The infinite increase in population, the pressure of survival, and the pressure of learning make the competition between people more and more fierce. Some college students have also been in a state of anxiety and panic for a long time, and mental health diseases have shown an explosive growth trend. The development of social networks such as Weibo, QQ, and WeChat not only provides more convenient communication methods for college students, but also provides a new emotional vent window for college students. They can record their living conditions in real time through social networks and interact with friends to express emotions and relieve stress. At the same time, the development of social networks has also provided a new way for the detection of depressed users. The current computer technology analyzes the user’s social network data to detect the user’s depression. This study uses text-level mining of Sina Weibo data from college students to detect depression among college students. First, collect text information of college student users in Sina Weibo, and construct the text information into input data that can be used for machine learning. Deep neural networks are used for feature extraction. An deep integrated support vector machine(DISVM) algorithm is introduced to classify the input data, and finally realize the recognition of depression. DISVM makes the recognition model more stable and improves the accuracy of depression diagnosis to a certain extent. Simulation experiments verify that the proposed depression recognition scheme can detect potential depression patients in the college student population through Sina Weibo data.

The acquisition of global context and boundary information is crucial for the semantic segmentation of remote sensing (RS) images. In contrast to convolutional neural networks (CNNs), transformers exhibit superior performance in global modeling and shape feature encoding, which provides a novel avenue for obtaining global context and boundary information. However, current methods fail to effectively leverage these distinctive advantages of transformers. To address this issue, we propose a novel single encoder and dual decoders architecture called STDSNet, which embeds the Swin transformer into the dual-stream network for semantic segmentation of RS imagery. The proposed STDSNet employs the Swin transformer as the network backbone in the encoder to address the limitations of CNNs in global modeling and encoding shape features. The dual decoder comprises two parallel streams, namely the global stream (GS) and the shape stream (SS). The GS utilizes the global context fusion module (GCFM) to address the loss of global context during upsampling. It further integrates GCFMs with skip connections and a multiscale fusion strategy to mitigate large-scale regional object classification errors resulting from similar features or shadow occlusion in RS images. The SS introduces the gate convolution module (GCM) to filter out irrelevant features, allowing it to focus on processing boundary information, which improves the semantic segmentation performance of small targets and their boundaries in RS images. Extensive experiments demonstrate that STDSNet outperforms other state-of-the-art methods on the ISPRS Vaihingen and Potsdam benchmarks.

Recent development in wireless power transfer enables a new paradigm of energy harvesting communications that can significantly impact in Internet of Things (IoT) applications. In this paper, we study the wirelessly powered IoT networks, in which a central node transmits radio frequency (RF) energy to power the IoT sensors, and the sensors harvest RF power to transmit data back to the central node. In this IoT network, the sensors transmit data according to their energy utilization policies, and the central node allocates charging powers to all sensors. We design the sensor energy utilization policies and the power allocation among sensors by maximizing the total data throughput of the system. In particular, the subproblem of energy utilization policy design is formulated as a Markov decision process and the power allocation subproblem is formulated as discrete optimization. By showing several key properties of these two subproblems, we propose low-complexity algorithms to solve the subproblems optimally. We demonstrate the performance gains of the proposed algorithms over some simple heuristics via simulations in terms of the total data throughput in wireleslly powered IoT networks.

Person reidentification, as one of the most important areas in the video surveillance field, is a crucial task in computer vision. It has attracted more and more attention in both academic research and industry due to its extremely high application value. However, the recognition accuracy of person reidentification is subjected to factors, such as illumination, pose, occlusion, and viewpoint. To alleviate the effect of such factors, the multiscale Retinex with color restoration (MSRSC) algorithm is adopted to preprocess the original images so that the color information can be restored and the illumination condition can be improved. To obtain pose-invariant features (PIFs), the convolution pose machine that can generate the body joint points of pedestrians is applied to divide the body into seven parts, the pose transform network is then used to align the body parts, and finally, the PIFs can be obtained from a designed pseudo-Siamese network by using the original and aligned images as the training samples. To further improve recognition accuracy, a reranking method based on bidirectional k-nearest neighbors (KNN) is presented to optimize the ranking list. Experimentally, the proposed method is implemented on three data sets: viewpoint invariant pedestrian recognition (VIPeR), CUHK03, and Market1501. The results demonstrate our method outperforms the other methods, as a result of both the representation of a more discriminative feature descriptor and the introduction of a reranking method.