35 research outputs found
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View
The next-generation wireless technologies, commonly referred to as the sixth
generation (6G), are envisioned to support extreme communications capacity and
in particular disruption in the network sensing capabilities. The terahertz
(THz) band is one potential enabler for those due to the enormous unused
frequency bands and the high spatial resolution enabled by both short
wavelengths and bandwidths. Different from earlier surveys, this paper presents
a comprehensive treatment and technology survey on THz communications and
sensing in terms of the advantages, applications, propagation characterization,
channel modeling, measurement campaigns, antennas, transceiver devices,
beamforming, networking, the integration of communications and sensing, and
experimental testbeds. Starting from the motivation and use cases, we survey
the development and historical perspective of THz communications and sensing
with the anticipated 6G requirements. We explore the radio propagation, channel
modeling, and measurements for THz band. The transceiver requirements,
architectures, technological challenges, and approaches together with means to
compensate for the high propagation losses by appropriate antenna and
beamforming solutions. We survey also several system technologies required by
or beneficial for THz systems. The synergistic design of sensing and
communications is explored with depth. Practical trials, demonstrations, and
experiments are also summarized. The paper gives a holistic view of the current
state of the art and highlights the issues and challenges that are open for
further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications
Surveys & Tutorial
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications
International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201
Artificial Intelligence and Ambient Intelligence
This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science
Mobile node-aided localization and tracking in terrestrial and underwater networks
In large-scale wireless sensor networks (WSNs), the position information of individual
sensors is very important for many applications. Generally, there are a small number
of position-aware nodes, referred to as the anchors. Every other node can estimate its
distances to the surrounding anchors, and then employ trilateration or triangulation for
self-localization. Such a system is easy to implement, and thus popular for both terrestrial
and underwater applications, but it suffers from some major drawbacks. First, the density
of the anchors is generally very low due to economical considerations, leading to poor
localization accuracy. Secondly, the energy and bandwidth consumptions of such systems
are quite significant. Last but not the least, the scalability of a network based on fixed
anchors is not good. Therefore, whenever the network expands, more anchors should be
deployed to guarantee the required performance. Apart from these general challenges,
both terrestrial and underwater networks have their own specific ones. For example, realtime
channel parameters are generally required for localization in terrestrial WSNs. For
underwater networks, the clock skew between the target sensor and the anchors must
be considered. That is to say, time synchronization should be performed together with
localization, which makes the problem complicated.
An alternative approach is to employ mobile anchors to replace the fixed ones. For
terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very
good choices, while autonomous underwater vehicles (AUVs) can be used for underwater
applications. Mobile anchors can move along a predefined trajectory and broadcast beacon
signals. By listening to the messages, the other nodes in the network can localize themselves
passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much
improved with the increased number of virtual anchors, which can be boosted at negligible
cost; thirdly, the coverage can be easily extended, which makes the solution and the network
highly scalable.
Motivated by this idea, this thesis investigates the mobile node-aided localization and
tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system
design, modeling, and performance analyses will be presented for various applications,
including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based
underwater localization and time synchronization; (3) the Doppler-based underwater localization;
(4) the underwater target detection and tracking based on the convolutional
neural network and the fractional Fourier transform. In these applications, different challenges
will present, and we will see how these challenges can be addressed by replacing
the fixed anchors with mobile ones. Detailed mathematical models will be presented, and
extensive simulation and experimental results will be provided to verify the theoretical
results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless
communications. A pilot decontamination method will be presented for the massive
multiple-input-multiple-output communications, and the data-aided channel tracking will
be discussed for millimeter wave communications. We will see that the localization problem
is highly coupled with the channel estimation in wireless communications
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Privacy-preserving human behaviour monitoring through thermal vision
Despite the abundance of human-centred research to support domestic human behaviour monitoring in various vital applications, there are still notable limitations to deploying such systems on a broader scale. The main challenge is the trade-off between privacy, performance, and cost of assistive technologies to support older adults to live independently in their own homes. For example, the traditional vision-based sensing approach provides excellent performance while violating human privacy in domestic environments. In contrast, the ambient sensing approach, e.g., employing Passive Infra-Red (PIR) sensors, maintains human privacy but suffers significant performance hindrances in realistic scenarios such as multi-occupancy environments.
This research proposes to utilise the Thermal Sensor Array (TSA) to adjust the trade-off between privacy and performance in domestic environment applications. The rationale behind proposing this sensor for human behaviour monitoring applications is its claimed advantages to perform well while maintaining human privacy, low-cost, and noncontact capabilities. Nevertheless, there has not been sufficient related work to empirically validate the hypothesis of using this low-resolution imager in domestic monitoring. Furthermore, most published works that use the TSA have not yet reached the deployment stage due to the TSA sensing constraints. In particular, TSA is sensitive to environmental thermal noise, and its Field of View (FoV) is not wide enough to cover a large inspection area. Intelligent algorithms should be employed in order to avoid these limitations.
The focus of this thesis is to investigate the human physiological and behavioural thermal patterns for privacy-preserving human behaviour monitoring to support the independent living of older adults in a multi-occupancy environment by using TSA. This will be achieved through signal processing and machine learning techniques. To achieve this aim, the research methodology is drawn into two main directions. First, human physiological processing of the human thermal signal. Second, human behavioural processing of the human motion signal. This drawn methodology resulted in four main novel contributions.
The first novel contribution of this research is to propose an adaptive segmentation of the human physiological presence and count the number of people from different sensor placements, indoor environments, and human-to-sensor distance. The second contribution is to extract localisation knowledge of the human physiological appearance in terms of human-to-sensor distance and human-to-human distance. Extracting human localisation knowledge is also applicable in other applications such as caregivers and care time monitoring. The third contribution is to fuse multiple TSAs to cover a wide inspection area, e.g., private or care homes. Hence, objects that appear in the low-resolution thermal images acquired from TSA have low intra-class variations and high inter-class similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This research proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout with a privacy improvement of utilising TSA in potential monitoring applications running in the cloud. Inspired by the results from this stage of the research, the fourth contribution of the research presented in this thesis is a human-in-the-loop fall detection approach in the Activities of Daily Living (ADLs) that reduces the false-positive alerts while keeping the false-negative fall predictions as low as possible. The novel solutions and the results presented in this thesis demonstrate a significant contribution toward enabling privacy-preserving human behaviour monitoring
Cognitive Radio Systems
Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems
Autonomisten metsäkoneiden koneaistijärjestelmät
A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator.
In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors.
In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä.
Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona.
Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen