Automatic Segmentation of Human Placenta Images with U-Net

© 2013 IEEE. Placenta is closely related to the health of the fetus. Abnormal placental function will affect the normal development of the fetus, and in severe cases, even endanger the life of the fetus. Therefore, accurate and quantitative evaluation of placenta has important clinical significance. It is a common method to segment human placenta with semantic segmentation. However, manual segmentation relies too much on the professional knowledge and clinical experience of the staff, and it will also consume a lot of time. Therefore, based on u-net, we propose an automatic segmentation method of human placenta, which reduces manual intervention and greatly speeds up the segmentation, making large-scale segmentation possible. The human placenta data set we used was labeled by experts, which was obtained from prenatal examinations of 11 pregnant women, about 1,110 images. It was a comprehensive and clinically significant data set. By training the network with such data set, the robustness of the model will be better. After testing on the data set, the segmentation effect is basically consistent with the manual segmentation effect

Nonvolatile CMOS memristor, reconfigurable array and its application in power load forecasting

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/TII.2023.3341256The high cost, low yield, and low stability of nano-materials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However these emulators lack nonvolatile characteristics, limiting their scope of application. This paper proposes an innovative nonvolatile memristor circuit based on complementary metal-oxide-semiconductor (CMOS) technology, expanding the horizons of memristor emulators. The proposed memristor is fabricated in a reconfigurable array architecture using the standard CMOS process, allowing the connection between memristors to be altered by configuring the on-off state of switches. Compared to nano-material memristors, the CMOS nonvolatile memristor circuit proposed in this paper offers advantages of low manufacturing cost and easy mass production, which can promote the application of memristors. The application of the reconfigurable array is further studied by constructing an Echo State Network (ESN) for short-term load forecasting in the power system.Peer reviewe

Memristor-Based HTM Spatial Pooler with On-Device Learning for Pattern Recognition

This article investigates hardware implementation of hierarchical temporal memory (HTM), a brain-inspired machine learning algorithm that mimics the key functions of the neocortex and is applicable to many machine learning tasks. Spatial pooler (SP) is one of the main parts of HTM, designed to learn the spatial information and obtain the sparse distributed representations (SDRs) of input patterns. The other part is temporal memory (TM) which aims to learn the temporal information of inputs. The memristor, which is an appropriate synapse emulator for neuromorphic systems, can be used as the synapse in SP and TM circuits. In this article, a memristor-based SP (MSP) circuit structure is designed to accelerate the execution of the SP algorithm. The presented MSP has properties of modeling both the synaptic permanence and the synaptic connection state within a single synapse, and on-device and parallel learning. Simulation results of statistic metrics and classification tasks on several real-world datasets substantiate the validity of MSP

Multi-label image classification by feature attention network

Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset

Speech Feature Analysis and Discrimination in Biological Information

A silent speech interface is a system that allows people doing speech communication without using their own speech sounds. Today, a variety of speech interfaces have been developed using biological signals such as the eye movement, and the articulatory. These interfaces are mainly for supporting people who have speech disorder to communicate with others, yet there are many speech disorder that have not been addressed by the current technologies. The possible cause of the issue is the limited numbers of the biological signals used for the speech interface. The uncovered issues with speech disorders can be addressed through identifying new biological signals for speech interface development. Therefore, we aim to find new biological signals that can be used for speech interface developments. The biological signals we focused on were the vibration of the vocal folds and brain waves. After measuring the data and extracting the features, we verified whether this data can be used to classify speech sounds through machine learning models: Support Vector Machine for the vocal folds vibration, and Echo State Network for the brain waves. As a result, using the vocal folds vibration signals, Japanese vowels could be classified with 71 % accuracy on average. Using the brain waves, five different consonants were classified with 28.3 % accuracy on average. These findings indicate the possibility that the vocal folds vibration signals and the brain waves can be used as new biological signals for speech interface developments. From this study, we were able to discover some needed improvements that should be considered in the future that may lead to further improvement in the classification accuracy

Design of Robust Memristor-Based Neuromorphic Circuits and Systems with Online Learning

Computing systems that are capable of performing human-like cognitive tasks have been an area of active research in the recent past. However, due to the bottleneck faced by the traditionally adopted von Neumann computing architecture, bio-inspired neural network style computing paradigm has seen a spike in research interest. Physical implementations of this paradigm of computing are known as neuromorphic systems. In the recent years, in the domain of neuromorphic systems, memristor based neuromorphic systems have gained increased attention from the research community due to the advantages offered by memristors such as their nanoscale size, nonvolatile nature and power efficient programming capability. However, these devices also suffer from a variety of non-ideal behaviors such as switching speed and threshold asymmetry, limited resolution and endurance that can have a detrimental impact on the operation of the systems employing these devices. This work aims to develop device-aware circuits that are robust in the face of such non-ideal properties. A bi-memristor synapse is first presented whose spike-timing-dependent plasticity (STDP) behavior can be precisely controlled on-chip and hence is shown to be robust. Later, a mixed-mode neuron is introduced that is amenable for use in conjunction with a range of memristors without needing to custom design it. These circuits are then used together to construct a memristive crossbar based system with supervised STDP learning to perform a pattern recognition application. The learning in the crossbar system is shown to be robust to the device-level issues owing to the robustness of the proposed circuits. Lastly, the proposed circuits are applied to build a liquid state machine based reservoir computing system. The reservoir used here is a spiking recurrent neural network generated using an evolutionary optimization algorithm and the readout layer is built with the crossbar system presented earlier, with STDP based online learning. A generalized framework for the hardware implementation of this system is proposed and it is shown that this liquid state machine is robust against device-level switching issues that would have otherwise impacted learning in the readout layer. Thereby, it is demonstrated that the proposed circuits along with their learning techniques can be used to build robust memristor-based neuromorphic systems with online learning