With the popularization of computer-aided diagnosis (CAD) technologies, more and more deep learning methods are developed to facilitate the detection of ophthalmic diseases. In this article, the deep learning-based detections for some common eye diseases, including cataract, glaucoma, and age-related macular degeneration (AMD), are analyzed. Generally speaking, morphological change in retina reveals the presence of eye disease. Then, while using some existing deep learning methods to achieve this analysis task, the satisfactory performance may not be given, since fundus images usually suffer from the impact of data imbalance and outliers. It is, therefore, expected that with the exploration of effective and robust deep learning algorithms, the detection performance could be further improved. Here, we propose a deep learning model combined with a novel mixture loss function to automatically detect eye diseases, through the analysis of retinal fundus color images. Specifically, given the good generalization and robustness of focal loss and correntropy-induced loss functions in addressing complex dataset with class imbalance and outliers, we present a mixture of those two losses in deep neural network model to improve the recognition performance of classifier for biomedical data. The proposed model is evaluated on a real-life ophthalmic dataset. Meanwhile, the performance of deep learning model with our proposed loss function is compared with the baseline models, while adopting accuracy, sensitivity, specificity, Kappa, and area under the receiver operating characteristic curve (AUC) as the evaluation metrics. The experimental results verify the effectiveness and robustness of the proposed algorithm.

The accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistics packaging box defect detection (LPDD) on the basis of support vector machine (SVM). Firstly, this paper designs a new mean denoising template and Laplace sharpening template, which are more suitable for logistics packaging based on image preprocessing, image enhancement and other relevant technical theories. Then in the stage of noise removal, this paper proposes an improved morphological method and a gray morphological edge detection algorithm. The edge defect detection of a gray image is carried out by combining the above two methods. Hence, LPDD extracts the features of logistics packaging box by using scale-invariant feature transform (SIFT) algorithm and designs SVM classifiers to classify the logistics package defects. This paper uses a large number of samples to train, learn and test the designed SVM classifier. The simulation results show that the proposed LPDD method can accurately detect two common types of defects in logistics packaging boxes with higher accuracy and less computational costs, which meets the requirements of manufacturers on the classification and recognition of defects in machine vision detection system.

With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from data in order to solve the optimization problem in complex systems. There are many forms of data storage. Among them, text data is an important data type that directly reflects semantic information. Text vectorization is an important concept in natural language processing tasks. Because text data can not be directly used for model parameter training, it is necessary to vectorize the original text data and make it numerical, and then the feature extraction operation can be carried out. The traditional text digitization method is often realized by constructing a bag of words, but the vector generated by this method can not reflect the semantic relationship between words, and it also easily causes the problems of data sparsity and dimension explosion. Therefore, this paper proposes a text vectorization method combining a topic model and transfer learning. Firstly, the topic model is selected to model the text data and extract its keywords, to grasp the main information of the text data. Then, with the help of the bidirectional encoder representations from transformers (BERT) model, which belongs to the pretrained model, model transfer learning is carried out to generate vectors, which are applied to the calculation of similarity between texts. By setting up a comparative experiment, this method is compared with the traditional vectorization method. The experimental results show that the vector generated by the topic-modeling- and transfer-learning-based text vectorization (TTTV) proposed in this paper can obtain better results when calculating the similarity between texts with the same topic, which means that it can more accurately judge whether the contents of the given two texts belong to the same topic.

Capture effect is very common in wireless communication systems. In a passive radio frequency identification (RFID) system, even though multiple tags backscatter their signals to a reader simultaneously, one of the tags will be successfully identified due to the capture effect. In this letter, we propose an algorithm which will optimize the identification efficiency of a dynamic frame length ALOHA RFID protocol under capture effect. The proposed algorithm estimates the number of tags and the probability of capture effect. From these parameters, an optimal frame length can be obtained. The advantage of the algorithm is that it does not need many steps to search an extreme value for the estimation. It thus reduces the computational complexity. Computer simulation results show that the proposed method's identification efficiency is almost identical to existing algorithms, but at lower computational complexity.

Accurate nodes’ localization is a key problem in wireless sensor network (WSN for short). This paper discusses and analyzes the effects of Voronoi diagram in 3D location space. Then it proposes Sequence Localization Correction algorithm based on 3D Voronoi diagram (SLC3V), which introduces 3D Voronoi diagram to divide the 3D location space and constructs the rank sequence tables of virtual beacon nodes. SLC3V uses RSSI method between beacon nodes as a reference to correct the measured distance and fixes the location sequence of unknown nodes. Next, it selects optimal parameterNand realizes the weighted location estimate withNvalid virtual beacon nodes by normalization process of rank correlation coefficients. Compared with other sequence location algorithms, simulation experiments show that it can improve the localization accuracy for nodes in complex 3D space with less measurements and computational costs.

This paper studies a distributed antenna system (DAS) network with radio frequency (RF) energy harvesting (EH) technology where the distributed antenna ports (DAPs) transmit energy and information to multiple users simultaneously. The time division multiple access (TDMA) protocol is adopted, so for each time slot is allowed to receive information, while the rest of the users harvest energy. In order to maximize the system energy efficiency (EE), subject to the EH requirements and data rate requirements of the users, the transmission time and power assignment are jointly optimized. In order to deal with this non-convex problem, based on Dinkelbach theory and the block-coordinate descent (BCD) scheme, an efficient algorithm is designed to obtain the global optimal solution. Then, simulation results are presented to show that the proposed method achieves much higher system EE compared with benchmark methods. With the increase of the user’s minimum information rate, the system EE decreases rapidly.