Application of invariant moments for crowd analysis

The advancement in technology such as the use of CCTV has improved the effects of monitoring crowds. However, the drawback of using CCTV is that the observer might miss some information because monitoring crowds through CCTV system is very laborious and cannot be performed for all the cameras simultaneously. Hence, integrating the image processing techniques into the CCTV surveillance system could give numerous key advantages, and is in fact the only way to deploy effective and affordable intelligent video security systems. Meanwhile, in monitoring crowds, this approach may provide an automated crowd analysis which may also help to improve the prevention of incidents and accelerate action triggering. One of the image processing techniques which might be appropriate is moment invariants. The moments for an individual object have been used widely and successfully in lots of application such as pattern recognition, object identification or image reconstruction. However, until now, moments have not been widely used for a group of objects, such as crowds. A new method Translation Invariant Orthonormal Chebyshev Moments has been proposed. It has been used to estimate crowd density, and compared with two other methods, the Grey Level Dependency Matrix and Minkowski Fractal Dimension. The extracted features are classified into a range of density by using a Self Organizing Map. A comparison of the classification results is done to determine which method gives the best performance for measuring crowd density by vision. The Grey Level Dependency Matrix gives slightly better performance than the Translation Invariant Orthonormal Chebyshev Moments. However, the latter requires less computational resources

Design Synthesis of 5.8 GHz Octagonal AMC on a Very Thin Substrate

This paper proposed a development of 5.8 GHz Artificial Magnetic Conductor, AMC using very thin substrate with thickness of 0.13 mm. Different shapes of PEC metallization are discussed in order to achieve the highest bandwidth of unit cell AMC. Another approaches for increasing the bandwidth is by applying the ring patch around the substrate as well as implementing the DGS ground plane. The proposed design produces 1.96 % bandwdith of unit cell. Applying the 2 x 4 unit cell arrangement gives the best result for directivity equal to 6.75 dBi, gain of 6.83 dB, efficiency = 98.41 % and return loss = -45.63 dB

THE SEVEN CASES UNSTRUCTURED TRIANGULATION TECHNIQUE FOR RADIATIVE HEAT TRANSFER APPROXIMATION IN AN ETHYLENE FURNACE CRACKER

Radiative heat distribution inside an ethylene cracker furnace is often modelled using the finite volume and finite element methods. In both cases, meshes in the form of rectangles and triangles are needed to serve as the approximating points in the domain. In this paper, a new method called the Seven Cases Unstructured Triangulation Technique (7CUTT) is proposed for meshing the domain inside the cracker furnace, integrated with the deployment of sensors on the wall to obtain the boundary value. 7CUTT is an enhanced version of the Standard Advancing Front Technique (SAFT) which two normal cases in SAFT are extended to the total of seven cases for consideration during the element creation procedure for initial mesh generation. The focus of this method is to construct the initial triangular meshes with the requirement of (1) having the location of sensors deployed along the wall as boundary nodes as well as forming boundary elements, (2) generating nodes at a certain boundary with linearly different lengths of boundary edges as interior gradation controls and (3) constructing the triangular element directly in every iteration without having to re-order the Front or delete the existing elements. There are three contributions from this paper, the first one is the introduction of seven extended cases for consideration for the element creation procedure, the second is the layer concept to generate edges with linearly different lengths and the third is the post-processing mesh procedure to improve the quality of the mesh that is suitable for 7CUTT. The final mesh is obtained once the post-processing procedure of improving the mesh quality is applied to the initial mesh. 7CUTT provides the framework for the heat to be approximated using the discrete ordinate method, which is a variant of the finite volume method. Simulation results produced using FLUENT support the findings for effectively approximating the flue gas temperature distribution, the circumferential radiative heat flux incident at the reactor coils as well as the circumferential reactor coil temperature in the furnace at the end of the study

Development of mobile based flood victims medical management system

Flood is the most devastating natural disaster Malaysia has ever seen. In Malaysia, including Sabah and Sarawak, there are 189 river basins (89 in Peninsula Malaysia, 78 in Sabah, and 22 in Sarawak), with the main rivers flowing directly into the South China Sea and 85 of them are prone to repeated floods. The projected area vulnerable to flood catastrophe is around 29,800 km2 or 9% of Malaysia's total territory, affecting nearly 4.82 million people or about 22% of the country's entire population. Humans are affected by floods in a variety of ways. Floods have the worst effect on human health because infectious illnesses spread readily during and after the flood. Furthermore, healthcare services are hampered during the flood season. It is owing to transportation and staffing difficulties, as well as the procedure of documenting flood victims' health reports. The primary goal of this initiative is to assist flood victims in terms of health. It will be easier to seek treatment, record the patient's health concerns online, and notify the hospital immediately if there are patients who need to be sent to the hospital with the information supplied. This system was created with the Android Studio platform as the user interface medium and Google Firebase as the data storage medium

Modeling physical interaction and understanding peer group learning dynamics: Graph analytics approach perspective

Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. However,observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Hence, more data-driven analysis needs to be performed to investigate the physicalinteraction in peer learning. This paper complements existing works by proposing a frameworkfor exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the grap model for more than 50 students taking part in group discussions. The experiment was conducted during a mathematics tutorial class. The physical interactions among students were captured through an online Google form and represented in a graph model. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience

On Crowd Density Estimation for Surveillance

The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of illumination due to textured brick surface, clothing and changes of weather. Image features were extracted using Grey Level Dependency Matrix, Minkowski Fractal Dimension and a new method called Translation Invariant Orthonormal Chebyshev Moments. The features were then classified into a range of density by using a Self Organizing Map. Three different techniques were used and a comparison on the classification results investigates the best performance for measuring crowd density by vision

Analysis of Optimization Medical Image Watermarking Using Particle Swarm Optimization Based on SLT

This paper propose an optimization technique, based on Slantlet Transform (SLT) and Particle Swarm Optimization (PSO). In order to improve the quality image to achieve the imperceptibility without losing robustness, an optimal scheme for embedding the secret message derived from PSO. The propose technique takes an advantages of SLT that was better time localization as an improved version of Discrete Wavelet Transform (DWT). In additional, SLT is a piecewise linear and used two zero moment in which SLT is better signal compression better than DWT and Discrete Cosine Transform (DCT). Meanwhile, PSO as a popular optimization technique and has been successed to use as a method to balance the imperceptibility and robustness, it is because PSO serves the weighting factor inside of embedding process. As we know that there is researcher ever done using SLT and PSO in watermarking, this paper used to investigate the capability of SLT-PSO in order to achieve imperceptibility without losing robustness. The comparison of this technique is displayed in this paper to show the capability of propose technique convince to improve the performance of SLT while the experiment will be implemented using medical images