Humaira Nisar

Optimization of membrane bioreactors by the addition of powdered activated carbon.

It was found that with replenishment, powdered activated carbon (PAC) in the membrane bioreactor (MBR) would develop biologically activated carbon (BAC) which could enhance filtration performance of a conventional MBR. This paper addresses two issues (i) effect of PAC size on MBR (BAC) performance; and (ii) effect of sludge retention time (SRT) on the MBR performance with and without PAC. To interpret the trends, particle/floc size, concentration of mixed liquor suspended solid (MLSS), total organic carbon (TOC), short-term filtration properties and transmembrane pressure (TMP) versus time are measured. The results showed improved fouling control with fine, rather than coarse, PAC provided the flux did not exceed the deposition flux for the fine PAC. Without PAC, the longer SRT operation gave lower fouling at modest fluxes. With PAC addition, the shorter SRT gave better fouling control, possibly due to greater replenishment of the fresh PAC.

A brain computer interface for smart home control

The aim of this study is to control home devices using a non invasive brain computer interface (BCI). The Electroencephalographic signals (EEG) recorded from the brain activity using the Emotiv EPOCH headset are interfaced with the help of mouse emulator to a graphical user interface (GUI) on the computer screen. The user will use this GUI to control various devices in a smart home. This application will be very useful especially for people with special needs.

Depth Map and 3D Imaging Applications: Algorithms and Technologies

Over the last decade, significant progress has been made in 3D imaging research. As a result, 3D imaging methods and techniques are being employed for various applications, including 3D television, intelligent robotics, medical imaging, and stereovision.Depth Map and 3D Imaging Applications: Algorithms and Technologies present various 3D algorithms developed in the recent years and to investigate the application of 3D methods in various domains. Containing five sections, this book offers perspectives on 3D imaging algorithms, 3D shape recovery, stereoscopic vision and autostereoscopic vision, 3D vision for robotic applications, and 3D imaging applications. This book is an important resource for professionals, scientists, researchers, academics, and software engineers in image/video processing and computer vision.

Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks. Extensive simulation results for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality and less complexity as compared to other ME algorithms. Implementation of proposed algorithm in JM12.2 of H.264/AVC shows reduction in computational complexity measured in terms of encoding time while maintaining almost same bit rate and PSNR as compared to Full Search algorithm.

A Fuzzy-Neural approach for estimation of depth map using focus

Depth map is used for recovery of three-dimensional structure of the object which is required in many high level vision applications. In this paper, we present a new algorithm for the estimation of depth map for three-dimensional shape recovery. This algorithm is based on Fuzzy-Neural approach using shape from focus (SFF). A Fuzzy Inference System (FIS) is designed for the calculation of the depth map and an initial set of membership functions and fuzzy rules are proposed. Then Neural Network is used to train the FIS. The training is done using back propagation algorithm in combination with the least squares method. Hence, a new set of input membership functions are generated while discarding the initial ones. Lastly, the trained FIS is used to obtain final depth map. The results are compared with five other methods including the traditional SFF method and the Focused Image Surface SFF method (FISM). Six different types of objects are used for testing the proposed algorithm.

Multiple initial point prediction based search pattern selection for fast motion estimation

A novel, computationally efficient and robust scheme for multiple initial point prediction has been proposed in this paper. A combination of spatial and temporal predictors has been used for initial motion vector prediction, determination of magnitude and direction of motion and search pattern selection. Initially three predictors from the spatio-temporal neighboring blocks are selected. If all these predictors point to the same quadrant then a simple search pattern based on the direction and magnitude of the predicted motion vector is selected. However if the predictors belong to different quadrants then we start the search from multiple initial points to get a clear idea of the location of minimum point. We have also defined local minimum elimination criteria to avoid being trapped in local minimum. In this case multiple rood search patterns are selected. The predictive search center is closer to the global minimum and thus decreases the effect of monotonic error surface assumption and its impact on the motion field. Its additional advantage is that it moves the search closer to the global minimum hence increases the computation speed. Further computational speed up has been obtained by considering the zero-motion threshold for no motion blocks. The image quality measured in terms of PSNR also shows good results.

Digital Image Processing and Analysis for Activated Sludge Wastewater Treatment

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

An adaptive block motion estimation algorithm based on spatio-temporal correlation

This paper suggests a simple scheme based on spatio-temporal neighborhood information for obtaining better estimates of motion vectors. The estimated motion vector is chosen as initial search center. This predictive search center is found closer to the global minimum. Based on the prediction, the algorithm also chooses between center-biased or uniform approach for slow or fast moving sequences. For final fine search quadrant selection algorithm is chosen that speeds up the process. Experimental results presented in this paper demonstrate the efficiency of the proposed approach.

An advanced center biased three step search algorithm for motion estimation

Recent studies show that the motion vector distribution within the search window shows a center biased behavior. Based on this fact, an advanced center biased three step search algorithm for fast block motion estimation has been proposed in this paper. The algorithm drastically reduces the computational complexity by strict application of the unimodal error surface assumption. Improved error performance has been achieved by an efficient center biased search strategy that improves the chance of getting correct motion vector. The half stop technique has been adopted to speed up the block matching process. Experimental results show that proposed algorithm has improved performance as compared to the original three step search algorithm. A good predicted image quality is also achieved.

Morphological analysis of activated sludge flocs and filaments

Purification of waste water is commonly done using the activated sludge process. The ratio of the activated sludge flocs and filamentous bacteria play a key role in the purification process of waste water. The sludge bulking or filamentous bulking is a common problem in activated sludge plants that prevents flocs to settle down. Digital imaging techniques can play an important role in monitoring activated sludge flocs and filaments in waste water treatment plants (WWTPs). In this paper, an algorithm to segment the flocs and the filaments of the microscopic sludge images captured at 4 times magnification in brightfield microscopy has been proposed. Morphological parameters, like, compactness, roundness, convexity, equivalent diameter are analyzed. Comparison with laser particle size analysis method has been done for the interpretation of the imaging results.