Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment.

Real-time moving target trajectory prediction is highly valuable in applications such as automatic driving, target tracking, and motion prediction. This paper examines the projection of three-dimensional random motion of an object in space onto a sensing plane as an illustrative example. Historical running trajectory data are used to train a reserve network.

Regional consensus of switched positive multi-agent systems with multiple equilibria.

This paper investigates regional proportional-integral-derivative consensus of switched positive multi-agent systems with multiple equilibria. A distributed proportional-integral-derivative control protocol is developed by integrating the communication protocol, agent state, and consensus error. A novel switched positive consensus error system is established and analyzed using copositive Lyapunov functions.

Diagnosis of Parkinson's disease by eliciting trait-specific eye movements in multi-visual tasks.

Background: Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.

Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar.

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks.

Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction.

In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective technique for preserving the quality of service. However, one important problem that has not been addressed in prior studies is how to manage network load while maintaining service-level agreements for time-varying resource demands.

A lightweight multi scale fusion network for IGBT ultrasonic tomography image segmentation.

The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis.

1.6-Inch Transparent Micro-Display with Pixel Circuit Integrated microLED Chip Array by Misalignment-Free Transfer.

Recent advances in mass transfer technology are expected to bring next-generation micro light-emitting diodes (µLED) displays into reality, although reliable integration of the active-matrix backplane with the transferred µLEDs remains as a challenge. Here, the µLED display technology is innovated by demonstrating pixel circuit-integrated micro-LEDs (PIMLEDs) and integrating them onto a transparent glass substrate. The PIMLED comprises of low-temperature poly-silicon transistors and GaN µLED.

A tunable multi-timescale Indium-Gallium-Zinc-Oxide thin-film transistor neuron towards hybrid solutions for spiking neuromorphic applications.

Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase their effectiveness. Such hardware, mainly digital, is typically built on mature silicon nodes. Future artificial intelligence applications will demand the execution of tasks with increasing complexity and over timescales spanning several decades.

Aggregation-induced emission(AIE)for next-generation biosensing and imaging: A review.

Luminescence technology is a powerful analytical tool for biomedical research as well as for marker detection. Luminescent materials with aggregation-induced emission (AIE) properties have attracted extensive research interest, and their unique luminescence characteristics, biocompatibility, and sensitivity make them useful for the development of fluorescence-turn-on biosensors with superior sensitivity. While numerous reviews have focused on the design of AIEgens, comprehensive summaries on the strategies for biosensor preparation and application fields remain limited.

Complex control of polaritons based on optical Stark potential.

Effectively controlling exciton-polaritons is crucial for advancing them in optical computation. In this work, we propose utilizing the valley-selective optical Stark effect (OSE) as an all-optical way to achieve the spatiotemporal control of polariton flow. We demonstrate the polarization-selective concentration of polaritons at pre-determined locations by wavefront shaping of the polaritons through an in-plane bar-code potential induced by the OSE, which helps overcome the intra-cavity disorder in potential distribution.

A generalized maximum correntropy based constraint adaptive filtering: Constraint-forcing and performance analyses.

The quadratic cost functions, exemplified by mean-square-error, often exhibit limited robustness and flexibility when confronted with impulsive noise contamination. In contrast, the generalized maximum correntropy (GMC) criterion, serving as a robust nonlinear similarity measure, offers superior performance in such scenarios. In this paper, we develop a recursive constrained adaptive filtering algorithm named recursive generalized maximum correntropy with a forgetting factor (FF-RCGMC).

Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models.

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images.

An efficient deep unrolling network for sparse-view CT reconstruction via alternating optimization of dense-view sinograms and images.

. Recently, there have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and achieving significant results.

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet.

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.

An autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation.

The use of rubber-tapping robots capable of autonomous navigation in place of manual rubber-tapping is a growing trend, but the challenging multi-objective navigation task in forest environments impedes their autonomous operation. To tackle this issue, an autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation is designed. This navigation decision mechanism is comprised of obtaining coordinates of target points (OCTP), selecting the next coordinate (SNC), generating the additional coordinates (GAC), and optimizing the planned paths (OPP).

Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks.

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) for ubiquitous data acquisition and tracking. However, the limited battery life of sensor nodes poses significant challenges to the long-term scalability and sustainability of these networks. Wireless power transfer technology offers a promising solution by enabling the recharging of energy-depleted nodes through a wireless portable charging device (WPCD).

Classification of Hyperspectral Images of Explosive Fragments Based on Spatial-Spectral Combination.

The identification and recovery of explosive fragments can provide a reference for the evaluation of explosive power and the design of explosion-proof measures. At present, fragment detection usually uses a few bands in the visible light or infrared bands for imaging, without fully utilizing multi-band spectral information. Hyperspectral imaging has high spectral resolution and can provide multidimensional reference information for the fragments to be classified.

ASIP Performance Enhancement by Hazard Control through Scoreboard.

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration.