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Cardiovascular disease is now one of the leading causes of morbidity and mortality. Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular system. Currently, there has been a lot of focus on accurately categorizing heartbeats. There is a high demand for automatic ECG classification systems to assist medical professionals. But there is a big issue in obtaining original data extensively in medical domains in rare diseases, so it is essential to have a robust solution adopting this challenge. So, we need a solution that can address the problem of tackling data insufficiency, which is a major concern nowadays for medical applications. Without having, significant training samples the overall output can be demised. But the recent works on ECG classification did not address the challenge of solving the data insufficiency label problem. To overcome this issue, we developed a new generative adversarial network-based deep learning method called HeartNet for tackling the data insufficiency problem. The proposed deep learning method is compressed by a multi-head attention mechanism on CNN architecture. The main challenge of insufficient data labels is solved by adversarial data synthesis by adopting a generative adversarial network (GAN) with generating additional training samples. It drastically improves the overall performance of the proposed method by 5-10% on each insufficient data label category. Since the training samples are increased. We evaluated our proposed method utilizing the MIT-BIH dataset. Our proposed method has shown $99.67\pm {0.11}$ accuracy and 89.24± 1.71 MCC trained with adversarial data synthesized dataset. However, we have also utilized two individual datasets as Atrial Fibrillation Detection Database and PTB Diagnostic Database to see the performance and generalization of our proposed model on ECG classification. The effectiveness and robustness of the proposed method are validated by extensive experiments, comparison, and analysis. Later on, we also highlighted some limitations of this work.

To effectively handle duplicate files, data deduplication schemes are widely used in many storage systems. Data deduplication algorithms reduce storage space by eliminating data to ensure that only single instance of data is stored in storage device. In this paper, we propose an energy efficient file synchronization scheme that provides hybrid data chunking using variable-length chunking (VLC) and fixed-length chunking (FLC). The main idea is to analyze similarities between old and new versions of data and decide which chunking method to apply in synchronizing the files. In particular, the proposed algorithm exploits the file similarity pattern for calculating the energy efficiency of chunking algorithms. We have developed an Android mobile application for file synchronization and measured energy consumption. The experiment results show that the proposed scheme helps save energy in synchronizing files, regardless of file types or amount of redundancies the files have.

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due to privacy concerns. Traditional models do not generalize on unseen cases and are also unable to preserve data privacy. Which incentivizes performance degradation in existing models with privacy limitations. To tackle generalization and privacy issues together, we introduce the framework SF-ECG, a source-free domain adaptation approach for patient-specific ECG classification. This framework does not require source data during adaptation, which solves the privacy issue during adaptation. We adopt a generative model (GAN) that learns to synthesize patient-specific ECG data in data-inefficient classes to make additional source data for imbalanced classes. Then, we use the local structure clustering method to strongly align target ECG features with similar neighbors. After seizing clustered target features, we use a classifier that is trained on source data with generated source samples, which makes the model generalizable in classifying unseen data. Empirical results under different experimental conditions in various interdomain datasets prove that the proposed framework achieves 0.8% improvements in UDA settings, along with preserving privacy and generalizability.