Near-field propagation, especially when facilitated by reconfigurable intelligent surface (RIS), has become a significant field of inquiry in recent times. Despite this, there remains a noticeable absence of an exhaustive literature review concerning RIS-empowered near-field technologies. This paper seeks to bridge this knowledge gap by presenting a concise overview of near-field principles, coupled with a systematic examination of the latest advancements in RIS-driven near-field technologies. It specifically concentrates on three pivotal areas: the establishment of pervasive near-field wireless propagation environments through RIS, the introduction of novel near-field frameworks for 6G networks via RIS, and the array of challenges inherent in RIS-based near-field technologies. The objective of this technical review is to bolster the progression and innovative exploration in the realm of RIS-oriented near-field technologies.

This study entailed the derivation of an inverse airflow dynamics model that causes pneumatic transmission delay due to the long air tube in pneumatic artificial muscle (PAM) control. The inverse model of the derived airflow dynamics was used in designing a feedforward pressure control command for the proportional pressure control valve. We also modulated the feedback command based on the proposed pressure control for precise force tracking performance. The proposed methods were validated by varying the tube lengths in the pressure tracking task and force tracking task. The tracking tasks with the sinusoidal desired profile of 2.5 Hz were successfully achieved using the proposed method. The results indicate that the proposed method can compensate for the delay due to the airflow dynamics in the long air tube and improve the control performance.

The 3D point cloud plays a crucial role in deep learning-based vision tasks by providing precise spatial and depth information, leading to its increasing importance in various applications. However, the sparse nature of 3D point clouds poses computational challenges. Researchers have explored the Submanifold Sparse Convolutional Network (SSCN) for processing point cloud data while preserving sparsity. Nevertheless, existing Convolutional Neural Network (CNN) accelerators encounter difficulties in effectively handling SSCNs, prompting recent studies to focus on developing dedicated accelerators for point cloud networks to improve processing performance. This brief presents a specialized hardware architecture designed for SSCNs to address the challenges of effectively processing sparse 3D point clouds. The proposed accelerator achieves a significant 2.51× improvement in throughput density compared to previous works, highlighting its effectiveness in point cloud processing.

A document by Yutao Ma. Click on the document to view its contents.

Unintended islanding refers to the disconnection of a grid section in a sequence of faults and uncontrolled continued operation of this section detached from the interconnected grid by chance. This condition should be avoided at the low-voltage level in particular, especially to ensure personal protection during repair work and to prevent damage to devices and equipment or blockages of the AWE. Therefore, current inverters are equipped with methods that recognize this condition and disconnect from the mains. It is believed that grid-forming technologies increase the risk of these islands developing and operating uncontrollably. There is therefore a need to investigate the effects of their future integration into the grid and, if necessary, to develop measures to safely detect and prevent the unwanted formation of islanded grids. The integration of grid-forming converters into the interconnected grid raises the question of how this will affect the ability of existing systems to detect islanding. This was examined in more detail in this study.

Background: In an era of increasing globalization and digital interaction, the demand for efficient, multilingual customer service solutions is paramount. This study introduces a novel software implementation of a conversational multilingual avatar-based interactive multifunctional AI kiosk and mobile application with computer vision, which is designed to address the challenges in delivering seamless, interactive, and inclusive customer service. The system integrates advanced technologies, including natural language processing, computer vision, and artificial intelligence, to create a user-friendly, avatar-based interactive platform. The purpose of this study is to explore the software's architecture, functionality, and effectiveness in providing a multilingual, conversational interface for diverse user demographics, particularly in kiosk and mobile application formats.Results: The implementation demonstrated robust performance in several key areas. The avatar-based interface, created using ReadyPlayerMe, delivered a highly interactive and engaging user experience, enhanced by computer vision technologies like OpenCV and Google ARCore for accurate face detection and tracking. The system effectively utilized Whisper API and Google Cloud's Speech-to-Text (STT) and Text-to-Speech (TTS) services to facilitate realtime language translation and processing. This ensured seamless communication in multiple languages. The integration with OpenAI's GPT-3.5 model, prompt-tuned to specific business data, provided contextually relevant and accurate responses, significantly enhancing the user experience. Critical issues such as privacy, data security, and real-time processing were effectively addressed, ensuring user trust and system efficiency. Additionally, the system demonstrated an ability to cater to a broad spectrum of industries, making it a versatile tool in varied business scenarios.Conclusions: The multifunctional conversational AI software effectively bridges the communication gap in customer service interactions, providing a scalable, secure, and user-friendly solution. Its ability to engage users in their native language, coupled with its interactive avatar-based interface, marks a significant advancement in the realm of digital customer service solutions. This implementation holds substantial potential for widespread adoption across various sectors, offering implications for enhancing global customer service standards, business efficiency, and user inclusivity. The success of this system sets the stage for future innovations in AI-driven customer interaction technologies.

Deep brain stimulation is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fibre activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signalling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.

A multiple scattering model for passive radiative transfer (RT) in vegetation that accounts for the vertical profile of the plant structure is developed, offering advancements over the commonly-used single-layer uniform scattering models prevalent in the vegetated land surface microwave remote sensing. The proposed model takes into account the complexities of the canopy morphology with vertical heterogeneity, enabling the representation of overlapping vegetation species applicable to diverse plant types and growth stages. Additionally, it serves as a valuable tool for understanding the influence of the vegetation vertical structure on the microwave brightness temperatures. The model is constructed based on high-order solutions to the RT equations, obtained through a numerical iterative approach with an efficient interpolation scheme for algorithm acceleration. This methodology facilitates the accurate distinction of the contributions to the brightness temperature from each scattering order and scattering mechanism, ensuring a comprehensive consideration of multiple scattering effects within various vegetated scenarios. The model is validated using the SMAPVEX12 L-band forest data set, encompassing a wide range of soil moisture variations. Comparisons are made between the brightness temperatures simulated by the newly developed multiple-scattering model with a continuous profile or layered profile and those obtained from a uniform single-layer model. Results demonstrate significant improvements in the multi-layered or the continuously profiled model, showing improved agreement with the measured brightness temperatures. Furthermore, the proposed model is parameterized by matching the high-order solutions to the RT equation to the widely adopted reduced order albedo-tau formalism. The resulting equivalent parameters are linked to the geometries and the electromagnetic properties of the vegetation layer, while also incorporating the effects of multiple scattering. Comparative analysis of the equivalent parameters derived from the layered model and those derived from the single-layer model reveals that the vertical heterogeneity of the vegetation structure has a notable influence on the effective scattering albedo and it yields a value more consistent with the albedo as chosen in the SMAP/SMOS inversion algorithms. Meanwhile, the impact of the vegetation vertical profile on the effective optical thickness and the effective transmissivity of the vegetation layer is weak.These insights are essential for the retrieval of soil moisture and vegetation characteristics including the plant vertical structures in microwave remote sensing.

We propose an integrated behavior and motion planning framework for the automated lane-merging problem. The behavior planner combines search-based planning with game theory to model the interaction between vehicles and select multivehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process. To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions. In practice, however, the surrounding vehicles might deviate from the computed equilibrium trajectories. Thus, we introduce a branch model predictive control (BMPC) framework to account for the uncertain behavior modes of the surrounding vehicles. A tailored numerical solver is developed to enhance computational efficiency by leveraging the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios.

This letter, based on measuring the admittance of a black-boxed grid-following inverter (GFLI) at seven distinct operating points to synthesize an operating-point-parameterized state-space model (OPP SSM), presents an efficient method for analytical prediction of the maximum transferable active power (MTAP) injected by the black-boxed GFLI into the grid under any arbitrary grid condition and reactive power injection level. The proposed method obviates the need for repetitive electromagnetic transient time-domain simulations or frequency-domain impedance criteria by gradually increasing active power output until the MTAP is reached. The feasibility of the synthesized OPP SSM for MTAP prediction has been experimentally validated on a power-controlled GFLI, demonstrating its potential for aiding the integration of inverter-based resources in modern power systems.

The demand for more accurate and visually pleasing images is increasing with the number of digital photos taken daily. However, the images captured by modern cameras are degraded by noise, which leads to deteriorated visual image quality. The de-noising approaches existing in the literature cannot reduce noise without losing image features (edges, corners, and other sharp structures). The developed de-noising method can detect and de-noise the noisy pixels without losing image features at higher and lower noise levels. The present approach can filter the noise at or above 90% noise density and preserve image details to enhance the image processing task.

This paper explores the intersection of Artificial Intelligence (AI) and digital storytelling in marketing, focusing on how AI-driven techniques can enhance emotional attachment and influence consumer behavior. With the rapid advancement of AI, its integration into marketing strategies has become crucial, particularly for personalizing consumer experiences and enhancing brand narratives. This study investigates AI's role in creating emotionally engaging narratives, a largely unexplored area in marketing and advertising. The research is motivated by the need to understand the dynamics between AI-driven techniques and emotional attachment in digital marketing. The paper hypothesizes a significant relationship between consumers' emotional attachment to brands, influenced by AI-driven storytelling, and their purchasing behavior. A mixed-methods research approach is employed to test this hypothesis, combining a survey with detailed interviews. The study assesses how emotional attachment, influenced by AI and storytelling, impacts consumer purchasing decisions and brand loyalty in online shopping. It also evaluates the effectiveness of AI-driven storytelling techniques in digital marketing campaigns from the perspective of online consumers. Preliminary findings suggest that while emotional attachment significantly influences consumer purchasing behavior, other factors also play a crucial role. The study reveals that AI's role in marketing is valued, but the essence of storytelling should remain grounded in human experiences. The paper concludes that the future of digital marketing lies in a harmonious blend of AI and traditional storytelling, where AI's data-driven insights complement the authenticity and emotional resonance of narratives. This research contributes to the understanding of AI's potential in enhancing emotional attachment-driven branding and online selling performance, offering insights for future strategies in digital marketing.

We present studies of a novel plastic scintillator branded M600, which was developed and provided by Target Systemelektronik. This new material is, in contrast to other solid organic scintillators offered by commercial providers, based on a polyurethane matrix complemented with scintillating and wavelength-shifting additives. This paper covers measurements of absolute light output, LO, (photons per 1 MeV-ɣ, ɣ-non-proportionality (NP), light pulse shapes, and neutron-gamma (n/ɣ) discrimination. The measurements were carried out either using standard calibration gamma sources (LO, NP) or in a mixed field of neutron and ɣ radiation from an intense (~4 × 105 neutrons/s/4π) AmBe source (n/ɣ discrimination). The measured light output was 9650±1000 ph/MeV, and the PSD's figure of merit (FoM) was found as 2.2±0.1 at 1000 keVee for Ø1" × 1" M600 sample. The bigger sample (Ø2" × 2") exhibits about 25% lower LO and poorer FoM when compared to the Ø1" × 1" M600, due to self-absorption.

The IIoT network involves smart sensors, actuators, and technologies extending IoT capabilities across industrial sectors. With the rapid development in connected technology and communications in industrial applications, IIoT networks and devices are increasingly integrated into less secure physical environments. Anomaly detection in IIoT is crucial for cybersecurity. This paper proposes a novel anomaly detection model for IIoT systems, leveraging a hybrid deep learning (DL) model. The hybrid DL approach combines Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for anomaly detection in IoT edge computing. The proposed CNN+GRU model achieves a notable 94.94% accuracy, underscoring the importance of careful model selection for IIoT anomaly detection. The paper suggests exploring XGBoost with hybrid CNN+GRU architectures as a future direction for high accuracy in complex IIoT contexts. The Experimental results indicate a 96.41% accuracy, excelling in metrics like false alarm rate (FAR), recall, precision, and F1score. Based on these findings, we recommend future researchers consider advanced hybrid architectures and enhance efficiency using XGBoost with hybrid CNN+GRU. This approach holds promise for significant contributions to IIoT systems' security and Performance evolution.

This paper describes the process followed to implement a system to characterize the complex permittivity of materials in the 10-330 GHz frequency band. Firstly, the method used and the system's calibration process are shown, consisting of a double calibration TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line). Subsequently, a smoothing technique is used to improve the accuracy of the results. Finally, a test is performed on quartz and glass fiber samples, showing that the results are quite reliable over the entire measured bandwidth.

This work outlines the procedure to establish a system for assessing materials' complex permittivity and permeability within the 110-170 GHz frequency range. We present the employed methodology and the calibration procedure for the system, incorporating a dual approach using TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line) methods. Following that, a smoothing technique is used to enhance the accuracy of the results. Tests were conducted on a HIPS sample to validate the system's performance, demonstrating the results' reliability across the entire measured bandwidth.

Unsupervised ML-based approaches have emerged for driving critical decisions about training data samples to help solve challenges in many life critical applications. This paper proposes parallel and distributed computing unsupervised ML techniques to improve the execution time of different ML algorithms. Various unsupervised ML models are developed, implemented, and tested to demonstrate the efficiency, in terms of execution time and accuracy, of the serial methods as compared to the parallelized ones. We developed sequential, parallel, and distributed cloud computing unsupervised ML models based and determined the most efficient model through comparative analysis. As a case study, sequential, parallel, and distributed approaches of Simple K-Means, Minibatch K-means, and Fuzzy C-Means are investigated to study the developed models' efficiency using country datasets for multiple organizations to train and test the developed model. Parallel and distributed computing models are developed utilizing could computing architect, i.e., cloud Amazon SageMaker, to study their efficiency in the execution time and model accuracy. The results show that the proposed parallel and distributed Fuzzy C-Means outperforms the other two clustering methods in terms of execution time with 0.932ms and 0.623ms with a minimal impact on the accuracy of the developed models.

Context-aware computing applications depend on embedded hardware systems, utilizing sensors embedded in the hardware to gather real-time data and interact with specialized OS software (firmware) for autonomous processing and analysis of intelligent data. Securing embedded IC hardware systems and ensuring their trustworthiness requires an intelligent approach to effectively detect spontaneous hardware Trojans (HTs) insertions and modification attacks aiming to compromise the system's integrity, potentially leaking sensitive information or causing destruction. Implementing robust and advanced intrusion detection systems against supply chain hardware Trojan to countermeasure and continuous monitoring the behavior of these malicious hardware is essential to enable trust in context-aware computing applications. AI-enabled hardware side-channel analysis, involving power and timing assessments, assists in detection of anomalies that may signify potential Trojans. This paper propose intelligent AI approach utilizing unsupervised machine learning in conjunction with hardware side-channel analysis to eliminate the need for golden data samples and efficiently detect hardware Trojan detection. Employing unsupervised clustering, the methodology not only showcased a superior false positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts such as the K-Nearest Neighbors (KNN) classifier, Support Vector Machine (SVM), and Gaussian classifier-methods reliant on the availability of golden data for training. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a groundbreaking paradigm in hardware Trojan detection, accentuating its potential in bolstering the integrity of semiconductor IC supply chains.