Introduction: Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD.Methods: We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients’ facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients.Results: We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales.Discussion: The BDD can effectively measure the current state of patients’ depression and its changing trend according to the patient’s expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.

Deep learning-based joint source-channel coding (deep JSCC) has been demonstrated to be an effective approach for wireless image transmission. However, many current approaches utilize an autoencoder framework to optimize conventional metrics such as Mean Squared Error (MSE) and Structural Similarity Index (SSIM), which are inadequate for preserving the perceptual quality of reconstructed images. Such an issue is more prominent under stringent bandwidth constraints or low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we propose DiffJSCC, a novel framework that leverages the prior knowledge of the pre-trained Stable Diffusion model to produce high-realism images via the conditional diffusion denoising process. First, our DiffJSCC employs an autoencoder structure similar to prior deep JSCC works to generate an initial image reconstruction from the noisy channel symbols. This preliminary reconstruction serves as an intermediate step where robust multimodal spatial and textual features are extracted. In the following diffusion step, DiffJSCC uses the derived multimodal features, together with channel state information such as the signal-to-noise ratio (SNR) and channel gain, to guide the diffusion denoising process through a novel control module. To maintain the balance between realism and fidelity, an optional intermediate guidance approach using the initial image reconstruction is implemented. Extensive experiments on diverse datasets reveal that our method significantly surpasses prior deep JSCC approaches on both perceptual metrics and downstream task performance, showcasing its ability to preserve the semantics of the original transmitted images. Notably, DiffJSCC can achieve highly realistic reconstructions for 768×512 pixel Kodak images with only 3072 symbols (<0.008 symbols per pixel) under 1dB SNR channels. The source code is available at https://github.com/mingyuyng/DiffJSCC.

In this article, we present an ultra-low-power multi-modal signal processing system on chip (SoC) audio and image multi-modal intelligence (AIMMI) that integrates a versatile deep neural network (DNN) engine with audio and image signal processing accelerators for multi-modal Internet-of-Things (IoT) intelligence. In order to get high energy efficiency under resource-constrained IoT scenarios, AIMMI features three efficiency-boosting techniques: 1) 2-MB on-chip non-volatile magnetoresistive RAM (MRAM) to store all DNN weights with MRAM-cache microarchitecture that incorporates dynamic power gating to reduce both leakage and dynamic power consumption; 2) a deliberate power management scheme that enables optimized power modes under different operating situations; and 3) a novel reconfigurable neural engine (NE) with energy-efficient dataflow for comprehensive DNN instructions. Fabricated in TSMC 22-nm ultra-low leakage (ULL) technology with MRAM, AIMMI achieves up to 3–10-TOPS/W peak energy efficiency and consumes only 0.25–3.84 mW. It demonstrates convolutional neural network (CNN), generative adversarial network (GAN), and back-propagation (BP) operations on a single accelerator SoC for multi-modal fusion, outperforming state-of-the-art DNN processors by 1.4 $\times$ –4.5 $\times$ in energy efficiency.

LiDAR sensors are integral to autonomous driving and augmented reality applications, providing essential depth information. However, managing the substantial volume of LiDAR point cloud data is crucial for practical application, necessitating efficient compression algorithms. Similar to other data compression domains, point cloud compression aims to spatially and temporally decorrelate information while preserving reconstruction quality. Addressing real-world challenges such as sensor pose retrieval inaccuracies or motion estimation failures, we introduce H-PCC, a hybrid LiDAR data compression pipeline that combines a range image prediction-based dynamic compression method with a novel point clustering octree-based static method. Additionally, we demonstrate that our content adaptive down-sampling technique using associated camera images can significantly enhance the LiDAR data compression rate with minimal impact on the accuracy of machine perception tasks. Extensive evaluations on the KITTI and Oxford datasets, using both traditional perceptual quality metrics and machine vision tasks, show that H-PCC surpasses state-of-the-art LiDAR data compression methods.

In order to balance the transmission efficiency and energy consumption of the power system sensor network, we proposed an algorithm that improved the parameter adjustment mode of the particle swarm optimization (PSO) algorithm. To complete effective routing and clustering, and to extend the lifetime of the network, we analyze the energy level and transmission status of sensor nodes and other parameters. The simulation results show that the solution has a more balanced energy consumption while meeting the data transmission efficiency of power system equipment.

In order to promote the accuracy and caculating speed of load active power and reactive power forecasting, this paper proposes an ultra-hort-term load forecasting method based on optimal variational mode decomposition (OVMD) and CatBoost. First, use OVMD to decompose load data into Intrinsic Mode Function (IMF) with different characteristics and frequencies, and then the CatBoost algorithm is used to predict each component respectively, and the complete prediction result can be obtained by super-imposing the prediction result of each component. Using this method to predict the power data of a certain power load in Hainan Province, China, verifies the proposed OVMD-CatBoost method has great accuracy in predicting power load data.