（Ag ＋ Fe）-doped ZnO nanopowders have been synthesized using combustion method. Ag doping level was kept as 2 at.%, and Fe doping level was varied from 3 to 6 at,%, and the structural, optical, surface morphological...（Ag ＋ Fe）-doped ZnO nanopowders have been synthesized using combustion method. Ag doping level was kept as 2 at.%, and Fe doping level was varied from 3 to 6 at,%, and the structural, optical, surface morphological, and antibacterial properties have been investigated. The structural studies show that ZnO/（Ag 4-Fe） nanopowders have hexagonal wurtzite structure with a preferential orientation along the （101） plane. The FE-SEM images indicate that there is a gradual decrease in the grain size with the increase in the doping level of Fe, and the TEM images are correlated well with FE-SEM images. The XPS profile clearly confirms the presence of expected elemental composition. Photolumi- nescence studies reveal the presence of extrinsic defects in the material. Antibacterial activity of Ag- and Fe-doped ZnO nanopowders against Vibrio parahaemolyticus, Vibrio Cholerae, and Staphylococcus aureus bacteria was also investigated.展开更多
Approximate computing is a popularfield for low power consumption that is used in several applications like image processing,video processing,multi-media and data mining.This Approximate computing is majorly performed ...Approximate computing is a popularfield for low power consumption that is used in several applications like image processing,video processing,multi-media and data mining.This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier.The multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its performance.There are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so easy.This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy.To overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher accuracy.In recent times,DL is the method that is used for higher learning and prediction accuracy in severalfields.Therefore,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate computing.To provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyfish search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier.The optimal hyperparameters of the LSTM model are identified by jelly search opti-misation.Thisfine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy.The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error metrics.The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.展开更多
Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits.Pruning is one of the types of approximate circuit design technique which removes logic g...Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits.Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error.In this work,a novel machine learning(ML)-based pruning technique is introduced to design digital circuits.The machine-learning algorithm of the random forest deci-sion tree is used to prune nodes selectively based on their input pattern.In addi-tion,an error compensation value is added to the original output to reduce an error rate.Experimental results proved the efficiency of the proposed technique in terms of area,power and error rate.Compared to conventional pruning,proposed ML pruning achieves 32%and 26%of the area and delay reductions in 8*8 multi-plier implementation.Low power image processing algorithms are essential in various applications like image compression and enhancement algorithms.For real-time evaluation,proposed ML optimized pruning is applied in discrete cosine transform(DCT).It is a basic element of image and video processing applications.Experimental results on benchmark images show that proposed pruning achieves a very good peak signal-to-noise ratio(PSNR)value with a considerable amount of energy savings compared to other methods.展开更多
基金the financial assistance from the director of collegiate education,Govt.of Tamil Nadu,Chennai
文摘（Ag ＋ Fe）-doped ZnO nanopowders have been synthesized using combustion method. Ag doping level was kept as 2 at.%, and Fe doping level was varied from 3 to 6 at,%, and the structural, optical, surface morphological, and antibacterial properties have been investigated. The structural studies show that ZnO/（Ag 4-Fe） nanopowders have hexagonal wurtzite structure with a preferential orientation along the （101） plane. The FE-SEM images indicate that there is a gradual decrease in the grain size with the increase in the doping level of Fe, and the TEM images are correlated well with FE-SEM images. The XPS profile clearly confirms the presence of expected elemental composition. Photolumi- nescence studies reveal the presence of extrinsic defects in the material. Antibacterial activity of Ag- and Fe-doped ZnO nanopowders against Vibrio parahaemolyticus, Vibrio Cholerae, and Staphylococcus aureus bacteria was also investigated.
文摘Approximate computing is a popularfield for low power consumption that is used in several applications like image processing,video processing,multi-media and data mining.This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier.The multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its performance.There are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so easy.This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy.To overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher accuracy.In recent times,DL is the method that is used for higher learning and prediction accuracy in severalfields.Therefore,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate computing.To provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyfish search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier.The optimal hyperparameters of the LSTM model are identified by jelly search opti-misation.Thisfine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy.The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error metrics.The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.
文摘Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits.Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error.In this work,a novel machine learning(ML)-based pruning technique is introduced to design digital circuits.The machine-learning algorithm of the random forest deci-sion tree is used to prune nodes selectively based on their input pattern.In addi-tion,an error compensation value is added to the original output to reduce an error rate.Experimental results proved the efficiency of the proposed technique in terms of area,power and error rate.Compared to conventional pruning,proposed ML pruning achieves 32%and 26%of the area and delay reductions in 8*8 multi-plier implementation.Low power image processing algorithms are essential in various applications like image compression and enhancement algorithms.For real-time evaluation,proposed ML optimized pruning is applied in discrete cosine transform(DCT).It is a basic element of image and video processing applications.Experimental results on benchmark images show that proposed pruning achieves a very good peak signal-to-noise ratio(PSNR)value with a considerable amount of energy savings compared to other methods.
