Graphene antennas operating at mm-wave and THz frequencies enable miniaturization by being smaller than its metallic counterparts, while additionally providing frequency tunability capabilities. These characteristics are interesting for 6G short-range communication applications, where the antenna size is still one of the largest and, consequently, costly components inside a chip. In this work, a tunable planar antenna for operation at terahertz frequencies based on a graphene stack is proposed and analyzed. The proposed antenna consists of a stack of two graphene patches separated by a thin dielectric to provide higher efficiency and gain. This concept can broadly be applied to future THz communication systems because it can directly be integrated into transceiver units by being compatible with CMOS processes and manufacturing techniques. We use a full-wave electromagnetic solver to evaluate and compare the performance of this antenna to a simple graphene patch antenna in the range of 220 - 325 GHz. The results show that an increase in efficiency and gain can be accomplished. We also demonstrate the frequency tuning possibility of the antenna, which can be achieved by the electric field effect in the graphene stack by applying a bias voltage between the two graphene sheets.

Breast cancer, cancer with the highest incidence rate worldwide, is also the most common cause of cancer death for women in all countries. Among them, brain metastasis (BrM) is a characteristic of a severely poor prognosis for breast cancer. Due to the unique anatomy of brain metastases, the understanding of the tumor immune microenvironment (TME) of BrM from breast cancer is limited, which hinders our diagnosis and treatment of breast cancer patients. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize immune cells in the cerebrospinal fluid (CSF) of BrM patients to describe the immune landscape of BrM. We identified 9 cell subtypes: T cells, Tregs, MonoMacs, Macs, TAMs, Myeloid cells, DCs, Endo_Fibs and Glioblastals from CSF samples and analyzed their functions in the TME. In addition, we classified T cell, macrophage and DC subclusters by UMAP analysis and compared the differential genes and biological processes of each subcluster with changes in prognosis. Finally, we also analyzed cell-cell interactions among cell subclusters and their differences. As our research focuses on the tumor immune microenvironment of brain metastasis, we believe that our research will be attractive not only to cancer immunologists but also to the interdisciplinary readership of Cancer Science.

This paper presents a novel feature-line points tracking algorithm designed to be highly efficient and accurate for real-time monocular line-based SfM applications in man-made environments. The proposed algorithm exploits feature-line points proprieties to detect and extract long line segments and ensure their temporal evolution in the subsequent frames while handling line segment detection and tracking issues such as noisy images, over-segmentation, edge thresholding, false detections, occlusions, and ambiguous matches. To fulfill the real-time and embeddability constraints of our proposed method, we adopt the hardware/software co-design to achieve a suitable implementation with a scalable FPGA-based embedded heterogeneous architecture. Based on this, experimental results show the portability of the line segment detector block of this proposed algorithm on scalable FPGA-based embedded heterogeneous architectures, improving the acceleration of the sequential execution by about 98\%, whereas a 53\% improvement with GPU-based heterogeneous architectures. For qualitative and quantitative assessment, we exhibit extensive benchmarking with other state-of-the-art algorithms regarding feature-line segment extraction and tracking, showing the efficiency of the proposed algorithm and ensuring very hopeful real-time performance using two common datasets.

The increase of IoT devices and the emergence of smart cities have revolutionized urban development, offering numerous benefits while addressing environmental concerns. This has caused an increase in the usage of IoT frameworks, and the need for efficient architecture and standardized ontology is imminent. In this regard, we propose a distributed, multi-layered data platform architecture comprising the Data Monitoring Layer (DML), Data Storage Layer (DSL), Data Enhancement Layer (DEnL), and Data Exchange Layer (DEL). Our architecture achieves interoperability, facilitates data transfer between nodes, enables telemetry data retrieval, and ensures cross-platform and cross-device compatibility. It addresses the challenges of handling increased sensor data and user demands by providing high throughput and scalability support. We investigated Smart City Living Lab at IIIT Hyderabad an existing large-scale system deployed within a 66-acre campus. This system consists of 291 nodes. By studying this deployed system, we were able to gather valuable real-world data, allowing us to analyze the challenges and potential solutions related to data architecture. Our results show improvements of up to 41.23\% in throughput and a decrease in latency by 29.19\% for data insertion from the sensor nodes. The retrieval by the data client gives an increase of over 800\% in both throughput and number of requests through DENL. These metrics are compared to a centralised data platform architecture. We conclude by discussing the implications of our findings and suggesting future work.

There is unrealized potential in using automation to alleviate the visual inspection associated with non-destructive testing in manufacturing facilities. The identification of defects during the production can help avoid substantial manufacturing errors by indicating that preventative maintenance should be introduced. The use of an autoencoder for this application reduces the need to generate datasets for various defect types, instead only one training dataset would be needed. To address this, this paper proposes a Convolution Neural Network (CNN) autoencoder approach to detect surface defects on cast components during the production. The proposed method categorizes the data into damaged and undamaged components by clustering based on the loss associated with the reconstructed image. The average F1-score and accuracy from retraining the model 10 times was 89.14% and 88.52% respectively. Although previous studies have obtained higher metrics, they have focused their efforts on supervised training techniques where as this research proposes an unsupervised training method with results comparable to the previous studies.

This survey paper compiles and benchmarks the performance of integrated magnetic sensors, in terms of their noise and full-scale magnetic field. In total, we collected performance metrics of 31 sensors realized in various technologies. For each sensor, we derived a noise model to estimate the noise under the same conditions. We obtained a comprehensive benchmark with a focus on integrated devices. We also measured the noise spectrum of 11 of those sensors to confirm our noise model. Given that for many emerging applications, such as tactile, current, torque and biomagnetic sensing, the signal of interest is the field gradient to reject magnetic stray fields, we extended the benchmark to gradiometers. We developed a low-noise measurement setup and a low-field gradient (full scale: 10 μT/mm) generation setup to characterize magnetic gradiometers in this regime. The paper discusses the key sensor trade-off between precision and range, and summarizes the technology trends graphically on a trade-off curve, providing broad insight. This paper has been ACCEPTED for publication in IEEE Access.

The advent of blockchain technology has ushered in a new era of innovation, potentially reshaping the landscape of various industries, including the financial and insurance sectors. This study seeks to explore the intricate interplay between blockchain technology and two pivotal economic theories: Friedman’s monetary policy and Keynesian economic principles. Through meticulous analysis, this research aims to unravel the potential synergies and conflicts that emerge when modern technology meets established economic doctrines. At the heart of this research is a comprehensive case study on Allianz, a prominent insurance company in Germany, which has embarked on a journey to integrate blockchain technology into its operations. This case study serves as a microcosm to understand the broader implications of blockchain technology in the context of Friedman’s emphasis on monetary stability and Keynesian advocacy for government intervention in economic management. By dissecting Allianz’s initiatives, this study delves into the nuances of blockchain implementation, examining its potential to foster efficiency, transparency, and economic stimulation, facets that resonate well with the theories propounded by Friedman and Keynes. Furthermore, this research scrutinizes the regulatory challenges and opportunities that emerge in this transformative journey, offering a balanced perspective on the potential harmonization of these epochs. Through this research, a nuanced dialogue is fostered that bridges the gap between technology and economic theory, paving the way for a harmonized future where innovation complements tradition. By harmonizing these epochs, a future is envisioned where blockchain technology not only revolutionizes industries but also aligns seamlessly with enduring economic principles, fostering a symbiotic relationship that propels society towards sustainable growth and development.

The waste processing machine of this invention was created according to the characteristics of Indonesian waste. This machine can convert waste into recycled materials and become a renewable energy source

This research paper considers a dataset of students of a particular class and uses different regression algorithms to predict an overall rating for the students. These regression algorithms are then compared with each other and further analyzed according to their performances.

There are many factors that can negatively impact worker safety in manufacturing or industry. In particular, hazards such as contact of workers’ hands or other body parts with overheated components can lead to accidents and production losses. Hot surfaces cannot be seen with the naked eye. To detect hot surfaces, thermal cameras can be utilized in work areas. Placing multiple thermal cameras in large work environments, such as factories, is cost prohibitive, and carrying them is not a solution. This paper proposes an augmented reality-based assistant that visualizes hot surfaces by integrating thermal camera information into augmented reality goggles. The major advantages of the system are twofold: (1) it consists of an untethered mobile hardware platform with a thermal camera that can send information through a wireless connection, (2) the visualization is implemented for augmented reality head-mounted displays that do not require the use of workers’ hands, unlike tablet-based augmented reality. Augmenting human sight with thermal vision may also have an impact on other research areas in the future

Robotics have significantly transformed surgical practices by improving precision and minimizing invasiveness. Accurate segmentation is crucial to the surgical systems, which remains challenging due to the complex and diverse nature of surgical scenarios and equipment. This paper presents a more robust architecture, the Robotic Surgical-tool Segmentation Network (RSSNet), to enhance the accuracy metrics of surgical-tool segmentation in robot-assisted surgery. The proposed RSSNet model combines the power of Atrous Spatial Pyramid Pooling (ASPP) with the efficiency of average pooling parameter to extract fine details through multiscale manifold convolution kernels for accurate segmentation of diverse-scale instruments. The proposed method outperformed the base U-Net architecture for the enhancement to segment biomedical images with surgical toolsets with intricate details. The model was rigorously tested on three primary datasets, Kvasir-Instrument and EndoVis 2017 & 2018, demonstrating robustness and versatility. An ablation study further confirmed the effectiveness of each component in the model architecture, particularly for generalizability. This study offers an effective, and high-performance low-end solution for surgical tool segmentation, an essential step toward fully autonomous robotic surgeries. Compared with the U-Net baseline method, our proposed method improved the Dice coefficient (DSC) by 5.93% and the mIoU by 6.84% due to multi-scale kernel processing with different dilation rates. While juxtaposed with the best model yet, i.e., Surg_Net, the DSC of the proposed method increased by 1.01%, and mIoU by 1.04%.

Wi-Fi promises to revolutionize wireless connectivity through the integration of time-sensitive networking (TSN) capabilities, ensuring low latency and unmatched reliability within license-exempt spectrum bands. This study delves into the seamless amalgamation of TSN functionalities with Wi-Fi, with a particular focus on its implications for Internet of Things (IoT) applications, notably industrial automation and collaborative robots. In this paper, we examine diverse network configurations to assess their impact on latency and reliability in industrial automation scenarios. Furthermore, the efficacy of prioritization, preemption, and credit-based shaping policies is evaluated to optimize time-sensitive traffic management and enhance overall performance. The findings highlight Wi-Fi’s enhanced suitability for industrial automation, showcasing how collaborative robots can leverage reduced latency and real-time communication to achieve heightened precision, safety, and seamless human-robot interaction. By considering several network setups and traffic shaping policies, this paper provides invaluable insights for industrial practitioners and researchers, shedding light on the potential impact of Wi-Fi in industrial automation and collaborative robotics domains. This publication has been accepted to be presented in IEEE 9th World Forum on Internet of Things and therefore published in the conference proceedings. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission-https:// creativecommons.org/licenses/by/4.0/ .

Incorporation of magnetic saturation and its aftereffects have been challenging for sub-domain based analytical electric motor models. The proposed model utilizes the direct phenomenal impact of magnetic saturation on spatial variation of the core's permeability to formulate the effective tooth width. Implementation of the proposed method does not need an additional domain or magnetic vector-potential dependent term to include the saturation and hence, provides an efficient approach. The developments derived from the proposed method are seen in better estimation of motor's power factor, breakdown torque, and additional iron loss due to tooth pulsation and surface eddy currents. Prediction capability of the model is demonstrated with the experimental and finite element results of an 11 kW, four-pole induction motor for a wide range of operation.

A document by Garbe Chukulu . Click on the document to view its contents.

Passage of vehicles across the links between IoT-devices severely hampers low-power communication activities. In an urban area, deployment of an IoT-edge system, hence, gets heavily affected because of the traffic-congested roads. In order to achieve reliable delivery despite the presence of such vehicular obstructions, the IoT-devices need to make multiple repeated transmissions at the link layer or even frequently change the communication path in the network layer. However, such actions significantly drain the energy in the IoT-devices and shorten their life-span. This serious issue, although has been pointed out in several works, has not been yet addressed seriously from the point of view of the mitigation of the problem. In this work we first provide a detailed study of the dynamics of the traffic affected links. Next, we propose a lightweight sensor-assisted data-collection strategy SenseCollect, which first classifies the time periods as bad and good based on the presence and absence, respectively, of the vehicular congestion across the communication links. Furthermore, the strategy takes a delay tolerant approach which exploits the good period, i.e., when the vehicular congestion is comparatively less, for low-power communication more compared to the other (bad) periods. In addition, in order to manage emergency situations we also accommodate scopes for faster and more reliable delivery of the data which are marked as urgent by the corresponding source node. We implement the complete system with the said objectives in the real devices. Evaluation studies depict that SenseCollect achieves data collection with reliability above 99% while saving upto 60% of radio duty-cycle compared to the state-of-the-art strategies when communication links are seriously affected by vehicular traffic.

This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs. An approximate policy-iteration scheme is built on the proposed class of mappings to solve the problem of selecting online, at every time instance, the “optimal” exponent p in a p-norm loss to combat outliers in linear adaptive filtering, without training data and any knowledge on the statistical properties of the outliers. Numerical tests on synthetic data showcase the superior performance of the proposed framework over several non-RL and kernel-based RL schemes. —- © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Towards a future of fully autonomous and intelligent systems, sixth-generation (6G) wireless communication networks are expected to transform client services and apps via the Internet of Things (IoT). Together, the infrastructure and apps should facilitate and enhance intelligent administration of city services, workplaces, and everyday lives. In particular, mission-critical IoT (MC IoT), which is the connection of critical objects to the IoT network that are critical to human life and safety, is of utmost significance. This paper covers MC IoT visions, services, and applications, as well as networking infrastructure. First, visions toward 6G MC IoT are presented in the context of fundamental technological needs, trends, and use cases. Second, the emerging 6G MC IoT services and applications are described. Finally, a networking architecture for 6G MC IoT is provided. There are challenges to the implementation of robust and effective 6G MC-IoT services and applications which are mentioned and future research directions are presented.

Electroencephalography (EEG)-based motor imagery (MI) decoding has established a novel experimental paradigm in brain-computer interface (BCI) applications that offer effective treatment for stroke paralyzed patients. However, existing MI-EEG-based BCI systems introduce deployment issues because of nonstationary EEG signals, suboptimal features, and limited multi-class scalability. To tackle these issues, we propose an enhanced sparse swarm decomposition method (ESSDM) based on selfish-herd optimization and sparse spectrum to solve the issue of choice of uniform decomposition and hyper-parameters in swarm decomposition and applied to enhance MI-EEG classification. ESSDM adopts improved swarm filtering to automatically deliver optimal frequency bands in sparse spectrums with optimized hyper-parameters to extract dominant oscillatory components (OCs) that significantly enhance MI activation-related sub-bands. In addition, new fitness criteria is designed based on the Kullback–Leibler divergence distance from spectral kurtosis of obtained modes to select hyper-parameters that optimize decomposition effect, avoid excessive iterations, and provide fast convergence with optimal modes. Further, fused time-frequency graph (FTFG) features were derived from computed time-frequency representation to find cross-channel mutual spectral information. The experimental results on the 2-class BCI III-4a and 4-class BCI IV-2a datasets reveal that the proposed FTFG feature with CapsNet classifier framework (ESSDM-FTFG-CapsNet) outperformed existing methods in specific-subject and cross-subject scenarios.