17 research outputs found

    Human Sensing via Passive Spectrum Monitoring

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    Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability

    Digital Ethics in Federated Learning

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    The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gained significant attention for its potential in privacy preservation and learning efficiency enhancement. In this paper, we highlight the digital ethics concerns that arise when human-centric devices serve as clients in FL. More specifically, challenges of game dynamics, fairness, incentive, and continuity arise in FL due to differences in perspectives and objectives between clients and the server. We analyze these challenges and their solutions from the perspectives of both the client and the server, and through the viewpoints of centralized and decentralized FL. Finally, we explore the opportunities in FL for human-centric IoT as directions for future development

    Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

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    Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.Comment: DDDAS 202

    Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

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    Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

    Biotechnology, Bioengineering and Applications of Bacillus Nattokinase

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    Thrombosis has threatened human health in past decades. Bacillus nattokinase is a potential low-cost thrombolytic drug without side-effects and has been introduced into the consumer market as a functional food or dietary supplement. This review firstly summarizes the biodiversity of sources and the fermentation process of nattokinase, and systematically elucidates the structure, catalytic mechanism and enzymatic properties of nattokinase. In view of the problems of low fermentation yield, insufficient activity and stability of nattokinase, this review discusses the heterologous expression of nattokinase in different microbial hosts and summarizes the protein and genetic engineering progress of nattokinase-producing strains. Finally, this review summarizes the clinical applications of nattokinase

    Characterization of a Nattokinase from the Newly Isolated Bile Salt-Resistant <i>Bacillus mojavensis</i> LY-06

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    Nattokinase is a potential new thrombolytic drug because of its strong thrombolytic effect, high safety, and low cost. However, there is no research reporting on bile salt-tolerant nattokinase-producing probiotics. In this study, the bile salt-tolerant nattokinase-producing strain Bacillus mojavensis LY-06 was isolated from local Xinjiang douchi, and the fermentation yield of nattokinase of 1434.64 U/mL was obtained by both a single factor experiment and an orthogonal experiment. A gene responsible for fibrinolysis (aprY) was cloned from the genome of strain Bacillus mojavensis LY-06, and the soluble expression of this gene in Escherichia coli (rAprY, fused with His-tag at C-terminus) was achieved; molecular docking elucidates the cause of insoluble expression of rAprY. The optimal pH and temperature for the fibrinolysis activity of nattokinase AprY fermented by Bacillus mojavensis LY-06 were determined to be pH 6.0 and 50 ┬░C, respectively. However, the optimal pH of rAprY expressed in Escherichia coli was 8, and its acid stability, thermal stability, and fibrinolytic activity were lower than those of AprY. Bioinformatics analysis found that the His-tag carried at the C-terminus of rAprY could affect its acidic stability by changing the isoelectric point and surface charge of the enzyme; in contrast to AprY, changes in the number of internal hydrogen bonds and the flexibility of the loop region in the structure of rAprY resulted in lower fibrinolytic activity and poorer thermal stability

    Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

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    Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects

    Whole-genome analyses of human adenovirus type 55 emerged in Tibet, Sichuan and Yunnan in China, in 2016

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    <div><p>Three outbreaks of acute respiratory disease occurred at military camps in 2016 at Tibet, Sichuan and Yunnan province, China. The pathogen induced these three outbreaks were all confirmed as HAdV-55 by genotype-specific PCR. The outbreak in Tibet was the first report that HAdV-55 occurred in the high altitude (HA, above sea level 3658 m). This study aims to determine the gene variation and evolution characteristics of these viral strains. Three strains of adenoviruses, LS89/Tibet/2016 (GenBank accession no. KY002683), SF04/SC/2016 (GenBank accession no. KY002684) and KM03/YN/2016 (GenBank accession no. KY002685) were obtained and confirmed by wholegenome sequencing. No multi-gene fragments recombination were found in these isolated HAdV-55 virus compared with previous reported HAdV-55 strains in China. The outbreaks in Tibet and in Sichuan continuously occurred. Virus isolated from Tibet (LS89/Tibet/2016) and Sichuan (SF04/SC/2016) had a similar mutation pattern and had a closer genetic evolutionary distance than KM03/YN/2016 strain, which indicates that the pathogens causing these two outbreaks may be of the same origin. Moreover, we found that heating was an effective way to inactive these viruses, which provide valuable information for the development of HAdV-55 vaccines. Our data provide new information for genetic evolution of HAdV-55, and contribute to the prevention and control of HAdV-55 infection in the future.</p></div

    The effect of heat and ultraviolet treatment on the infectivity of HAdV-55.

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    <p>The viruses were incubated for 30 minutes at 56┬░C or treated for ultraviolet irradiation at a wavelength of 254 nm for 30 minutes. (A) Viral DNA levels after treatment were determined by qPCR. (B) Viral titers in heat- or ultraviolet- treated virus samples were determined by infection on Hep-2 cells. Data were shown the means and standard errors of three replicate assays (* <i>P</i> < 0.05, compared with control). WT, XZ, SC and YN present HAdV-55 strains of Y16/SX/2011, LS89/Tibet/2016, SF04/SC/2016 and KM03/YN/2016 respectively.</p
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