This paper presents and evaluates a new terahertz metamaterial absorber (MMA) for sensing applications. Because of its unique properties, metamaterial-based sensors are widely employed in a variety of applications. The reported structure comprises of two identical metallic patches, a dielectric spacer and a ground metal plane. The finite element approach has been utilized to simulate and analyse the design. It is found that the MMA offered a prominent resonant peak with near 100% absorbance at frequency 4.5 THz due to the resultant effect of coupling between the two identical patches. In addition, surface current distribution, absorption mechanism and structural parametric analysis has also been investigated. The peak is designated as ‘A’, with a line width of 0.02 THz and a quality factor (Q-factor) of 225, which is sensitive to the refractive index of the environment (RI). As a result of its highly sensitive sensing capabilities, the proposed design can be employed as a sensor for refractive index, having 1.6 THz per refractive index unit (RIU) sensitivity and figure of merit (FoM) of 80 in terms of change in RI of the environment. The majority of biomedical samples have RI of 1.3 to 1.36, which is worth highlighting. Thus, biomedical applications may be possible with the suggested sensor.

Because the possibility of array element failure during operation cannot be ruled out [1], selfrecoverable antenna array systems have recently been receiving considerable attention in modern radar technology. Thus, an approach to maintaining the radiation properties of the array by resynthesizing the excitations of the functioning elements is preferable to replacing failed elements. This article describes the implementation of two optimization algorithms to find a suitable solution for optimal beam pattern. The algorithms, based on metaheuristics, are called particle swarm optimization (PSO) and bacteria foraging optimization (BFO). Of these two techniques, BFO is relatively new to the antenna community. We compare the performance of both algorithms in terms of their ability to suppress the sidelobe level (SLL) and null steering in failed antenna arrays to achieve a graceful degradation in the radiation pattern.

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects on the network if they are not diagnosed and informed of the fault status to other nodes. Fault diagnosis is difficult when the nodes behave at fault sometimes and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set show that the new method provides better detection accuracy (DA) with the minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in a wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR, and 0% FPR if the probability of the data from faulty node exceeds 25%.