In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

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Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing’s HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model.

In industrial monitoring and control applications, a server often has to send a command to a node or group of nodes in wireless sensor networks. Flooding achieves high reliability of message delivery by allowing nodes to take the redundancy of receiving the identical message multiple times. However, nodes consume much energy due to this redundancy and the long duty cycle. A reliable slotted broadcast protocol (RSBP) tackles this problem by allocating a distinct broadcast slot (BS) to every node using a tree topology. Not only does it remove collision, but it also minimizes energy consumption such that every node remains active only during its parent’s broadcast slot and its own broadcast slot to receive and rebroadcast a message, respectively. However, it suffers from low reliability in harsh environments due to the compete removal of redundancy and low responsiveness to the changes in network topology due to the global scheduling of slots. Our approach allocates one distinct broadcast sharable slot (BSS) to each tree level, thus making a BSS schedule topology-independent. Then, nodes at the same level compete to rebroadcast a message to nodes at one level higher within the BSS, thus allowing the redundancy. In addition, it uses a slot-scheduled transmission within BSS that can further improve reliability by reducing message collisions and also enables the precise management of energy. According to simulations and experiments, the proposed approach can achieve high reliability comparable to flooding and low-energy consumption comparable to RSBP.

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