Phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters

Analog Integrated Circuits and Signal Processing - This paper presents the complete design of a phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters. The...     

Personalized web service recommendation through mishmash technique and deep learning model

Multimedia Tools and Applications - Discovering the relevant web services for specific applications in the dynamically changing business world becomes very critical. Researchers have used many...     

Ultra-compact with improved data rate optical encoder based on 2D linear photonic crystal ring resonator

Photonic Network Communications - In the present work, a high-speed optical encoder is proposed based on two-dimensional photonic crystal ring resonator using coupled mode theory and resonance...     

Enthalpy Calibration Method for Differential Scanning Calorimeters

Measurement Techniques - A method for enthalpy calibration of differential scanning calorimeters is described which makes it possible to eliminate the existing limits on the temperature range...     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

An Efficient Near Lossless Image Compression Algorithm Using Dissemination of Spatial Correlation for Remote Sensing Color Images

Wireless Personal Communications - Image transmission from space borne equipment is a challenging task owing to its restrictions on bandwidth and memory requirement. Many of the state of art...     

Structural,Dielectric and AC Conductivity of Multicomponent Borotellurite Glasses

Glass Physics and Chemistry - Structural, dielectric and conductivity of multicomponent borotellurite glasses have been thorougly investigated. Noncrystallinity confirmed through XRD and structural...     

Gamma ray-irradiated induced effects on SCN ligand-based MMTC single crystals for optoelectronic applications synthesized by SR method

Journal of Materials Science: Materials in Electronics - Nonlinear optical organometallic single crystal of manganese mercury thiocyanate (MMTC) has been grown by SR method in aqueous solution. The...     

Polysorbate identity and quantity dictate the extensional flow properties of protein-excipient solutions

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate extensional rheology, using a model ovalbumin (OVA) protein and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impact the onset of weakly elastic flow behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than in analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.