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 them is purely based on the acoustic signal. However, that method still has a weakness, wherein it is difficult to separate the signal generated by the termite or the noise from the environment. The combination of the feature extraction at the acoustic signal and the classification model is expected to overcome the weakness. In this investigation, we inserted 220 subterranean termites Coptotermes curvignathus into pine wood for feeding activity and observed its acoustic signal. In addition, three acoustic features (i.e., short-term energy, entropy and zero moment power) were proposed to recognize the termite's acoustic signal. Subsequently, these features will be analyzed and combined with discriminant analysis to produce the robust classification model. According to the numerical results, the integrated discriminant analysis and the acoustic feature in our termite detection system has an 83.75% accuracy.

Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Most municipal solid waste (MSW) is found to be dominated by organic debris, which has excellent potential as an energy source. However, the main problems of this material are poor planning, urban expansion, and lack of management skills. All these problems are presently being encountered by the regional governments of Jakarta, Bogor, Depok, Tangerang, and Bekasi city (known locally as Jabodetabek), Indonesia. In the MSW management system, a vital planning protocol is reportedly assessing suitable landfill sites for energy generation, although this selection process is still a complex task that should consider various factors, such as environmental, social and safety, and economic variables. Therefore, this study aims to examine various factors in determining a suitable location for landfills. It also aims to identify the various factors required for MSW energy generation. Based on this study, a multicriteria decision analysis (MCDA) approach was applied to weigh the factors determining the appropriate location. This approach is popular in decision-making due to evaluating the complexity of multidimensionality factors. The results showed that 3 factors and 14 subfactors were formulated and structured in the MCDA hierarchy, with their information obtained to create pairwise comparisons by 10 involved experts. In this study, the MCDA output was the weight value associated with a systematic priority level, indicating that the environment was the highest factor in determining a suitable landfill site for energy generation. In addition, the weight factors were used for overlay analysis, in determining the suitable site for future energy generation studies.