Treatment efficacy of algae-based sewage treatment plants

Lagoons have been traditionally used in India for decentralized treatment of domestic sewage. These are cost effective as they depend mainly on natural processes without any external energy inputs. This study focuses on the treatment efficiency of algae-based sewage treatment plant (STP) of 67.65 million liters per day (MLD) capacity considering the characteristics of domestic wastewater (sewage) and functioning of the treatment plant, while attempting to understand the role of algae in the treatment. STP performance was assessed by diurnal as well as periodic investigations of key water quality parameters and algal biota. STP with a residence time of 14.3 days perform moderately, which is evident from the removal of total chemical oxygen demand (COD) (60 %), filterable COD (50 %), total biochemical oxygen demand (BOD) (82 %), and filterable BOD (70 %) as sewage travels from the inlet to the outlet. Furthermore, nitrogen content showed sharp variations with total Kjeldahl nitrogen (TKN) removal of 36 %; ammonium N (NH4-N) removal efficiency of 18 %, nitrate (NO3-N) removal efficiency of 22 %, and nitrite (NO2-N) removal efficiency of 57.8 %. The predominant algae are euglenoides (in facultative lagoons) and chlorophycean members (maturation ponds). The drastic decrease of particulates and suspended matter highlights heterotrophy of euglenoides in removing particulates.

Study of wrist pulse signals using time domain spatial features

Blood travels throughout the body and thus its flow is modulated by changes in body condition. As a consequence, the wrist pulse signal contains important information about the status of the human body. In this work we have employed signal processing techniques to extract important information from these signals. Radial artery pulse pressure signals are acquired at wrist position noninvasively for several subjects for two cases of interest, viz. before and after exercise, and before and after lunch. Further analysis is performed by fitting a bi-modal Gaussian model to the data and extracting spatial features from the fit. The spatial features show statistically significant (p < 0.001) changes between the groups for both the cases, which indicates that they are effective in distinguishing the changes taking place due to exercise or food intake. Recursive cluster elimination based support vector machine classifier is used to classify between the groups. A high classification accuracy of 99.71% is achieved for the exercise case and 99.94% is achieved for the lunch case. This paper demonstrates the utility of certain spatial features in studying wrist pulse signals obtained under various experimental conditions. The ability of the spatial features in distinguishing changing body conditions can be potentially used for various healthcare applications. (C) 2015 Elsevier Ltd. All rights reserved.