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1 |
iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
2 |
AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
3 |
Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیشبینی تعامل رسانههای اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022 In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta-
grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’
Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to
social media engagement. Results demonstrate that food images that are more confidently evaluated by Google
Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-
up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier
to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and
food industry trends, the more typical a food appears, the more social media engagement it receives. Using
Google Vision AI to identify what product offerings receive engagement presents an accessible method for
marketers to understand their industry and inform their social media marketing strategies. keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI |
مقاله انگلیسی |
4 |
Power to the people: Applying citizen science and computer vision to home mapping for rural energy access
قدرت به مردم: به کارگیری علم شهروندی و بینش رایانه در نقشهبرداری خانه برای دسترسی به انرژی روستایی-2022 To implement effective rural electricity access systems, it is fundamental to identify where potential consumers
live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map
remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was
completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thou-
sands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of
7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The
raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to
69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass
for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67%
and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for
accelerated rural home-level mapping to enable energy system design. keywords: دانش شهروندی | بینایی کامپیوتر | دسترسی به برق | نقشه برداری روستایی | تصویربرداری ماهواره ای | سنجش از دور | Citizen science | Computer vision | Electricity access | Rural mapping | Satellite imagery | Remote sensing |
مقاله انگلیسی |
5 |
Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems
تولید مجموعههای قابل دسترس در مدلسازی دینامیکی سیستمهای شبکه کوانتومی-2022 In this article, we consider the dynamical modeling of a class of quantum network systems consisting of
qubits, where information extraction is allowed by performing measurement on several selected qubits of the system.
For a variety of applications, a state space model is a useful
approach to modeling the system dynamics. To construct
a state space model for a quantum network system, the
major task is to find an accessible set containing all of
the operators coupled to the measurement operators. This
article focuses on the generation of a proper accessible set
for a given system and measurement scheme. We provide
analytic results on simplifying the process of generating
accessible sets for systems with a time-independent Hamiltonian. Since the order of elements in the accessible set
determines the form of state space matrices, guidance is
provided to effectively arrange the ordering of elements in
the state vector. Defining a system state according to the
accessible set, one can develop a state space model with
a special pattern inherited from the system structure. As a
demonstration, we specifically consider a typical 1-D-chain
system with several common measurements and employ
the proposed method to determine its accessible set.
Index Terms: Accessible set | dynamical modeling | quantum network system | quantum system. |
مقاله انگلیسی |
6 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |
7 |
Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning
مدیریت منابع برای IoV به کمک هوش لبه (EI) با استفاده از یادگیری تقویتی الهام گرفته از پردازش کوانتومی-2022 Recent developments in the Internet of Vehicles
(IoV) enable interconnected vehicles to support ubiquitous
services. Various emerging service applications are promising to
increase the Quality of Experience (QoE) of users. On-board
computation tasks generated by these applications have heavily
overloaded the resource-constrained vehicles, forcing it to offload
on-board tasks to other edge intelligence (EI)-assisted servers.
However, excessive task offloading can lead to severe competition
for communication and computation resources among vehicles,
thereby increasing the processing latency, energy consumption,
and system cost. To address these problems, we investigate
the transmission-awareness and computing-sense uplink resource
management problem and formulate it as a time-varying Markov
decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL)
is proposed to develop an intelligence-oriented edge offloading
strategy. Specifically, the vehicle can flexibly choose the network
access mode and offloading strategy through two different radio
interfaces to offload tasks to multiaccess edge computing (MEC)
servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive
balance between these two aspects. Simulation results show that
the proposed algorithm can significantly reduce the transmission
latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL) |
مقاله انگلیسی |
8 |
The Quantum Multiple-Access Channel With Cribbing Encoders
کانال دسترسی چندگانه کوانتومی با رمزگذارهای Cribbing-2022 Communication over a quantum multiple-access
channel (MAC) with cribbing encoders is considered, whereby
Transmitter 2 performs a measurement on a system that is
entangled with Transmitter 1. Based on the no-cloning theorem,
perfect cribbing is impossible. This leads to the introduction of
a MAC model with noisy cribbing. In the causal and non-causal
cribbing scenarios, Transmitter 2 performs the measurement
before the input of Transmitter 1 is sent through the channel.
Hence, Transmitter 2’s cribbing may inflict a “state collapse”
for Transmitter 1. Achievable regions are derived for each
setting. Furthermore, a regularized capacity characterization is
established for robust cribbing, i.e. when the cribbing system
contains all the information of the channel input. Building on the
analogy between the noisy cribbing model and the relay channel,
a partial decode-forward region is derived for a quantum MAC
with non-robust cribbing. For the classical-quantum MAC with
cribbing encoders, the capacity region is determined with perfect
cribbing of the classical input, and a cutset region is derived for
noisy cribbing. In the special case of a classical-quantum MAC
with a deterministic cribbing channel, the inner and outer bounds
coincide.
Index Terms—Quantum communication | Shannon theory | multiple-access channel | cribbing | relay channel. |
مقاله انگلیسی |
9 |
LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring
پیاده سازی سیستم اینترنت اشیاء مبتنی بر LoRaWAN برای نظارت بر کیفیت هوای خارج از منزل در محدوده بلند-2022 This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality in-
formation and update it on the cloud. The developed long-range wide area network (LoRaWAN)-
based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-
IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness.
The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and hu-
midity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network
(TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a
rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable
operation. Our system simultaneously gathers the considered air quality information by using the
smart sensing unit. Then, the system transmits the information through the gateway to the TTN
platform, which is integrated with the ThingSpeak IoT server. This action updates the collected
data and displays these data on a developed Web-based dashboard and a Graphical User Interface
(GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily
accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-
AQMS are validated by comparing them with experimental results based on the high-
technology Aeroqual air quality monitoring devices. Our system can reliably monitor various
air quality indicators and efficiently transmit the information in real time over the Internet. keywords: پایش کیفیت هوا | Air quality monitoring | Iot lora lorawan | TTN ThingSpeak Virtuino |
مقاله انگلیسی |
10 |
A holistic approach to health and safety monitoring: Framework and technology perspective
رویکردی جامع برای نظارت بر سلامت و ایمنی: چارچوب و دیدگاه فناوری-2022 Existing H&S monitoring methods are manual, cumbersome, time consuming and issues with
safety compliance and use of PPE remain a concern. With the existing manual H&S processes,
there are significant delays and, in some cases, even failure to report incidences, resulting in no or
slow improvements to safety. This paper proposes a prototype PPE access monitoring system
which combines smart PPE and an indoor/outdoor personnel location monitoring system. The
paper also proposes a generic framework to be used for smart gateway services within a
manufacturing site to augment and enable smart PPE, separating areas of high and low risk. The
prototype automated PPE detection gate presents a practical use of the framework and demon-
strates a suitable method for assessing workforce/visitor PPE compliance. Its secondary function
is to act as a location waypoint system to support other location tracking methods, identified in
the literature and throughout the testing protocol. The system could be further adapted to support
augmented personnel within the Operator 4.0 paradigm to improve site safety, monitoring and
control. keywords: اینترنت اشیا | تجهیزات حفاظت فردی | اپراتور 4.0 | انطباق با PPE | چارچوب | RFID | Internet of things | Personal protective equipment | Operator 4.0 | PPE compliance | Framework | RFID |
مقاله انگلیسی |