Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

Various methods for termite detection have been developed, one of which is purely based on their acoustic signals. However, this method has a weakness, as it is difficult to separate the signals generated by the termites from noise in the environment. A combination of the feature extraction of the acoustic signals and a classification model is expected to overcome this weakness. In this investigation, we inserted 220 subterranean termites Coptotermes curvignathus into pine wood for feeding activity and observed their acoustic signals. In addition, three acoustic features (shortterm energy, entropy and zero moment power) were proposed to recognize the termites' acoustic signals. Subsequently, these features were analyzed and combined with discriminant analysis to produce a robust classification model. According to the numerical results, the integrated discriminant analysis and the acoustic feature in our termite detection system has an accuracy of 83.75%.

Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples.

The Quality of Service (QoS) on the traffic environment with high mobility, dynamic topology, and change trajectory is one of the challenges in the use of Vehicular Ad-Hoc Networks (VANET) Routing Protocols. PUMA routing protocol presents multicast work mechanism based on shared mesh topology. It always dynamically adapt to VANET traffic conditions.In this paper, we evaluated the PUMA routing protocol performance using the Manhattan Mobility Model with the effect of the Nakagami fading distribution in VANET. We also analyse the average node distribution in the tagged vehicle communication range using Poisson distribution. We use the NS 2.34 and VanetMobiSim for the simulation and evaluation of the performance. The performance results will be analyzed by varying the traffic parameters in the Manhattan mobility model from low to high traffic condition.The simulation result shows that the communication range under the Nakagami fading distribution affected the Quality of Service (QoS) in VANET. We found that the delay is higher compared with those in the condition without Nakagami fading. The throughput is also lower compared with those in the condition without Nakagami fading.

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