Given the current emphasis on maintaining and inspecting high-rise buildings, conventional inspection approaches are costly, slow, error-prone, and labor-intensive due to manual processes and lack of automation. In this paper, we provide an automated, periodic, accurate and economical solution for the inspection of such buildings on real-world images. We propose a novel end-to-end integrated autonomous pipeline for building inspection which consists of three modules: i) Autonomous Drone Navigation, ii) Façade Detection, and iii) Model Construction. Our first module computes a collision-free trajectory for the UAV around the building for surveillance. The images captured in this step are used for façade detection and 3D building model construction. The façade detection module is a deep learning-based object detection method which detects cracks. Finally, the model construction module focuses on reconstructing a 3D model of a building from captured images to mark the corresponding cracks on the 3D model for efficient and accurate inferences from the inspection. We conduct experiments for each module, including collision avoidance for drone navigation, façade detection, model construction and mapping. Our experimental analysis shows the promising performance of i) our crack detection model with a precision and recall of 0.95 and mAP score of 0.96; ii) our 3D reconstruction method includes finer details of the building without having additional information on the sequence of images; and iii) our 2D-3D mapping to compute the original location/world coordinates of cracks for a building.

Complementary Split Ring Resonators (CSRR) and Inter-Digitated Capacitors (IDC) based RF sensors have been ubiquitously optimized assuming empty cavities. However, in bio-sensing applications, most of the samples are dissolved in respective solvents. As a result, designing sensors based on specific solvent-filled cavities is the optimal approach for future RF bio-sensors. This paper presents the application of Binary Particle Swarm Optimization (BPSO) technique for maximizing the sensitivity of CSRR and IDC sensors. BPSO optimizes the designs by cell patterning the sensing region in the presence of phosphate buffer as a solvent. An enhancement of 47.38× for the CSRR and 2.03× for the IDC in the detection of L-Lysine at frequencies 2.18 GHz and 0.72 GHz respectively has been achieved, when compared to sensitivities of their traditional counterparts. Promising results have also been observed in the detection of sucrose and glucose, making it a suitable contender for optimization of next-generation RF bio-sensors.

A comprehensive review paper concerning the geometry, topology, implementation methodology, feeding mechanisms and application domain of wideband and ultrawideband (UWB) antennas for high power applications is presented. Single element antennas, reflector based antennas and array type antennas developed across the globe are taken into account. The antennas considered here are grouped based on their similarity in structure and their performance is compared based on their mode of implementation. This information provides a guidance to the selection of different geometries for various applications especially in the applications pertaining to intentional electromagnetic interference(IEMI). Few antenna models were simulated in CST for the transient response to show their typical transient responses. An exhaustive comparative table is presented with the worldwide designs and some indigenous developments.

Water quality assessment has undeniable value for humanity’s well-being. Despite that, most of the existing techniques struggle to measure the quality of running water due to costly infrastructure or large evaluation time. Recently, quality measurement has been demonstrated using spectrum analysis. However, these modern systems need complicated signal processing algorithms and high processor capabilities for decision making, have elaborate optical assemblies and sizable power requirement making them bulky and expensive to operate. Thus, a refined technique with simplified implementation, low cost, and reliable long-time operation is essential to address this challenge. We present, for the first time to our knowledge, a holistic approach towards a simple and rapid method for determination of water quality parameters in flowing water. This is based on the selection of characteristic wavelengths for the parameters considered and usage of narrowband LEDs as the inspection light source. Thereby incorporating the benefits of optical sensing such as electromagnetic immunity, selectivity, sensitivity, etc. The specific wavelengths of 560nm, 860nm, and 635nm have been demonstrated to have a dominant effect due to pH, turbidity, and total dissolved solids (TDS) respectively, from regression analysis. Using only these wavelengths, an evaluation system capable of determining the light absorption after passing through water has been designed and developed. The obtained optical responses are subsequently related to water parameters, specifically pH, TDS, and turbidity. Experiments were performed to evaluate samples and then validate this technique against standard instruments for both flowing and sampled water setups. It is shown from the measurement results that pH, turbidity, and TDS have linear regression coefficients of 0.9773, 0.9617, 0.8271 and 0.9691, 0.9729, 0.76 for flowing and sampled water arrangements, respectively.