The dielectric constants and dielectric loss tangents of Li-Ti ferrites of different composition were measured at room temperature using a capacitance bridge at low frequencies. It was observed that the lower concentration of titanium (<0.6 mole of Ti) had minimal effect on the value of the dielectric constant, while for higher (>0.6 mol. of Ti) titanium dopants, the value of dielectric constant decreased very rapidly. For some compositions, the dielectric loss tangent curves exhibited a maximum at frequencies between 3 and 15 kHz. A qualitative explanation for the observed behaviour is given.

No abstract

No abstract

No abstract

The emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization-driven deep learning technique for image forgery detection. The purpose is to develop a copy-move image forgery detection technique using a deep neuro-fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro-fuzzy network. Finally, the deep neuro-fuzzy network training is performed using the proposed multi-verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi-verse optimizer and invasive weed optimization technique. Thus, the copy-move image forgery detection is effectively performed using the proposed MVIWO-based deep neuro-fuzzy network. The 586