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Huiqian Wang

Bio: Huiqian Wang is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 7, co-authored 18 publications receiving 150 citations. Previous affiliations of Huiqian Wang include Drexel University & University of Pennsylvania.

Papers
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Journal ArticleDOI
TL;DR: The experimental results indicate that the proposed algorithm has a large key space, high key sensitivity and excellent attack resistance ability, and is feasible in privacy protection of BAN system.

56 citations

Journal ArticleDOI
TL;DR: An improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems.
Abstract: In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.

35 citations

Journal ArticleDOI
TL;DR: A new encryption method based on the QRS complex of the ECG signal is proposed, which adopts the vital signs from the BAN system to form the initial key, utilizes the LFSR (Linear Feedback Shift Register) circuit to generate the key stream, and then encrypts the data inThe BANs.

24 citations

Journal ArticleDOI
TL;DR: The authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology to PET/CT images and demonstrate the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error.
Abstract: Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.

21 citations

Journal ArticleDOI
TL;DR: A frame structure model of a self-adaptive guard band (SAGB) protocol is proposed, which introduces a guard band in each time slot according to the allowed maximum time drift of the crystal, adaptively adjusts the value of the GB based on the actual time drift, and then ensures that the node simultaneously maintains the sleeping state and synchronization with the coordinator during beacon transmission, thus reducing the energy consumption.
Abstract: Body area networks (BAN) are at the forefront of technologies for long-term monitoring of personal healthcare, which is intended be an effective strategy to address the aging population worldwide. The transceiver is the most energy-consuming part of a sensor node, and radio transmission in the vicinity of the human body is highly lossy and inefficient. Therefore, the energy of the sensor node constrains the life cycle and quality of service (QoS) of the network; consequently, low-cost protocol shave attracted wide interest. This paper proposes a frame structure model of a self-adaptive guard band (SAGB) protocol, which introduces a guard band (GB) in each time slot according to the allowed maximum time drift of the crystal, adaptively adjusts the value of the GB based on the actual time drift, and then ensures that the node simultaneously maintains the sleeping state and synchronization with the coordinator during beacon transmission, thus reducing the energy consumption.

17 citations


Cited by
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Journal ArticleDOI
TL;DR: The principle issues and clinical applications of PPG for monitoring oxygen saturation are reviewed and wearable unobtrusive PPG monitors are commercially available.
Abstract: A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

141 citations

Journal ArticleDOI
TL;DR: Topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters in the flash flood susceptibility modeling of Kalvan watershed.
Abstract: Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

104 citations

Journal ArticleDOI
TL;DR: This paper presents a novel authentication system using an efficient feature detection algorithm and a convolutional neural network (CNN) based on ECG for human authentication that is highly usable in a real-time authentication system.

93 citations

Journal ArticleDOI
TL;DR: This research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content, which are different in the number of layers and learnable filters.
Abstract: Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

92 citations

Journal ArticleDOI
TL;DR: Experiments and analysis prove that the improved chaotic map and the algorithm has an excellent performance in image encryption and various attacks.
Abstract: This paper introduces new simple and effective improved one-dimension(1D) Logistic map and Sine map made by the output sequences of two same existing 1D chaotic maps. The comparison analysis of the proposed improved 1D chaotic map and previous 1D chaotic map confirmed the accuracy of the improved chaotic map. To investigate the applications of the improved chaotic system in image encryption, a novel bit-level image encryption system is proposed. Experiments and analysis prove that the improved chaotic map and the algorithm has an excellent performance in image encryption and various attacks.

92 citations