Muhammad Burhan Khan

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.

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.

Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

Abstract. Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.

Segmentation assessment of activated sludge flocs at different magnifications for wastewater treatment

Activated sludge process form an important part of wastewater treatment plant with domestic effluent. The variations in the state of the process are appeared as those in the size and structure of flocs and filaments found in the wastewater samples from aeration tank of secondary treatment. The normal operation requires proper settling of flocs in the secondary clarifier, which is affected by problem of bulking and pin point flocs. Conventional physico-chemical methods take a lot of time to detect the abnormal operation, consequently leaving insufficient time for precautionary measures. Image processing and analysis of microscopic images can offer a time-efficient alternative to monitor the operation of activated sludge process. Segmentation is a necessary part of image processing and analysis for identification of regions of interest in the image, and its acceptable accuracy is pre-requisite of the morphological analysis. In this paper, three segmentation techniques, fuzzy cmeans, k-means and Otsu thresholding, were used to segment flocs in microscopic images of samples taken from aeration tank of activated sludge process. The performance of the segmentation algorithms was evaluated for images taken at four different objective magnifications of microscope, using metrics of global consistency error (GCE), random index (RI) and variation of information (VI). The performance metrics were evaluated by comparing the segmented images with the approximation of ground truth images. Finally, the effect of magnification was investigated on the image segmentation and analysis procedure and observed that the size of floc, perceptible to the image segmentation and analysis procedure is greater and more precise at higher magnification.

Illumination Compensated Segmentation of Microscopic Images of Activated Sludge Flocs

Image processing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plant. However its effectiveness is dependent on performance of the segmentation algorithms. The activated sludge plant is monitored by image processing and analysis of images acquired through trinocular microscope. The sample observed under microscope is collected from aeration tank of the plant. In this paper, a segmentation technique with integrated illumination compensation is proposed for the microscopic images of the activated sludge samples. The illumination noise was modeled and estimated as Gaussian distribution symmetric about a threshold value determined by global Otsu thresholding algorithm. The performance of the algorithm was evaluated using time required for segmentation, Rand index, accuracy and quantification of flocs. In order to compare with the state-of-the-art algorithms, gold approximations of ground truth images were manually prepared. The performance was assessed by combining the evaluation metrics in an integrated perspective. The proposed algorithm exhibits better performance in terms of both integrated and non-integrated perspectives.

Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Switched hybrid position control of elastic systems with backlash

Backlash nonlinearity, caused by physical disconnect of mating gears, becomes a problem in the presence of position or speed mismatch between the sides of the mechanical transmission link. In this paper, switched hybrid model of a generalized system has been used, and all fixed model configuration of multiple model adaptive control has been adopted for position control of elastic system with backlash nonlinearity. A number of different control strategies were suggested availing the inherent architecture of multiple model adaptive control and taking into account the possibilities of sensor locations. All the strategies do not require a priori knowledge of backlash angle, eliminating the need to estimate the angle. The control techniques are compared on the basis of transient response, steady state response, control effort and frequent switching of system modes.

Generalized classification modeling of activated sludge process based on microscopic image analysis

ABSTRACT The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

Contactless heart rate monitor for multiple persons in a video

In this paper a contactless heart rate (HR) measurement system has been proposed. Algorithm has been developed to process video in real-time captured using a webcam. In the first step face and region of interests (cheeks) are detected. RGB color model is used for analysis; hence three color traces, R, G, B are obtained for the video. Fast Fourier transform is applied to the traces and peak frequency is detected after band pass filtering. The HR was calculated using the peak frequency. The results obtained from three channels of the RGB model are compared for their accuracy. It ahs been observed that the green channel gives better results. The algorithms were implemented; for multiple subjects in a frame and different illumination conditions. The algorithms were also tested; for different distances between the subject and the camera. The minimum percentage error of 3.1% is achieved; in the presence of movement and multiple persons at the relative distance of 70 cm.

Estimation of sludge volume index (SVI) using bright field activated sludge images

Image processing and analysis is a potential tool for monitoring of activated sludge wastewater treatment plant. One of the important parameters to track the performance of activated sludge plant is sludge volume index (SVI). In this paper, image analysis based modeling is used to estimate the sludge volume index. Bright field microscopic images of activated sludge were segmented by integrating four algorithms to skim any possible failures of any of them. The morphological parameters for activated sludge flocs have been extracted from the segmented images. Seven classes were identified for image analysis parameters with respect to range of equivalent diameter of activated sludge flocs. The process resulted into 134 image analysis parameters and seven classes. The feature selection is done by two procedures: correlation method and stepwise linear regression. The stepwise linear regression is automated process which selected 6 parameters with adjusted correlation of 95.1%. The results showed that image analysis based modeling with as small as six parameters can be used to predict the sludge volume index. Finally, three out of seven classes are identified which can contribute to the estimation of SVI.