored simple harmonic oscillator. Two case studies in South Africa show that reliable class differentiation can be obtained between natural vegetation and settlement land cover types, when using the parameters of the colored simple harmonic oscillator as input features to a classifier. The two case studies were conducted in the Gauteng and Limpopo provinces of South Africa. In the case of the Gauteng case study, we obtained an average for single-band classification, while standard harmonic features only achieved an average . In conclusion, the results obtained from the colored simple harmonic oscillator approach outperformed standard harmonic features and the minimum distance classifier.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443ai201

Cavalieri integration

We use Cavalieri’s principle to develop a novel integration technique which we call Cavalieri integration. Cavalieri integrals differ from Riemann integrals in that non-rectangular integration strips are used. In this way we can use single Cavalieri integrals to find the areas of some interesting regions for which it is difficult to construct single Riemann integrals. We also present two methods of evaluating a Cavalieri integral by first transforming it to either an equivalent Riemann or Riemann-Stieltjes integral by using special transformation functions h(x) and its inverse g(x), respectively. Interestingly enough it is often very difficult to find the transformation function h(x), whereas it is very simple to obtain its inverse g(x).http://www.tandfonline.com/loi/tqma20hb201

An inductive approach to simulating multispectral MODIS surface reflectance time series

In this paper, a first order MODIS time series simulator, which uses a Colored Simple Harmonic Oscillator, is proposed. The simulated data can be used to augment data sets so that data intensive classification and change detection algorithms can be applied without enlarging the available ground truth data sets. The simulator’s validity is tested by simulating data sets of natural vegetation and human settlement areas and comparing it to the ground truth data in the Gauteng province located in South Africa. The difference found between the real and simulated data sets, which is reported in the experiments is negligent. The simulated and real world data sets are compared by using a wide selection of class and pixel metrics. In particular the average temporal Hellinger distance between the real and simulated data sets is 0.2364 and 0.2269 for the vegetation and settlement class respectively, while the average parameter Hellinger distance is 0.1835 and 0.2554 respectively.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

Using Page's cumulative sum test on MODIS time series to detect land-cover changes

Human settlement expansion is one of the most pervasive forms of land cover change in South Africa. The use of Page’s Cumulative Sum Test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500 m MODIS time series satellite data. The method is a sequential per pixel change alarm algorithm that can take into account positive detection delay, probability of detection and false alarm probability to construct a threshold. Simulated change data was generated to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before mentioned study areas.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

Evaluating the Impact of Computerized Provider Order Entry on Medical Students Training at Bedside: A Randomized Controlled Trial

Objective: To evaluate the impact of computerized provider order entry (CPOE) at the bedside on medical students training. Materials and Methods We conducted a randomized cross-controlled educational trial on medical students during two clerkship rotations in three departments, assessing the impact of the use of CPOE on their ability to place adequate monitoring and therapeutic orders using a written test before and after each rotation. Students’ satisfaction with their practice and the order placement system was surveyed. A multivariate mixed model was used to take individual students and chief resident (CR) effects into account. Factorial analysis was applied on the satisfaction questionnaire to identify dimensions, and scores were compared on these dimensions. Results: Thirty-six students show no better progress (beginning and final test means = 69.87 and 80.98 points out of 176 for the control group, 64.60 and 78.11 for the CPOE group, p = 0.556) during their rotation in either group, even after adjusting for each student and CR, but show a better satisfaction with patient care and greater involvement in the medical team in the CPOE group (p = 0.035*). Both groups have a favorable opinion regarding CPOE as an educational tool, especially because of the order reviewing by the supervisor. Conclusion: This is the first randomized controlled trial assessing the performance of CPOE in both the progress in prescriptions ability and satisfaction of the students. The absence of effect on the medical skills must be weighted by the small time scale and low sample size. However, students are more satisfied when using CPOE rather than usual training

Three little pieces for computer and relativity

Numerical relativity has made big strides over the last decade. A number of problems that have plagued the field for years have now been mostly solved. This progress has transformed numerical relativity into a powerful tool to explore fundamental problems in physics and astrophysics, and I present here three representative examples. These "three little pieces" reflect a personal choice and describe work that I am particularly familiar with. However, many more examples could be made.Comment: 42 pages, 11 figures. Plenary talk at "Relativity and Gravitation: 100 Years after Einstein in Prague", June 25 - 29, 2012, Prague, Czech Republic. To appear in the Proceedings (Edition Open Access). Collects results appeared in journal articles [72,73, 122-124