Use Of Source Separation Research Articles

Facing Up to Waste: How Can Hotel Managers in Kerbala, Iraq, Help the City Deal with its Waste Problem?

Every year, religious festivals attended by around 20 million pilgrims, take place in the city of Kerbala, Iraq. Management of the large amounts of waste which are generated pose severe problems to city authorities due to the negative impacts on the environment, human health and the attractiveness of the city. Minimization of the generated waste is one of the most economical approaches and one in which the public plays a key role regarding its success. However, waste management system currently applied in Kerbala lacks the use of source separation as well as recycling. Therefore, this study aims to investigate hoteliers’ knowledge and willingness to participate in waste source separation and recycling scheme during major festivals, which represent a new contribution to the knowledge. To achieve this goal, a survey was conducted with 150 respondents during one major religious event, over 20 days. Interviews and questionnaires were employed to collect the data. Questions were designed to collect information about knowledge of the environment, willingness to participate, preferred sorting alternatives and the actions that can motivate hoteliers and pilgrims to be involved in the program. The outcome of this research clearly indicates that the majority of respondents have poor environmental awareness. However, 68% were willing to participate in the WSS program. The provision of recycling containers was identified as the highest priority to encourage waste separation. Therefore, a set of actions has been proposed to support the Kerbala waste management authority adopt a waste separation plan during religious events.

Blind source separation of ex-vivo aorta tissue multispectral images.

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method's performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue.

Signal processing evaluation of myoelectric sensor placement in low‐level gestures: sensitivity analysis using independent component analysis

AbstractSurface electromyogram (sEMG) is a technique in which electrodes are placed on the skin overlying a muscle to detect the electrical activity. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one‐to‐one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity‐independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent component analysis (ICA) provides the perfect answer of separating skin surface recordings into a set of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor placements in sEMG. The purpose of this paper is to describe the use of source separation for sEMG using ICA. The actual use in practical sEMG experiments is demonstrated, when the number of recording channels for electrical muscle activities is varied.