This paper proposes a dual notched band ultra-wideband (UWB) bandpass filter (BPF) based on hybrid transition of microstrip and coplanar waveguide (CPW). The CPW in ground plane houses a stepped impedance resonator shorted at ends, and is designed to place its resonant modes within the UWB passband. The microstrips on the top plane are placed some distance apart in a back-to-back manner. The transition of microstrip on top and shorted CPW in the ground is coupled through the dielectric in a broadside manner. The optimized design of the transition develops the basic UWB spectrum with good return/insertion loss and extended stopband. Later, defected ground structure, embedded in CPW, and split ring resonators, coupled to feeding lines are utilized to develop dual sharp passband notches. The simulated data are verified against the experimentally developed prototype. The proposed dual notched UWB-BPF structure measures only 14.6 × 7.3 mm2, thereby justifying its compactness.

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A novel and compact ultra-wideband (UWB) bandpass filter (BPF) based on the broadband balun mechanism is proposed. The proposed structure is made up of two back-to-back open circuited meandered microstrip lines on the top plane coupled in a broadside fashion with a short circuited co-planar waveguide in the ground. This configuration gives rise to a smooth UWB passband with an insertion loss of <0.19 dB in simulation and two transmission zeros at the lower and upper passband edges which provide sharp selectivity. To confirm the theory a prototype is fabricated and its measured response is found to be in close agreement with the simulated response. The proposed structure is very small in size measuring only 14.6 × 7.3 mm 2 .

Drilling and blasting remain the preferred technique used for rock mass breaking in mining and construction projects compared to other methods from an economic and productivity point of view. However, rock mass breaking utilizes only a maximum of 30% of the blast explosive energy, and around 70% is lost as waste, thus creating negative impacts on the safety and surrounding environment. Blast-induced impact prediction has become very demonstrated in recent research as a recommended solution to optimize blasting operation, increase efficiency, and mitigate safety and environmental concerns. Artificial neural networks (ANN) were recently introduced as a computing approach to design the computational model of blast-induced fragmentation and other impacts with proven superior capability. This paper highlights and discusses the research articles conducted and published in this field among the literature. The prediction models of rock fragmentation and some blast-induced effects, including flyrock, ground vibration, and back-break, were detailed investigated in this review. The literature showed that applying the artificial neural network for blast events prediction is a practical way to achieve optimized blasting operation with reduced undesirable effects. At the same time, the examined papers indicate a lack of articles focused on blast-induced fragmentation prediction using the ANN technique despite its significant importance in the overall economy of whole mining operations. As well, the investigation revealed some lack of research that predicted more than one blast-induced impact.