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In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price, the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed, and the application mode of multidisciplinary in the agricultural industry is discussed. On the basis of the big data platform of garlic industry chain, this paper constructs a Garch model to analyze the fluctuation law of garlic price in the circulation link and provides the garlic industry service from the angle of price fluctuation combined with the economic analysis. The research shows that the average price rate of the price of garlic shows “agglomeration” and cyclical phenomenon, which has the characteristics of fragility, left and a non-normal distribution and the fitting value of the GARCH model is very close to the true value. Finally, it looks into the industrial service form from the perspective of garlic price fluctuation.

Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is challenging for breast cancer detection using breast ultrasound (BUS) images due to the complicated breast structure and poor quality of the images. This paper proposes a novel tumor saliency estimation (TSE) model guided by enriched breast anatomy knowledge to localize the tumor. First, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with an increasing 10% of $F_{measure}$ on the public BUS dataset.

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound (BUS) image due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model the generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues. The extensive experiments demonstrate that the proposed approach obtains more accurate foreground and background maps with breast anatomy; especially, for the images having large or small tumors. Meanwhile, the new objective function can handle the images without tumors. The newly proposed method achieves state-of-the-art performance comparing to eight tumor saliency estimation approaches using two BUS datasets.

The smart mobile Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the field of modern wireless telecommunications. Picking operations are the key link in an entire warehousing operation, and congestion problems significantly contribute to managers’ decisions regarding the warehousing layout, the storage strategy, and the number of pickers. In this paper, the discrete Markov method is used to study the problem of picking congestion in a narrow-aisle double picking system with a traditional layout, an S-type picking path and a classified storage strategy. First, congestion models are constructed when the picker picks one item with a picking speed that is equal to the walking speed and when the picker picks multiple items on one picking surface under a picking speed that is very different from the walking speed. A sensitivity analysis involving parameter changes was carried out. Second, the paper compares and analyzes situations in which the picking speed is equal to the walking speed and in which there is a large difference between the picking speed and the walking speed and determines the impact of the picking speed, the number of goods picked on a picking surface, the differentiated classified storage strategy, and the number of picking surfaces on the picking congestion rate. The effectiveness of the mathematical model was verified by a MATLAB simulation, and the exploration and discovery for subsequent research were performed.

Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages.

Medical ultrasound imaging is widely used in clinical diagnosis because of its non-invasive, convenient and quick characteristics. However, due to its low image contrast, multiple artifacts, noise and lack of paired high-resolution and low-resolution image data sets, the task of super-resolution reconstruction of medical ultrasound images is more challenging. In this paper, the Two-Stage GAN network model was adjusted by CycleGAN generation and unsupervised learning methods, and the Two-Stage ZSSR (“Zero-Shot” Super-Resolution) CycleGAN network was proposed. The objective evaluation indexes PSNR and SSIM were raised to 40.8079 and 0.9953. The visual effect was also significantly improved.

Automatic tumor segmentation of thyroid ultrasound image is quite challenging due to the poor image quality. Recently the U-shaped network, especially U-Net, has achieved good results in medical image segmentation. In this paper, we proposed a modified U-Net model (ReAgU-Net), which embedded the improved residual units into the skip connection among the encoding and decoding path and introduce the attention gate mechanism to multiply the weight feature maps obtained from shallow layers and deep layers. Also, a hyperparameter is introduced to combine Focal-Tversky Loss, Dice Loss and Cross-entropy Loss to jointly guide the model optimization process. The experimental results demonstrate that the proposed approach outperforms the other U-shaped models.

Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality. Most tumor segmentation approaches achieve good performance on BUS images collected in controlled settings; however, the performance degrades greatly with BUS images from different sources. Tumor saliency estimation (TSE) has attracted increasing attention to solving the problem by modeling radiologists' attention mechanism. In this paper, we propose a novel hybrid framework for TSE, which integrates both high-level domain-knowledge and robust low-level saliency assumptions and can overcome drawbacks caused by direct mapping in traditional TSE approaches. The new framework integrated the Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the layer structure-based weighted map. The experimental results demonstrate that the proposed approach outperforms state-of-the-art TSE methods., Comment: 6 pages and 8 figures, Conference paper and accepted by ICPR 2018

Aiming at the problem of strong decentralization, poor compatibility and low sharing in the traceability system of domestic agricultural products, combined with the industrial chain of agricultural products, three general traceability models of agricultural products were proposed and established. Through the research of the model, it is concluded that the establishment of the traceability information flow model of agricultural products ensures seamless connection of all links in the agricultural product chain, and facilitates the traceability of agricultural products smoothly. The information sharing model of the traceability information of agricultural products improves the sharing of traceability information and is beneficial to the establishment of a comprehensive traceability system. The traceability function model of agricultural products provides a more general model for the traceability system. The validity of the model was proved by applying the model to the traceability system of winter jujube, providing a theoretical basis and a feasible solution for the construction of agricultural products.

In recent years, the Internet of things has developed rapidly. And the location-based service (LBS) is becoming more and more extensive. Service providers hold a large number of users’ information. In order to improve the quality of service, service providers increasingly use big data technology to provide more accurate services for users. At the same time, it aggravates the information disclosure of users’ privacy. From the perspective of the service provider, a mobile location privacy access control method based on game theory is proposed to solve the access control problem of mobile location privacy information. Firstly, the weight coefficient is set according to the location privacy influence factors, and then the access control threshold is calculated according to the privacy location leakage situation of the mobile location. Different visitor levels are set according to the threshold. In the process of access control, the prejudgement of the access behaviour is performed, and then the privacy information amount of the information requested for access is calculated according to the weights of the different information. Compare the result with the threshold and get the access control strategy. The strategy set is selected based on strategy matrix of game theory and thresholds are adjusted based on the calculation returns of strategy matrix. The effectiveness and practicability of the method are verified through the security analysis.

A new multiple-instance learning (MIL) algorithm which combines local distribution feature of samples with Citation-kNN is proposed. The local distance feature and local sparseness feature are considered. The voters are weighted according to their local distribution. The differently weighted schemes and their combinations are applied to Musk benchmark data set and Breast Ultrasound (BUS) Images. For musk data set, a bag represents a molecule. And instances in a bag represent low-energy conformations of the molecule. For BUS image classification, the image is viewed as a bag and its sub-regions are considered as the instances of the bag. And the image classification problem is converted into a MIL problem. In comparison with Citation-kNN and other methods, the proposed algorithm demonstrates competitive classification accuracy and adaptability.

Based on the technology of radio frequency identification (RFID), combined with Internet and information processing technology, intelligent refrigerator for food management is developed, food records the within the refrigerator is achieved. These data will be automatically uploaded to the production of intelligent refrigerator cloud data service platform, easy to be viewed by the user. And according to the food inside the refrigerator it can provide recipes. Wireless communication module is used to achieve the reception of Internet information. A week before shelf life of the food, a warning will be sent to the user. When the food over the shelf life, there will be usage warning to the user. When the food is reduced or not available through the information on the Internet, different businesses will be compared to choose the best food. At the same time the wireless communication module can also be used to connect intelligent terminal and the refrigerator, the situation inside the refrigerator is checked.

Interactive image segmentation is a popular and challenging task. User interactions, e.g., setting seeds or specifying bounding box, play a critical role in determining the performance of all interactive segmentation approaches. However, most methods focus on improving segmentation performance by integrating higher level information; and to the best of our knowledge, no work has been done to improve the effectiveness of user interactions yet. In this paper, we propose the effective interactive segmentation (EISeg) method based on Neutro-Connectedness, which provides user with objective visual clues for guiding interactions. The experiments demonstrate that the proposed EISeg method guides interaction effectively, and achieves better results with much less user interaction (averagely 2.3 foreground and 1.8 background seeds/image) than state-of-the-art approaches.

Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than the smoothness term based on region dissimilarity when the image has complicated background or low contrast. The primal-dual interior point method is applied to optimize the proposed QP in polynomial time. Extensive experiments demonstrate that the proposed method can outperform 10 state-of-the-art methods on three public benchmark datasets.

The age of Internet of things gives rise to more challenges to various secure demands when designing the protocols, such as object identification and tracking, and privacy control. In many of the current protocols, a malicious server may cheat users as if it was a legal server, making it vital to verify the legality of both users and servers with the help of a trusted third-party, such as a registration center. Li et al. proposed an authentication protocol based on dynamic identity for multi-server environment, which is still susceptible to password-guessing attack, eavesdropping attack, masquerade attack, and insider attack etc. Besides, their protocol does not provide the anonymity of users, which is an essential request to protect users’ privacy. In this article, we present an improved authentication protocol, depending on the registration center in multi-server environments to remedy these security flaws. Different from the previous protocols, registration center in our proposed protocol is one of parties in authentication phase to verify the legality of the users and the servers, thus can effectively avoid the server spoofing attack. Our protocol only uses nonce, exclusive-OR operation, and one-way hash function in its implementation. Formal analysis has been performed using the Burrows–Abadi–Needham logic to show its security.

Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches. First, the segmentation performance of ROI-based methods is sensitive to the initial ROI: different ROIs may produce results with great difference. Second, most seed-based methods need intense interactions, and are not applicable in many cases. In this work, we generalize the Neutro-Connectedness (NC) to be independent of top-down priors of objects and to model image topology with indeterminacy measurement on image regions, propose a novel method for determining object and background regions, which is applied to exclude isolated background regions and enforce label consistency, and put forward a hybrid interactive segmentation method, Neutro-Connectedness Cut (NC-Cut), which can overcome the above two problems by utilizing both pixel-wise appearance information and region-based NC properties. We evaluate the proposed NC-Cut by employing two image datasets (265 images), and demonstrate that the proposed approach outperforms state-of-the-art interactive image segmentation methods (Grabcut, MILCut, One-Cut, MGC_max^sum and pPBC)., Comment: 15 pages, 14 figures, 4 tables, journal

Tumor detection is the key issue of computer-aided diagnosis (CAD) systems using breast ultrasound (BUS) images. However, accurately and automatically locating the suspicious lesions in BUS images is still a very challenging job. In this paper, we propose a saliency model to describe the radiologists' visual (RV) attention. The main contributions of the paper are: (1) The saliency model is built based on biological hypotheses instead of using inflexible assumptions which are more robust, adaptive and objective. (2) Background based attention cue is also proposed. Before, the background information was either ignored or only used for excluding non-target areas. However, we find that the background information is very useful for establishing the anatomy constrains. The proposed method is evaluated using real breast ultrasound images and the result demonstrates a significantly improved performance comparing with that of the state-of-the-art methods.

Transesophageal echocardiography (TEE) is widely used to detect left atrium (LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary pattern variance (LBPV) features are extracted from region of interest (ROI). And the dynamic features are formed by using the information of its neighbor frames in the sequence. The sequence is viewed as a bag, and the ROIs in the sequence are considered as the instances. Multiple-instance learning (MIL) method is employed to solve the LAA thrombi detection. The experimental results show that the proposed method can achieve better performance than that by using other methods.

Multi-modality thyroid ultrasound image can provide more information about the lesion for the physician to diagnosis. In this paper, the thyroid B-mode ultrasound image and the elastogrom are viewed as a bag. And the local features of the B-mode image and the global features of the elastogram are considered as instances of the bag. Multiple-instance learning (MIL) method is employed to solve thyroid ultrasound image classification problem. Local features of B-mode are mapped to the concept space by self-organizing map (SOM). The hue component of elastogram is extracted to represent the elasticity information of the lesion. The bag vector is composed of the concept vector of the B-mode and global elasticity of elastogram. Finally, a traditional supervised learning method, support vector machine (SVM), is employed for classifying the lesion. The experimental results show that the proposed method can achieve better performance.

Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p

At present, the widely methods used to evaluate elastograms clinically are color score and strain ratio. The color score is a qualitative measure estimated by radiologists, and its high subjectiveness may lead to error. Although the strain ratio is a quantitative method, the region selected to calculate the value is subjective and its accuracy is still quite low. A new effective, accurate, and quantitative metric using computer aided diagnosis (CAD) techniques is proposed in this paper. The statistical features and texture features are extracted from the lesion region on the elastogram. The important and reliable features are selected by using Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm. The selected features were input to the SVM to classify the thyroid nodules. The experiment results confirm that the method is more accurate and robust than color score and strain ratio.