17 research outputs found
Human Sensing via Passive Spectrum Monitoring
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
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
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
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
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
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
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
<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.
<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