Chemical Classification By Monitoring Liquid Evaporation Using Extrinsic Fabry-Perot Interferometer With Microwave Photonics

Identification of liquids is essential in chemical analysis, safety, environmental protection, quality control, and research. A novel liquid identification system based on Microwave Photonics (MWP) measured time transient evaporation signals is investigated. An extrinsic Fabry-Perot Interferometer (EFPI) based optical probe using single-mode fiber (SMF) is proposed to monitor evaporation of different liquids. The MWP system is used to measure the optical path changes during liquid evaporation due to its high sensitivity, selectivity, and Signal-to-Noise Ratio (SNR). The measured S21 continuous wave (CW) time Magnitude and Phase signals were processed to extract features such as histogram and Fast Fourier Transform (FFT) peaks. Using features extracted from droplet evaporation time transient events, machine learning classification accurately identified chemicals in each liquid with an accuracy rate of over 99%, employing three algorithms: Decision Trees, Support Vector Machine (SVM), and K-nearest neighbors (KNN). The classification results demonstrate accurate liquid identification based on evaporation measurements by the MWP system

Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering

Ambient-assisted living (AAL) is promising to become a supplement of the current care models, providing enhanced living experience to people within context-aware homes and smart environments. Activity recognition based on sensory data in AAL systems is an important task because 1) it can be used for estimation of levels of physical activity, 2) it can lead to detecting changes of daily patterns that may indicate an emerging medical condition, or 3) it can be used for detection of accidents and emergencies. To be accepted, AAL systems must be affordable while providing reliable performance. These two factors hugely depend on optimizing the number of utilized sensors and extracting robust features from them. This paper proposes a generic feature engineering method for selecting robust features from a variety of sensors, which can be used for generating reliable classi cation models. From the originally recorded time series and some newly generated time series [i.e., magnitudes, rst derivatives, delta series, and fast Fourier transformation (FFT)-based series], a variety of time and frequency domain features are extracted. Then, using two-phase feature selection, the number of generated features is greatly reduced. Finally, different classi cation models are trained and evaluated on an independent test set. The proposed method was evaluated on ve publicly available data sets, and on all of them, it yielded better accuracy than when using hand-tailored features. The bene ts of the proposed systematic feature engineering method are quickly discovering good feature sets for any given task than manually nding ones suitable for a particular task, selecting a small feature set that outperforms manually determined features in both execution time and accuracy, and identi cation of relevant sensor types and body locations automatically. Ultimately, the proposed method could reduce the cost of AAL systems by facilitating execution of algorithms on devices with limited resources and by using as few sensors as possible.info:eu-repo/semantics/publishedVersio

Discriminative Feature Extraction of Time-Series Data to Improve Temporal Pattern Detection using Classification Algorithms

Time-series data streams often contain predictive value in the form of unique patterns. While these patterns may be used as leading indicators for event prediction, a lack of prior knowledge of pattern shape and irregularities can render traditional forecasting methods ineffective. The research in this thesis tested a means of predetermining the most effective combination of transformations to be applied to time-series data when training a classifier to predict whether an event will occur at a given time. The transformations tested on provided data streams included subsetting of the data, aggregation over various numbers of data points, testing of different predictive lead times, and converting the data set into a binary set of values. The benefit of the transformations is to reduce the data used for training down to only the most useful pattern containing points and clarify the predictive pattern contained in the set. In addition, the transformations tested significantly reduce the number of features used for classifier training through subsetting and aggregation. The performance benefit of the transformations was tested through creating a series of daily positive/negative event predictions over the span of a test set derived from each provided data stream. A landmarking system was then developed that utilizes the prior results obtained by the system to predetermine a “best fit” transformation to use on a new, untested data stream. Results indicate that the proposed set of transformations consistently result in improved classifier performance over the use of untransformed data values. Landmarking system testing shows that the use of prior knowledge results in selection of a near best fit transformation when using as few as 3 reference transformations

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

Background: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments

Identification of time series components using break for time series components (bftsc) and group for time series components (gftsc) techniques

Commonly in time series modelling, identifying the four time series components which are trend, seasonal, cyclical, and irregular is conducted manually using the time series plot. However, this manual identification approach requires tacit knowledge of the expert forecaster. Thus, an automated identification approach is needed to bridge the gap between expert and end user. Previously, a technique known as Break for Additive Seasonal and Trend (BFAST) was developed to automatically identify only linear trend and seasonal components, and consider the other two (i.e., cyclical and irregular) as random. Therefore, in this study, BFAST was extended to identify all four time series components using two new techniques termed Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC). Both techniques were developed by adding cyclical and irregular components to the previous BFAST technique. The performance of BFTSC and GFTSC were validated through simulation and empirical studies. In the simulation study, monthly and yearly data were replicated 100 times based on three sample sizes (small, medium, and large), and embedding the four time series components as the simulation conditions. Percentages of identifying the correct time series components were calculated in the simulation data. Meanwhile in the empirical study, four data sets were used by comparing the manual identification approach with the BFTSC and GFTSC automatic identification. The simulation findings indicated that BFTSC and GFTSC identified correct time series components 100% when large sample size combined with linear trend and other remaining time series components. The empirical findings also supported BFTSC and GFTSC, which performed as well as a manual identification approach for only two data sets exhibiting linear trend and other components combinations. Both techniques were not performing well in other two data sets displaying curve trend. These findings indicated that BFTSC and GFTSC automatic identification techniques are suitable for data with linear trend and require future extensions for other trends. The proposed techniques help end user in reducing time to automatically identify the time series component

A New Feature Selection Method Based on Class Association Rule

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed