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Found 25 papers, 0 papers with code
AM-DCGAN: Analog Memristive Hardware Accelerator for Deep Convolutional Generative Adversarial Networks
Generative Adversarial Network (GAN) is a well known computationally complex algorithm requiring signficiant computational resources in software implementations including large amount of data to be trained.
Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM
The automated wafer inspection and quality control is a complex and time-consuming task, which can speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors.
Memristive LSTM network hardware architecture for time-series predictive modeling problem
allows to model systems with multiple input variables and control several parameters such as the size of the look-back window to make a prediction and number of time steps to be predicted.
Learning in Memristive Neural Network Architectures using Analog Backpropagation Circuits
The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem.
Memristor-based Synaptic Sampling Machines
Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses.
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices.
Approximate Probabilistic Neural Networks with Gated Threshold Logic
Probabilistic Neural Network (PNN) is a feed-forward artificial neural network developed for solving classification problems.
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure.
Hierarchical Temporal Memory using Memristor Networks: A Survey
This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM).
Hardware Architecture Emerging Technologies
Neuron inspired data encoding memristive multi-level memory cell
Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units.
Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory
Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex.
Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development
The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes based on in vitro models.
Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review
The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks.
Facial emotion recognition using min-max similarity classifier
In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem.
On-chip Face Recognition System Design with Memristive Hierarchical Temporal Memory
Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of neocortex, part of the human brain, which is responsible for learning, classification, and making predictions.
Emerging Technologies
High Voltage Insulator Surface Evaluation Using Image Processing
High voltage insulators are widely deployed in power systems to isolate the live- and dead-part of overhead lines as well as to support the power line conductors mechanically.
Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States
A non-invasive method for the monitoring of heart activity can help to reduce the deaths caused by heart disorders such as stroke, arrhythmia and heart attack.
Edge Detection for Pattern Recognition: A Survey
This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision.
Detection and Analysis of Emotion From Speech Signals
The classification was performed for different classifiers.
Adaptive Digital Scan Variable Pixels
The square and rectangular shape of the pixels in the digital images for sensing and display purposes introduces several inaccuracies in the representation of digital images.
A Review of Feature and Data Fusion with Medical Images
The fusion techniques that utilize multiple feature sets to form new features that are often more robust and contain useful information for future processing are referred to as feature fusion.
Spatial Stimuli Gradient Sketch Model
The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem.
Sparse distributed localized gradient fused features of objects
The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy.
Memristive Threshold Logic Circuit Design of Fast Moving Object Detection
Real-time detection of moving objects involves memorisation of features in the template image and their comparison with those in the test image.
Examplers based image fusion features for face recognition
Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image.
