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Mucky Ann
Mucky Ann is about a spate of graffiti I encountered in Sheffield. The essay and some photographs appear in The Eccentric City, a publication conceived and edited by Harry Palmer and Si Walker
Power scalable implementation of artificial neural networks
As the use of Artificial Neural Network (ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple example, we demonstrate how power scaling can be achieved with dynamic pruning techniques
Power Aware Learning for Class AB Analogue VLSI Neural Network
Recent research into Artificial Neural Networks (ANN) has highlighted the potential of using compact analogue ANN hardware cores in embedded mobile devices, where power consumption of ANN hardware is a very significant implementation issue. This paper proposes a learning mechanism suitable for low-power class AB type analogue ANN that not only tunes the network to obtain minimum error, but also adaptively learns to reduce power consumption. Our experiments show substantial reductions in the power budget (30% to 50%) for a variety of example networks as a result of our power-aware learning
Lung Cancer Detection Using Artificial Neural Network
In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy
Intelligent optical performance monitor using multi-task learning based artificial neural network
An intelligent optical performance monitor using multi-task learning based
artificial neural network (MTL-ANN) is designed for simultaneous OSNR
monitoring and modulation format identification (MFI). Signals' amplitude
histograms (AHs) after constant module algorithm are selected as the input
features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and
PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI
simultaneously with higher accuracy and stability compared with single-task
learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and
OSNR monitoring root-mean-square error of 0.63 dB for the three modulation
formats under consideration. Furthermore, the number of neuron needed for the
single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity
optical performance monitoring devices for real-time performance monitoring
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