A New Subclass of Meromorphic Starlike Functions Associated with Q-Hypergeometric Functions

The fractional calculus operator has used in various field of sciences, GFT and in the engineering, if we extend the ordinary fractional calculus in the q-theory we get fractional q-calculus operator. In this paper by making use of fractional q-calculus operator we have introduced a new subclass of Meromorphic starlike functions N_q (? ,? ,?) defined in the open disk and determined coefficient estimate, neighbourhood result, subordination results, extreme points and partial sums for the functions belonging to this class

ApproxVision: Approximating the Image By Exploiting the Limitations of Human Visual System

Approximate computing has recently emerged as a promising approach to the energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result for saving energy and performance enhancement. In image processing, applications impose high energy consumption in loading and accessing the image data in the memory. Fortunately, most image processing applications can tolerate approximation in processing. The quality of service (QoS) of image processing applications depends upon the human visual system. The Human Visual system has some limitations like weak peripheral vision and not able to distinguish the difference between the quality of the original image and processed image when PSNR value is greater than 30 dB. These limitations give us a hint that instead of approximating the entire image we should take the peripheral part of the image because the human eye has the lower peripheral vision and high center vision and at the same time processed image has PSNR value greater 30 dB to gain the excellent quality of an image. Leveraging these facts we proposed one approximate computing technique that will save energy without sacrificing the QoS. We will approximate only the peripheral part of the image, and in the peripheral region, we change lower bits in each pixel because the contribution of lower bits in a pixel is less compare to higher order bits in a pixel. The proposed technique will take care of the limitations of the human visual system to approximate the images

M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues

We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities. M3ER models a novel, data-driven multiplicative fusion method to combine the modalities, which learn to emphasize the more reliable cues and suppress others on a per-sample basis. By introducing a check step which uses Canonical Correlational Analysis to differentiate between ineffective and effective modalities, M3ER is robust to sensor noise. M3ER also generates proxy features in place of the ineffectual modalities. We demonstrate the efficiency of our network through experimentation on two benchmark datasets, IEMOCAP and CMU-MOSEI. We report a mean accuracy of 82.7% on IEMOCAP and 89.0% on CMU-MOSEI, which, collectively, is an improvement of about 5% over prior work