A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Image Processing with Dipole-Coupled Nanomagnets: Noise Suppression and Edge Enhancement Detection

Hardware based image processing offers speed and convenience not found in software-centric approaches. Here, we show theoretically that a two-dimensional periodic array of dipole-coupled elliptical nanomagnets, delineated on a piezoelectric substrate, can act as a dynamical system for specific image processing functions. Each nanomagnet has two stable magnetization states that encode pixel color (black or white). An image containing black and white pixels is first converted to voltage states and then mapped into the magnetization states of a nanomagnet array with magneto-tunneling junctions (MTJs). The same MTJs are employed to read out the processed pixel colors later. Dipole interaction between the nanomagnets implements specific image processing tasks such as noise reduction and edge enhancement detection. These functions are triggered by applying a global strain to the nanomagnets with a voltage dropped across the piezoelectric substrate. An image containing an arbitrary number of black and white pixels can be processed in few nanoseconds with very low energy cost

Scanning optical homodyne detection of high-frequency picoscale resonances in cantilever and tuning fork sensors

Higher harmonic modes in nanoscale silicon cantilevers and microscale quartz tuning forks are detected and characterized using a custom scanning optical homodyne interferometer. Capable of both mass and force sensing, these resonators exhibit high-frequency harmonic motion content with picometer-scale amplitudes detected in a 2.5 MHz bandwidth, driven by ambient thermal radiation. Quartz tuning forks additionally display both in-plane and out-of-plane harmonics. The first six electronically detected resonances are matched to optically detected and mapped fork eigenmodes. Mass sensing experiments utilizing higher tuning fork modes indicate >6x sensitivity enhancement over fundamental mode operation.Comment: 3 pages, 3 figures, submitted to Applied Physics Letter