Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Brain Image Analysis Based on Multimodal Deep Learning Scheme

In this paper, we propose a new approach for brain image segmentation based on 3D U-Net deep learning architecture. The proposed approach takes into consideration both the neural network's optimizer as well the biological context of the segmentation tissue, by modeling the structured nature of glioma and edematous tissue around the enhancing and non-enhancing tumor within the U-net model. By training multiple deep neural networks based on 3D U-Nets, with a two-stage design, with whole tumor segmentation as the first stage, followed by segmentation of enhancing and non-enhancing tumors in the second stage, along with data augmentation, it was possible to build sparse deep learning model with few images, and achieve better tumor detection performance as compared to other deep learning models reported for BraTS 2018 challenge task, involving the usage of large dataset for building the models.
Prediction Research on the Energy Consumption of Public Building Based on MLR-BP Neural Network

Accompanying the constant development of data mining technology and neural network, the application of computer technology in energy consumption prediction of large public buildings is the trend of green buildings and intelligent life in the field of engineering. In this paper, a new energy consumption prediction algorithm based on MLR-BP is proposed, which solves the issue of current energy consumption algorithm: the massive amount of calculation and the low prediction accuracy. Firstly, factors that affect building energy consumption are categorized as external factors (meteorological factors) and internal factors. In addition, the multiple linear regression is used to model the external factors, and the influencing factors with high correlation are screened out. Then, the selected external factors and internal factors are combined into the training of BP neural network to obtain a new energy consumption prediction model. The simulation results show that in comparison with the BP neural network, the proposed prediction algorithm in this paper can predict the building energy consumption more accurately.
Research on Quick Detection of Abnormal Events in Power Grid Based on the Large Dimensional Random Matrix Theory

Detecting abnormal operating states in the power grid is a fundamental method used in processing abnormal events in the power grid, while the massive applications of WAMS (wide area measurement system) provide conditions for detecting weak anomalies in the power grid. According to the random matrix theory, acquisition signals of a variety of PMUs (phase measurement units) were constructed into a large-dimensional random matrix. Based on the characteristics of spectral distribution of the large dimensional random matrix, the principle and criterion for detecting abnormal states in the power grid were proposed on the basis of the spectral distribution of the large-dimensional random matrix. What’s more, the detection method proposed was proven to be effective by analyzing PMU frequency monitoring data in the case. The method proposed in this paper can be used for sensitively detecting abnormal events in the power grid and making a quick judgment, which offers a new way of analyzing and processing abnormal events in the power grid.
Can in-memory/analog accelerators be a silver bullet for energy-efficient inference?

A lot of energy-efficient accelerators for inference has been reported. Our investigation result on dependence of energy efficiency on bit precision of accelerators shows that energy efficiency of in-memory/analog accelerators looks better than that of digital accelerators. However, accuracy of in-memory/analog accelerators is usually deteriorated. In order to practically reduce average bit precision for weights while maintaining accuracy, we introduce our proposed filter-wise quantization technique and a specific HW architecture with variable-bit width MAC units used in a bit-parallel manner. Finally, we discuss whether in-memory/analog accelerators can actually be a silver bullet for energy-efficient inference.
Kalman Filter-based Centralized Controller Design for Smart Microgrid

In this paper, a uncomplicated and valid centralized controller is presented to control smart microgrid system with uncertainty. Achieving good suppression of load interference is the control goal we expect to achieve. Consider the transient characteristics of the smart microgrid system during the switching mode of the grid-connected mode and the islanded mode, the smart microgrid model equations are represented in a uncertain state-space. To design the controller, a Kalman Filter Algorithm (KFA) can not only solve the discrete algebraic Riccati equation, but also recursively estimate the state vector of the controller. Kalman filter has a novel usage that combines the observer and the controller, the advantage of this is that it can simplify the design of the controller. The system model was established by Matlab/Simulink, the reliability and practicability of the control system are verified by setting different situations. Simulation results show that the proposed controller has a good performance.
Automatic Detection and Identification of Controlled Knives Based on Improved SSD Model

The automatic detection and identification of security inspection system is the inevitable trend of intelligent development. It is an important research problem to improve the accuracy and efficiency of X-ray security inspection equipment to detect dangerous goods. Taking the automatic tool detection and recognition system of security inspection system as the research object, aiming at the fact that the shallow characteristic chart representation ability of SSD algorithm is not strong, the small target feature of training stage gradually disappears, and the detection accuracy is not high, and there are some problems, such as missing detection and false detection of small targets such as controlling tool in security inspection dangerous goods. This paper proposes to replace the basic network VGGI 6of SSD with ResNetl01 with stronger anti-degradation performance to construct SSD-ResNet101 network structure. On this basis, the feature fusion method is used to make the shallow layer merge the deeper features to increase the receptive field of the shallow feature map. Make full use of the context information to improve the detection accuracy of the small target of dangerous goods in security inspection. The analysis of experimental test data shows that the improved algorithm has high detection accuracy, good real-time performance and good robustness to small targets such as control tools in security picture
Novel 1T2R1T RRAM-based Ternary Content Addressable Memory for Large Scale Pattern Recognition

Resistive Random Access Memories (RRAMs) are a promising solution to implement Ternary Content Addressable Memories (TCAMs) that are more area- and energy-efficient with respect to Static Random Access Memory (SRAM)-based TCAMs. However, RRAM-based TCAMs are limited in the number of bits per word due to the low ratio between the resistances of the high and low resistance states (HRS/LRS) and resistance variability of RRAM. Such a limitation on the word length hinders the parallel search of a very large number of data bits for data-intensive applications. To overcome this issue, for the first time, we propose a new TCAM cell composed of two transistors and two RRAMs in a 1T2R1T configuration, where a RRAM voltage divider (2R) biases a transistor gate (1T) and an additional transistor is used to program the RRAMs (1T). A 3x128bits 1T2R1T TCAM macro were designed, integrated and extensively characterized. We experimentally demonstrate that the sensing margin of the proposed structure is insensitive to HRS/LRS RRAM resistance ratio and variability. With respect to the most common type of 2T2R RRAM-based TCAM [1]–[3], the proposed circuit improves the sensing margin by >5000x while reaching search times of 0.93ns. This allows the search of large volumes of data in parallel. In addition, the proposed structure improves programming and search endurance by 100x and >10x, respectively.
Preparing for the Ai Era Under the Digital Health Framework

Information and communication technology (ICT) for health has shown great potential to improve healthcare efficiency, especially artificial intelligence (AI). To better understand the influence of ICT technology on health, a framework of the digital health industry has been proposed in this paper. Factors from the health industry and the ICT part are extracted to study the interaction between two groups of component factors. Health factors include service and management; and ICT factors include sensors, networks, data resources, platforms, applications and solutions. The interaction between ICT and health can be traced through the development history, from the stage of institutional informationization to regional informationization, and finally to service intelligentization. Following such a developmental roadmap, AI was chosen as one of the most powerful technologies to study the penetration effect and key development trends from the perspectives of data, computing power and algorithms. The health industry will be much improved or redefined in the coming AI era. To better understand the strengths, weaknesses and limitations of AI for health, exogenous factors are discussed at the end of the paper; preparations on collaboration mechanism; standardization and regulation have been proposed for the sustainable development of digital health in the AI era.
A Dual Band Notch UWB Antenna Using Spiral Corner Feed Electromagnetic Band Gap Structures

A dual band notch UWB antenna based on Spiral corner feed Electromagnetic Band Gap (SCF-EBG) Structure is proposed. Two SCF-EBG structures are positioned alongside the feed line of the UWB antenna. The SCF-EBG structures produce two notch band at 3.5 GHz and 5.8 GHz which is in WiMAX band, the low frequency of fifth generation mobile communication (5G) frequency band in China and the wireless local area network interference band. The proposed antenna is suitable for UWB communication systems with dual band notch and a small size notch EBG structure.
Robust Point Set Registration with Mixture Re-Weighting Based on Relative Geometric Structures

Point set registration is one of the challenging tasks in computer vision. One critical step is to find the corresponding relationship between the model point set and the scene point set. Existing registration algorithms primarily utilize the information of global and local shape, yet neglect the credibility of corresponding relation, therefore they may lead to the insufficient estimation of spatial transformation. To tackle this problem, we firstly adopt a relative polar coordinate system, it performs spatial pooling operation and further divides the feature extraction region into sub-areas with different scales. Then, based on the Relative Average Distance (RAD) and the Relative Average Offset Angle (RAOA), we propose multi-granular MRGS descriptor to extract visual structures of the point set. The similarity between the model point set and scene point set is then represented by the Gaussian Mixture Model, where the weights can be dynamically adjusted during the process of registration. Finally, we apply the robust mixture re-weighting to reduce the impact of false corresponding pairs and reinforce the weight of correct matching points. Experimental results on synthetic data and medical image data not only show that our method outperform state-of-the-art methods, but also demonstrate the robustness of our method when the non-grid transformation of point sets suffers from deformations, noises and outliers.
An Effective Channel Access Mechanism for Data Transmission in Heterogeneous Cognitive Radio Sensor Networks

Wireless sensor networks (WSN) function in ISM (Industrial Scientific and Medical) band undergoes unlimited interference problem due to overcrowded sensor nodes and also, it consumes large amount of energy to transmit data. To make an effective use of the spectrum more efficiently Cognitive Radio technology is integrated to Wireless Sensor Networks. The so-called cognitive radio and wireless sensor networks together form a new terminology known as cognitive radio sensor networks, thereby utilizes the spectrum more efficiently by accessing licensed channels. However, in order to support cognitive radio (CR) such as channel discovery, switching and accessing licensed channel in wireless sensor networks makes the task more complex since CRSN utilizes more energy. In pursuance of a solution to the existing, this work has devised a new mechanism by effectively accessing the channel in CRSNs, to improve the energy utilization. Based on the cognitive radio functions and capability, this work has further investigated that when the nodes should shift the channel i.e. from licensed band to ISM band for improving the energy efficiency, in accordance with the packet drop measure of the license-free channel. This work has proposed the effective channel accessing and switching mechanism for data transmission within a cluster and outside cluster in CRSN. The simulation results of the proposed algorithms show that reduction of energy utilization in CRSNs.
An Approach for Peg-in-Hole Assembling Based on Force Feedback Control

In this paper, an intuitive and effective method is proposed for a sensor guided industrial robot to carry out the Peg-in-Hole assembly. This method contains two main steps. The first step is calibrating the wrist force sensor to compensate the load gravity and the bias of sensor so that the external force on the load can be precisely obtained. Secondly an impedance controller is designed to realize the compliant behavior between the manipulator and external environment. Different from the traditional method based on the position control loop, the force control strategy is based on the robot end-effector velocity control loop, which is easier and convenient to implement. Finally, a Peg-in-Hole experiment using an industrial manipulator with wrist sensor and a Peg-in-Hole mould with a tiny clearance was successfully completed. The experimental results verified the effectiveness and practicability of the proposed method.
An EEMD and convolutional neural network based fault diagnosis method in intelligent power plant

Rotating machinery is common equipment in intelligent power plant, such as steam turbine, generator and pump. What’s more, some rotating machinery plays an important role in plants, such as steam turbine, generator and pump. Once these equipment breaks down, the whole power generation process will be paralysed. As the result of nonstationarity and nonlinearity, rotary machinery is complex and difficult to monitor and diagnosis. Traditional methods usually preprocess the vibration signal of rotary machinery firstly and then extract statistical features for fault diagnosis. However, the statistical features are hard to choose. Thus features reduction is appended to the features extraction. But it wastes a lot of human power. In this paper, to reduce the human labor for feature extraction and improve the efficiency of modeling, an EEMD and convolutional network based method is proposed to develop the fault diagnosis model using vibration signal. First, intrinsic mode functions (IMF) are obtained by the ensemble empirical mode decomposition (EEMD) which is a good way to decompose the complex signal. Next, a 1D convolution network using the IMFs is built to extract features automatically. Each IMF is regarded as a channel signal to input to the convolution network. The whole feature extract process requires little prior knowledge and is under the supervision of fault category. The efficacy of the proposed method is illustrated using the dataset collected from a real intelligent power plant.
Comprehensive Calibration Algorithm for Shipborne Grid SINS Based on Double Calculating Circuits

This paper addresses an optimal comprehensive calibration algorithm (CCA) based on double calculating circuits to estimate the gyroscope constant drifts of shipborne grid Strap-down Inertial Navigation System (SGSINS). Two calculating circuits named navigation circuit and calibration circuit are defined. With the same outputs of Inertial Measurement Unit (IMU), the navigation circuit outputs the navigation parameters which can navigate in arbitrary working state; the calibration circuit works in non-damping state which is used to estimate the gyroscope constant drifts. On the basis of SGSINS error model, the gyroscope constant drifts are estimated by Kalman filter under the assistance of external position. The simulation results indicate that the proposed algorithm can estimate the gyroscope constant drifts accurately, compensating the gyroscope constant drifts can effectively suppress the drifted error in SGSINS. In the proposed CCA, the navigation circuit can navigate in arbitrary working state, meanwhile because the gyroscope constant drifts are estimated in non-damping state, the algorithm is not interfered by the maneuvering of the ship.
Interface Engineering to Enhance the Photoresponse of Core-Shell Structure Silicon Nanowire Photodetectors

The core-shell silicon nanowire photodetector has been reported with enhanced responsivity. The interface traps play critical role to determine the performance of core-shell photodetector. The effect of interface quality on the photo response has been investigated using crystalline silicon and polysilicon shells. The device with single-crystalline silicon shell exhibits higher responsivity than that with polysilicon shell. The performance degradation of polysilicon device is caused by the grain boundaries, which are defect sources. Based on these results, the single-crystalline silicon shell was used for fabricating core-shell silicon nanowire photodetector. The photodetector exhibits the maximum responsivity of 0.51 A/W, which is 10% higher than the planar photodetector. Consequently, the core-shell homogeneous silicon nanowire structure can be a promising component for photodetector application.
A Reinforcement Learning Approach for Evaluation of Disaster Relief

Ad hoc networks are considered more suitable for disaster scenarios due to infrastructure-less feature. Existing routing algorithms used on such mobile ad hoc networks are non-adaptive routing algorithms and based on shortest path algorithms. But shortest path may not give optimum path. If the network is heavy loaded with traffic, shortest path may not give better results. Various reinforcement algorithms are used to design such adaptive routing where the routing takes place based on the actual traffic present on a network. One of the reinforcement routing algorithms is Q routing and various variants of Q routing like CQ routing, CDRQ routing etc. In this paper, Q routing and CDRQ routing are implemented and compared using disaster area mobility model in disaster area scenario.
Automatic Generation of Program Comments Based on Problem Statements for Computational Thinking

To support the understanding of programs and understanding of procedures, we think need to automatically generate comments from source code. As a method, we learn the source code and comment pair by Encoder–Decoder translation model using LSTM, thereby generating comments of the source code that was the target of learning. Though, since it is difficult to increase the amount of learning data when generating comment for problem description, generated comments have some incorrect word for problem sentence. We use program problem sentence to generate comments for source code more suitable for users.
Experimental Measurements of Thermal Performances of Carbon Nanomaterial with Vertical Structures in Hotspot Heat Dissipation *

The dramatic progresses in electronics technologies in recent years bring forwards higher requests for quick and valid heat dissipation. Thermal management has become a vital issue in the performance and lifetime reliability of electronic devices and products with high packaging density. Carbon nanomaterials have attracted great attention due to their distinctive structures and excellent properties. In this paper, two kinds of carbon nanomaterials with vertical structures, namely carbon nanowalls and vertically aligned carbon nanofibers, have been prepared and tested to evaluate their heat dissipation performance. Experimental measurements have been carried out to obtain the hotspot temperatures of a test chip with and without the carbon nanomaterials applied. Comparisons of temperature values of the chip at various power loadings show that the utilization of both the carbon nanowalls and the carbon nanofibers can effectively decrease the hotspot temperature. And the heat dissipation improvement is remarkable at a high power density.
Energy-Efficient Offloading and Resource Allocation for Multi-Access Edge Computing

Multi-access edge computing (MEC), one of the key technologies of $5\mathbf{G}$, offloads the computing tasks generated by smart devices to MEC servers. Compared with traditional centralized cloud computing, MEC can significantly reduce the delays in the transmission and data exchange process, and relieve the computational burden of the core network server. In order to minimize the weighted total energy and time consumption under delay requirements, this paper presents a strategy which can offload the computing task from user to MEC server and central cloud server (CCS). Moreover, a priority-based joint calculation offloading algorithm is proposed to figure out the resource allocation problem. In particular, the proposed algorithm sets a priority for each user, and the user with high priority can preferentially select a channel with high transmission quality. The simulation results indicate that the total cost of the proposed algorithm is reduced by 50 percent, compared with the local computing.
Improving HEVC Coding Perceptual Quality Using Saliency Guided CTU Layer QP Adjustment

Recently, improving the perceptual quality of video coding under bit rate constraint has attracted researchers’ attention in video coding field. In this paper, we propose to improve the coding perceptual quality for the latest High Efficiency Video Coding (HEVC) standard using saliency guided coding tree unit (CTU) level quantization parameter (QP) adjustment scheme. Our work mainly includes two steps: first, a CTU layer saliency detection scheme with low complexity is proposed to measure the visual significance for each CTU in a frame. Then, a CTU layer QP adjustment scheme based on relative saliency is proposed to increase the bit rate resource for more salient regions and decrease the bit rate cost for less salient regions. Therefore, the proposed QP adjustment scheme obtains better bit rate allocation among CTUs and generates better coding perceptual quality. Experimental results show that the proposed method achieves better perceptual quality and rate distortion performance for HEVC video coding.
5M-Building: A Large-Scale High-Resolution Building Dataset with CNN Based Detection Analysis

Building detection in remote sensing images plays an important role in applications such as urban management and urban planning. Recently, convolutional neural network (CNN) based methods which benefits from the popularity of large-scale datasets have achieved good performance for object detection. To our best knowledge, there is no large-scale remote sensing image dataset specially build for building detection. Existing building datasets are in small size and lack of diversity, which hinder the development of building detection. In this paper, we present a large-scale high-resolution building dataset named 5M-Building after the number of samples in the dataset. The dataset consists of more than 10 thousand images all collected from GaoFen-2 with a spatial resolution of 0.8 meter. We also present a baseline for the dataset by evaluating three state of the art CNN based detectors. The experiments demonstrate that it is great challenge to accurately detect various buildings from remote sensing images. We hope the 5M-Building dataset will facilitate the research on building detection.