Miracle-dz/UniFAR-FTUs huggingface dataset:
2d-3d Hierarchical Feature Fusion Network For Segmentation Of Bone Structure In Knee Mr Image

Automatic segmentation of knee bone structures is an important task in orthopedics diagnosis of knee disease based on MRI images. Inspired by doctors’ diagnosis of knee in sagittal plane of MR image, we propose to first calculate the sagittal maximum intensity projection (MIP) of MR image, then construct a high precision 2D-3D hierarchical feature fusion network for automatic segmentation of knee based on convolutional encoding and decoding architecture. It includes: 1) A 2D bypass network extracting global features based on MIP; 2) A 3D backbone network calculating local details based on MR volume; 3) A feature fusion module integrating 2D global context and 3D local details hierarchically. Particularly, the global features as anchor points will be fused with the local details at each level of the encoding path to enrich the context of local details and improve the segmentation accuracy. Our method is verified on SKI10 dataset. The average dice coefficients of femur, femoral cartilage, tibia and tibia cartilage are 0.978, 0.848, 0.979 and 0.848, respectively, and the segmentation performance is far better than the state-of-the-art methods.
Limited-View Photoacoustic Imaging Reconstruction With Dual Domain Inputs Based On Mutual Inforamtion

Based on photoacoustic effect, photoacoustic tomography is developing very fast in recent years, and becoming an important imaging tool for both preclinical and clinical studies. With enough ultrasound transducers placed around the biological tissue, PAT can provide both deep penetration and high image contrast by hybrid usage of light and sound. However, considering space and measurement environmental limitations, transducers are always placed in a limited-angle way, which means that the other side without transducer coverage suffers severe information loss. With conventional image reconstruction algorithms, the limited-view tissue induces artifacts and information loss, which may cause doctors’ misdiagnosis or missed diagnosis. In order to solve limited-view PA imaging reconstruction problem, we propose to use both time domain and frequency domain reconstruction algorithms to get delay-and-sum (DAS) image inputs and k-space image inputs. These dual domain images share nearly same texture information but different artifact information, which can teach network how to distinguish these two kinds of information at input level. In this paper, we propose Dual Domain Unet (DuDounet) with specially designed Information Sharing Block (ISB), which can further share two domains’ information and distinguish artifacts. Besides, we use mutual information (MI) with an auxiliary network, whose inputs and outputs are both ground truth, to compensate prior knowledge of limited-view PA inputs. The proposed method is verified with a public clinical database, and shows superior results with SSIM =93.5622% and PSNR =20.8859.
Multipath Extraction Based UL/DL Channel Estimation for FDD Massive MIMO-OFDM Systems

In this paper, we propose a multipath extraction based uplink/downlink channel estimation scheme for wideband massive multiple-input multiple-output (MIMO) systems, where orthogonal frequency division multiplexing (OFDM) is adopted and the beam squint effect is considered. Firstly, we investigate the spatial- and frequency-wideband effects and obtain the relationship between angle-delay information and coordinates of on-grid paths. Secondly, according to the spatial- and frequency-wideband effects, we design a pilot pattern in uplink training to ensure that exact paths can be acquired from limited potential paths. Combined with pilot pattern design, a modified density-based spatial clustering of applications with noise (M-DBSCAN) approach is proposed to acquire the on-grid potential paths. Potential paths are used for initialization in uplink channel extraction. Due to the fact that coordinates of only a few potential paths are close to those of true paths, the uplink multipath extraction problem can be regarded as an off-grid sparse signal reconstruction problem. To deal with this issue, an off-grid sparsity adaptive matching pursuit (OSAMP) based compressive sensing method with low computational complexity is proposed. In OSAMP, angle-delay information as well as path gains of exact paths are accurately estimated, which are further used for accomplishing the uplink channel reconstruction. Angle-delay reciprocity between uplink and downlink channels is exploited, and downlink channel estimation is accomplished with the help of uplink multipath extraction. Downlink pilots are only used for downlink path gain estimation, therefore, the downlink training overhead will be significantly reduced. Simulation results are provided to demonstrate the effectiveness of our proposed low computational complexity channel estimation method.
Towards Automated Data Sharing in Personal Data Stores

The data on the Web is increasingly being centralised towards a few service providers. Personal Data Stores (PDS) have emerged, proposing a fundamental shift from the current service-centric data ecosystem to a decentralised data storage and processing environment by placing the data with users. Users are to assume total self-sovereignty over their data, including opportunities to monetise. While PDS systems enable user empowerment, they also put a greater burden on the users, who may not be technically-savvy, to manage data access, which may increase the chance of unintended mishaps and privacy risks. This research proposes a privacy preference recommender system for privacy-preserving data sharing control that is designed to work with the constraints of user-centric data storage and processing environment for PDS. The outcome contributes towards a user-assisting privacy technology that utilises user context effectively to recommend privacy settings while conforming to the PDS architecture by storing and processing all analytics locally.
Reactive Power Planning using Security-Constrained AC Optimal Power Flow and Sensitivity Analyses

This paper proposes an approach for siting and sizing reactive power sources in order to maintain adequate reactive power reserves in future loading scenarios. We consider both normal and post-contingency operation using a security-constrained optimal power flow (SCOPF) formulation to compute the minimum-required reactive power reserves. To maintain tractability, the SCOPF explicitly considers a small set of important contingencies that are identified using a continuation power flow. The remaining contingencies are evaluated using the SCOPF solution. Any violated contingencies are iteratively added to the considered set in the SCOPF formulation until all contingencies are satisfied. Our approach combines this SCOPF formulation with a bus sensitivity index that identifies potential locations for new reactive power supply. We demonstrate the proposed approach on the IEEE 30- and 118-bus test cases.
Brief Review: Characteristics and Applications of Homogeneous Atmospheric Pressure Dielectric Barrier Discharge

Dielectric barrier discharge (DBD) can generate low temperature plasma under atmospheric pressure, with great research value and bright application prospect. Compared with filamentary DBD, homogeneous DBD has obvious advantages such as the mildness and uniformity of discharge. This paper provided a brief review of DBD research history and paid considerable attention to homogeneous DBD at atmospheric pressure. By summarizing the latest diagnostic technology and numerical simulation methods, this paper reported some of the most important results of DBD characteristics and mechanism in recent decades. In addition, typical DBD plasma applications in biomedical fields, food safety, environmental pollution control were introduced. Technical problems that came up in present DBD research and application development were analysed. And the outlook for the DBD technology was given.
Adaptive Multi-Organ Loss Based Generative Adversarial Network For Automatic Dose Prediction In Radiotherapy

Radiation therapy is regarded as the primary treatment for cancer in clinical, whose target is to apply the accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs) from radiation as much as possible. Nevertheless, dose planning is often performed clinically by manual trial and error, which is time consuming and dependent on the subjective experience of physicians. In this paper, we propose an Adaptive Multi-organ Loss (AML) based Generative Adversarial Network (AML-GAN) for automatic dose prediction of cervical cancer, which achieves satisfactory PTV dose coverage and OARs sparing. By considering both the adversarial loss and the loss constraints for each individual organ, we can obtain a more refined dose map. Extensive experiments have been conducted to demonstrate the superiority of our proposed method in almost all PTV and OARs criteria compared with other state-of-the-art methods.
Rendezvous Delivery: Utilizing Autonomous Electric Vehicles to Improve the Efficiency of Last Mile Parcel Delivery in Urban Areas

Recent advances in technology have led to an increasing degree of automation and optimization in most areas of the parcel logistics process. However, this trend has not been widely adopted in one critical part of the process, which is the actual delivery (also often referred to as “last mile”). One critical task in the last mile delivery process is the computation of a set of optimal delivery tours for a set of given delivery locations. This task is commonly known as the vehicle routing problem. In this work, we propose an extended version of the vehicle routing problem that aims to increase the degree of automation in the last mile delivery process by leveraging on the current advancements in the area of autonomous driving. Furthermore, we study the application of state-of-the-art machine learning methods to solve the proposed problem. We show that such a set up can reduce the overall time needed for delivering parcels compared to the conventional method, in which the delivery agent manually drives the delivery vehicle to each delivery address. In addition, the proposed model is computationally cheap which is essential to support close to real time analysis of context changes (e.g., traffic situation) and decision making, which is critical for an application in the context of highly dynamic Smart City environments.
An Ultra-Lightweight Mutual Authentication Scheme for Smart Grid Two-Way Communications

It is well established that the efficiency, safety, flexibility, and reliability of the power grid is improved by utilization of information and communication technology (ICT) in smart grid. Nevertheless, the use of ICT introduces serious challenges regarding security issues, which has led to introducing many protocols for securing the communication between the smart meters and neighborhood gateways in recent years. However, providing functional and security features such as two-way communication and resistance against memory attack, alongside the lightweight design is the ultimate goal of these schemes. To overcome all the mentioned problems, this paper proposes a super-lightweight secure protocol based on only one-way hash functions and XOR operation, which provides two-way communication and secure one-time pad key for each data transmission. The security and performance analysis shows that not only does the proposed scheme resist the existing attacks but also it dramatically improves the efficiency in terms of storage burden and computational cost.
Mitosis Detection In Phase Contrast Microscopy Image Sequences Using Spatial Segmentation And Spatio-Temporal Localization Refinement

Mitosis identification and localization gives us information on cell status and behaviour, and is foundational to many biological research and medical applications. In this paper, we propose a novel two-stage computer vision framework for the automation of mitosis event detection in large-scale time-lapse phase contrast microscopy image sequences. First stage is a two dimensional image segmentation method for proposing probable spatial locations of mitotic events. The second stage is spatio-temporal mitotic localization refinement driven image sequence classification. Second stage analyzes the temporal dynamics augmented with visual contextual learning and is specifically trained for classifying true mitotic events from hard negative cases wherein the cells appear very similar to mitotic cells. To the best of our knowledge, the proposed method outperforms all previous approaches of mitosis key-point detection. Our state of the art approach achieved top performance in the CVPR 2019 contest on mitosis detection in phase contrast microscopy image sequences with an average F1 score of 0.8804 on the test data of C2C12-16 dataset.
Diffraction Tomography From Single-Molecule Localization Microscopy: Numerical Feasibility

Single-molecule localization microscopy (SMLM) is a fluorescence microscopy technique that achieves super-resolution imaging by sequentially activating and localizing random sparse subsets of fluorophores. Each activated fluorophore emits light that then scatters through the sample, thus acting as a source of illumination from inside the sample. Hence, the sequence of SMLM frames carries information on the distribution of the refractive index of the sample. In this proof-of-concept work, we explore the possibility of exploiting this information to recover the refractive index of the imaged sample, given the localized molecules. Our results with simulated data suggest that it is possible to exploit the phase information that underlies the SMLM data.
EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

Epileptic seizures present a serious danger to the lives of their victims, rendering them unconscious, lacking control, and may even result in death only a few seconds after onset. This gives rise to a crucial need for an effective seizure detection method that is fast, accurate, and has the potential for mass market adoption. Kriging methods have a good reputation for high accuracy in spatial prediction, hence, their extensive use in geostatistics. This paper demonstrates the successful application of Kriging methods for an effective seizure detection device in an edge computing environment by modeling the brain as a spatial panorama. We hereby propose a novel wearable for real-time automated seizure detection from EEG signals using three different types of Kriging, namely, Simple Kriging, Ordinary Kriging and Universal Kriging. After multiple experiments with electroencephalogram (EEG) signals obtained from seizure patients as well as those from their healthy counterparts, the results reveal that the three Kriging methods performed very well in accuracy, sensitivity and latency of detection. It was found however, that Simple Kriging outperforms the other Kriging methods with a mean seizure detection latency of 0.81 sec, a perfect specificity, an accuracy of 97.50% and a sensitivity of 94.74%. The results in this paper compare well with other seizure detection models in the literature but their excellent seizure detection latency surpasses the performance of most existing works in seizure detection.
Managing Class Imbalance in Multi-Organ CT Segmentation in Head and Neck Cancer Patients

Radiotherapy planning of head and neck cancer patients requires an accurate delineation of several organs at risk (OAR) from planning CT images in order to determine a dose plan which reduces toxicity and salvages normal tissue. However training a single deep neural network for multiple organs is highly sensitive to class imbalance and variability in size between several structures within the head and neck region. In this paper, we propose a single-class segmentation model for each OAR in order to handle class imbalance issues during training across output classes (one class per structure), where there exists a severe disparity between 12 OAR. Based on a U-net architecture, we present a transfer learning approach between similar OAR to leverage common learned features, as well as a simple weight averaging strategy to initialize a model as the average of multiple models, each trained on a separate organ. Experiments performed on an internal dataset of 200 H & N cancer patients treated with external beam radiotherapy, show the proposed model presents a significant improvement compared to the baseline multi-organ segmentation model, which attempts to simultaneously train several OAR. The proposed model yields an overall Dice score of $0.75 \pm 0.12$, by using both transfer learning across OAR and a weight averaging strategy, indicating that a reasonable segmentation performance can be achieved by leveraging additional data from surrounding structures, limiting the uncertainty in ground-truth annotations.
3-D Maximum Likelihood Estimation Sample Consensus for Correspondence Grouping in 3-D Plant Point Cloud

Computer vision based plant phenomics can be used to monitor the health and the growth of plants. This letter presents the extension of 2-D maximum likelihood matching to 3-D maximum likelihood estimation sample consensus (MLEASAC) and provides a comparative evaluation of some popular 3-D correspondence grouping algorithms. We test these algorithms on 3-D point clouds of plants along with two standard benchmarks addressing shape retrieval and point cloud registration scenarios. The performance of the correspondence grouping algorithms is evaluated in terms of precision and recall. The results show that of all the evaluated algorithms, 3-D random sample consensus (RANSAC) and MLEASAC perform the best, with MLEASAC being slightly more efficient while being computationally less intense than RANSAC.
Universal Artificial Pheromone Framework with Deep Reinforcement Learning for Robotic Systems

Pheromone-based communication has been adopted into robotic systems inspired by social insects as an alternative communication strategy, promising for dynamic and complex environments. For this reason, artificial pheromone communication system for robotic systems, especially for swarm robotic systems, has been proposed with diverse mediums such as light and virtual environment. However, the low versatility of each method makes it difficult to utilise the benefits of pheromone-based communication in diverse robotic platforms and environments. In this paper, we proposed PhERS (Pheromone for Every RobotS) framework designed to increase versatility, aiming to boost research and applications of pheromone-based communication in robotics. To validate the framework, we conducted experiments with simulated robots manoeuvred by hand-tuned controller performing navigation and collision avoidance tasks. As another contribution, we proposed a novel Deep Reinforcement Learning (DRL)-based controller for robots utilising pheromones to overcome the limitations of hand-tuned controller. Experiments and observed results demonstrated the feasibility of using the proposed framework in a robotic scenario, showing that DRLbased controller outperforms the baseline hand-tuned controller in a dynamic environment.
Pediatric Hydronephrosis Severity Assessment Using Convolutional Neural Networks With Standardized Ultrasound Images

Hydronephrosis, dilation of the urinary collecting system, is one of the most common abnormalities detected in young children. Ultrasound is a routine imaging modality for detecting pediatric hydronephrosis. In this paper, we present a new deep learning-based approach to predict the hydronephrosis severity defined by presence or absence of obstruction or blockage at the junction of the kidney and ureter. First, we semi-automatically segmented the kidney to analyze its appearance that characterizes the obstruction. Then, we developed a deep learning-based model to predict the obstruction using each slice in the ultrasound image. Finally, we fused the information obtained from kidney slices to get the fmal risk score of obstruction. We evaluated the performance of this method on a dataset of volumetric ultrasound images acquired from 54 hydronephrotic kidneys. We obtained an average accuracy of 0.78 to identify clinically relevant obstructions associated with pediatric hydronephrosis.
Improved Resistive Droop Control Strategy for Power Sharing of Low-voltage AC Microgrid

In low-voltage AC microgrid, the different distance between distributed generation units (DGs) and common bus leads to the mismatch of feeder impedance, which results in the error of output power sharing. In order to realize the accurate power sharing and maintain the stability of the AC bus voltage, this paper proposes an improved resistive droop control strategy based on adaptive virtual impedance, which introduces dynamic dominant resistance and negative inductance to diminish the impact caused by different feeder impedance. Furthermore, a voltage compensation stage is added to maintain the bus voltage at a satisfactory value by mitigating the voltage drop caused by the virtual impedance. The effectiveness of the proposed control strategy has been demonstrated by various simulation cases. The results show that the proposed control strategy can deal with conditions of dynamic and nonlinear load without the information of accurate feeder parameters.
Determination of Equilibrium Solubility and Apparent Oil/Water Partition Coefficient of Ursolic Acid

OBJECTIVE To determine the equilibrium solubility of ursolic acid and its partition coefficients in the n-octanol-water system for designing new formulations.METHODS High performance liquid chromatography(HPLC) was established to determine the contents of ursolic acid in different pH solutions,differernt solvents and the partition coefficients for the n-octanol-water systems.RESULTS The equilibrium solubility of ursolic acid was 5.64 mg? L? 1 in water at 25 ℃ and was not influenced by the pH of the buffer solution.The solubility of ursolic acid in oils was poor,but was relatively good in surfactants and Semi polar solvents.Partition coefficient of ursolic acid was 2.17 in n-octanol-water system.CONCLUSION Ursolic acid is a lipophilic drug with little solubility property.
Improved black-box attack based on query and perturbation distribution

Adversarial examples cause the deep neural network prediction error, which is a great threat to the deep neural network. How to generate more natural adversarial examples and improve the robustness of deep neural networks has received attention. In this paper, we propose an improved blackbox attack (IBBA) algorithm based on query and perturbation distribution. This algorithm only needs the top-l label of the attacked model to generate the adversarial examples. Based on the existing black-box attacks, we optimize the performance of the algorithm from two aspects: query distribution and perturbation distribution. In the aspect of query distribution, we adopt different strategies for nontargeted attack and targeted attack; in the aspect of perturbation distribution, we choose different low-frequency noise according to the difference between the targeted attack and nontargeted attack. The experimental results on ImageNet show that the proposed algorithm is better than the existing algorithms in low query number, and the targeted attack is better in each specified query number.