Information fusion by utilizing multiple distributed sensors is studied in this work. Extending the classical parallel fusion structure by incorporating the fading channel layer that is omnipresent in wireless sensor networks, we derive the likelihood ratio based fusion rule given fixed local decision devices. This optimum fusion rule, however, requires perfect knowledge of the local decision performance indices as well as the fading channel. To address this issue, two alternative fusion schemes, namely, the maximum ratio combining statistic and a two-stage approach using the Chair-Varshney fusion rule, are proposed that alleviate these requirements and are shown to be the low and high signal-to-noise ratio (SNR) equivalents of the likelihood-based fusion rule. To further robustify the fusion rule and motivated by the maximum ratio combining statistics, we also propose a statistic analogous to an equal gain combiner that requires minimum a priori information. Performance evaluation is performed both analytically and through simulation.

Abstract-Sensor censoring has been introduced for reduced communication rate in a decentralized detection system where decisions made at peripheral nodes need to be communicated to a fusion center. In this letter, the fusion of decisions from censoring sensors transmitted over wireless fading channels is investigated. The knowledge of fading channels, either in the form of instantaneous channel envelopes or the fading statistics, is integrated in the optimum and suboptimum fusion rule design. The sensor censoring and the ensuing fusion rule design have two major advantages compared with the previous work. 1) Communication overhead is dramatically reduced. 2) It allows incoherent detection, hence, the phase information of transmission channels is no longer required. As such, it is particularly suitable for wireless sensor network applications with severe resource constraints.Index Terms-Censoring sensor, decision fusion, fading channels, wireless sensor networks.

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

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In recent years, wild morel mushroom species have begun to be widely cultivated in China due to their high edible and medicinal values. To parse the medicinal ingredients, we employed the technique of liquid-submerged fermentation to investigate the secondary metabolites of Morehella importuna. Two new natural isobenzofuranone derivatives (1–2) and one new orsellinaldehyde derivative (3), together with seven known compounds, including one o-orsellinaldehyde (4), phenylacetic acid (5), benzoic acid (6), 4-hydroxy-phenylacetic acid (7), 3,5-dihydroxybenzoic acid (8), N,N′-pentane-1,5-diyldiacetamide (9), and 1H-pyrrole-2-carboxylic acid (10), were obtained from the fermented broth of M. importuna. Their structures were determined according to the data of NMR, HR Q-TOF MS, IR, UV, optical activity, and single-crystal X-ray crystallography. TLC-bioautography displayed that these compounds possess significant antioxidant activity with the half DPPH free radical scavenging concentration of 1.79 (1), 4.10 (2), 4.28 (4), 2.45 (5), 4.40 (7), 1.73 (8), and 6.00 (10) mM. The experimental results would shed light on the medicinal value of M. importuna for its abundant antioxidants.