Intelligent Fusion Technology (United States)

Abstract Lymphocyte Activation Gene-3 (LAG-3) is a 503-amino acid transmembrane protein that modulates immune responses by negatively regulating the proliferation and activation of T cells - key effectors in adaptive immunity. The finely tuned expression of LAG-3, along with other immune checkpoints, prevents excessive immune activation and safeguards tissues from inflammation-induced damage. Importantly, the immune system also plays a critical role in tumor surveillance by recognizing and eliminating cells expressing neoantigens arising from somatic mutations. However, many cancers exploit immune checkpoint molecules like LAG-3 to dampen antitumor immunity. Elevated expression of LAG-3 within the tumor microenvironment contributes to immune evasion by suppressing cytotoxic T-cell activity. Consequently, inhibition of LAG-3 has emerged as a promising strategy for restoring immune function and enhancing anticancer immunity. This report presents the rational design and development of small-molecule inhibitors targeting LAG-3 through a novel semi-allosteric mechanism - a priori superior to classic (antibody) binding inhibitory - representing a next-generation therapeutic approach with potential applications in oncology and immune-related disorders.

This pilot study aimed to evaluate the effectiveness of motor imagery training (MIT) in enhancing maximal voluntary contraction (MVC) force among healthy older adults and inducing neural adaptations measured by electromyography (EMG) signals of both agonist and antagonist muscles, as well as the co-contraction index (CCI). Conducted with a single MIT group using a within-subject design, the study involved 12 right-handed elderly participants, with nine completing the 8-week training (5 sessions/week and 30 min/session). Elbow flexion MVC force and MVC EMG for biceps brachii (agonist) and triceps brachii (antagonist) were recorded pre- and post-training. Significant improvements in muscle strength were observed following the 8-week MIT, with a notable 22% increase (p < 0.05). Additionally, there was a significant 27% increase in EMG amplitude for the agonist muscle (p < 0.05), with no notable change in EMG amplitude for the antagonist muscle. Notably, this study is the first to demonstrate a significant decrease in CCI (-32%, p < 0.05) following MIT. These findings provide further evidence of MIT's efficacy in enhancing voluntary muscle strength through neural adaptations, particularly beneficial and safer for older individuals encountering challenges with conventional strength training methods.

In the zenith-horizon placement for achieving minimum geometric dilution of precision (GDOP), one access node or sensor is positioned along the z-axis, while the remaining nodes are placed symmetrically on a three-dimensional (3D) cone. This configuration yields the minimum GDOP at the cone’s tip, which we term the designated min-GDOP point. However, in practical localization applications, the unknown node is not necessarily located at this designated min-GDOP point; instead, it may be situated anywhere within an area. As a result, evaluating localization accuracy across the entire area, rather than at a single point, is more relevant. Averaged horizontal dilution of precision (HDOP) and GDOP across the region provide more meaningful metrics for system-wide performance than values computed only at a specific location. Although many recent positioning applications leverage multiple unmanned aerial vehicles (UAVs), many established fixed sensor deployments predate the widespread adoption of UAVs. This paper proposes a novel approach with a single UAV working in conjunction with existing fixed access nodes for positioning. This approach offers improved adaptability for fixed infrastructure while circumventing the expense of establishing entirely new UAV systems, thus providing a valuable compromise. We investigate the criteria of average HDOP and GDOP over the given area. The objective is to determine optimal UAV positions along the flight path that minimize the average HDOP and/or GDOP across the area. Due to the analytical complexity, we employ numerical methods. Our simulation results demonstrate that minimizing average HDOP and GDOP often requires different UAV positions, depending on the number of access nodes and the size of the area. Consequently, achieving simultaneous minimization of both metrics with a single UAV trajectory is generally infeasible. When minimizing the average HDOP with a small number of access nodes, aligning the UAV’s XY-plane angle with those of the stationary nodes, offset by , proves advantageous. This angular alignment becomes less critical as the number of access nodes increases. For scenarios where both HDOP and GDOP are important, UAV placement can be optimized by selecting appropriate trade-offs. Additionally, we quantify how increasing the number of access nodes improves the average HDOP and GDOP over the specified area.

Sensor, data, and information fusion techniques are typically implemented in a centralized approach that requires cloud servers to process the large amounts of data. Recently, collaborative computing approaches can support effective and efficient distributed and decentralized information fusion communication among many sensors at the edge. The panel highlighted opportunities and challenges of edge computing sensor fusion designs. Examples of opportunities included decentralized timely response, extended coverage, and resilient redundancy; all of which will be developed in the near future to enhance healthcare, smart cities, and surveillance. The challenges to make these opportunities a reality would include low size, weight, and power (SWaP) constraints, privacy and security concerns, as well as standardized data flow and architecture protocols. Common to the discussion was that with more heterogeneous edge sensing, data and information flow techniques are needed to harness the prospects of a distributed enterprise data fusion network.

Digital twins (DT) are becoming popular methods to leverage auxiliary information for real-time support. Digital Twins are closely related to the Dynamic Data driven Applications Systems (DDDAS) paradigm which utilizes real-time data to update first-principle simulations, while at the same time the simulation provides augmented data to enhance run-time instrumentation support. Hence, DDDAS using concepts in data assimilation, object estimation, and scientific modeling can be considered as a “dynamic digital twin”. Key advances in recent dynamic DT methods include techniques from artificial intelligence (AI) to provide trustworthy and explainable DTs (xDT). Among the many attributes desired for the AI-DT coordination, the DDDAS first-principle physics can enhance DT interpretability and explainability. This paper highlights opportunities to coordinate measured and augmented data from static, dynamic, and generative DTs for enhanced multi-modal systems engineered awareness.

The popularity of mmWave in 5G and future communications is hindered by challenging propagation environments, such as line-of-sight obstruction. Reconfigurable intelligent surfaces (RIS) address this issue by dynamically modifying wireless channels, thereby enhancing data rates, reducing latency, and improving reliability in non-line-of-sight scenarios. For high data-rate communication and precise mobile user localization, a large RIS is required, resulting in a high pilot overhead for channel estimation. To address this issue, we exploit a semi-passive RIS with sparsely distributed active RIS elements in lieu of fully passive RIS. This approach efficiently enables channel estimation both between the base station and the RIS as well as between the RIS and the mobile users. Structured covariance matrix interpolation optimally utilizes the array aperture from the sparsely placed active RIS elements. Recognizing the need for frequent channel estimation, we introduce a recurrent neural network-based model for sequential channel prediction, resulting in a significant reduction of the required training pilot signals. Simulation results affirm the capability and effectiveness of the proposed approach to enhance data transmission.

Within the recent years, the concept of Digital Twins (DT) emerged to support digital engineering physical-systems design in the processing and analysis of device components, data flows, and networked systems. As an element of systems engineering, the DT serves as a method for life cycle management for operations and maintenance through monitoring, diagnostics, and prognostics. Key to DT methods is the use of distributed sensors to monitor the system and determine the functioning with the use of physical design information, such as a series of distributed edge sensors and the layout of a physical electrical grid. While industries like industrial manufacturing, electrical power, space systems, and healthcare maintenance have embraced DT; other groups are utilizing the concept for analysis. Given that a large number of sensors are used to gather data on the health of system, it is natural that data fusion, estimation theory, and signals processing support digital twin fusion (DTwF); but there are a variety of challenges such as big data, distribution fusion, and edge analytics. The panel will discuss areas in which data and information fusion techniques enhance DT applications.

Apache Storm is a popular open-source distributed computing platform for real-time big-data processing. However, the existing task scheduling algorithms for Apache Storm do not adequately take into account the heterogeneity and dynamics of node computing resources and task demands, leading to high processing latency and suboptimal performance. In this thesis, we propose an innovative machine learning-based task scheduling scheme tailored for Apache Storm. The scheme leverages machine learning models to predict task performance and assigns a task to the computation node with the lowest predicted processing latency. In our design, each node operates a machine learning-based monitoring mechanism. When the master node schedules a new task, it queries the computation nodes obtains their available resources, and processes latency predictions to make the optimal assignment decision. We explored three machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN). Our experiments showed that LSTM achieved the most accurate latency predictions. The evaluation results demonstrate that Apache Storm with the proposed LSTM-based scheduling scheme significantly improves the task processing delay and resource utilization, compared to the existing algorithms.

Accurate localization and effective suppression of ground electromagnetic interference (EMI) signals are critical to maintaining uninterrupted satellite operations and the quality of services. For EMI sources emitting certain waveforms, such as continuous-wave or phase shift keying signals, their localization exploiting the time-varying Doppler signature due to satellite motion is an attractive approach. The high-speed motions and complicated orbits of satellites render the Doppler signatures varying with a high dynamic range and nonlinear instantaneous frequency signatures. As such, their processing requires high complexity which may not be practical for real-time processing, particularly for on-orbit satellite implementation for which the computational capability is highly limited. In this paper, we develop a low-complexity approach to iteratively estimate both the location and Doppler signatures of EMI signals. We introduce virtual ground references around the sensed scenarios and compute the Doppler difference between the measured results and the predicted ones for the virtual ground reference positions. By considering such Doppler difference in lieu of the Doppler frequencies, the Doppler dynamic range and the slope of the instantaneous frequency signatures are reduced to facilitate more effective time-frequency analysis and EMI source localization. We further consider the use of multiple virtual ground references for performance improvement, and a simple EMI localization approach is proposed through the interpolation of the Doppler difference frequencies observed at these virtual references. The effectiveness of the proposed approach is verified using simulation results.