Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey

Gupta, Mantisha; Jha, Rakesh Kumar; Jain, Sweta

doi:10.1109/access.2022.3148473

Cited by 12 publications

(11 citation statements)

References 144 publications

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“…That is, in timeslot t, the FC calculates the function uniform to all devices normalization operator such that the variables s k t ð Þ f g are of zero mean and unit variance. Then, the FC is able to recover the desired mean value by simply applying a de-normalization operation in Equation (1), that is,…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…The sixth generation (6G) of wireless communications is foreseen to accommodate a huge number of mobile devices within the context of the so-called internet-of-things (IoT) for enabling novel and demanding applications such as smart cities, interconnected autonomous vehicles, and so forth [1]. These devices require the aggregation of massive data distributed to them, to support their functions.…”

Section: Introductionmentioning

confidence: 99%

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Over-the-Air Computation with Quantized CSI and Discrete Power Control Levels

Tsinos,

Spantideas,

Giannopoulos

et al. 2023

Wireless Communications and Mobile Computing

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In this paper, an Over-the-Air Computation (AirComp) scheme for fast data aggregation is considered. Multisource data are simultaneously transmitted by single-antenna mobile devices to a single-antenna fusion center (FC) through a wireless multiple-access channel. The optimal power levels at the devices and a postprocessing scaling function at the FC are jointly derived such that mean square error of the computation is minimized. Different than the existing approaches that rely on perfect channel state information (CSI) at the FC and assume that the devices’ optimal power levels can be selected from an infinite solution set, in the present paper, it is assumed that only quantized CSI is available at the FC and that the aforementioned optimal power levels lie in a finite discrete set of solutions. To derive the optimal power levels and FC’s scaling factor, a difficult nonconvex constrained optimization problem is formulated. An efficient and robust solution to quantization errors is developed via the deep reinforcement learning framework. Numerical results verify the good performance of the proposed approach while it exhibits a significant reduction in the required feedback.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Over-the-Air Computation with Quantized CSI and Discrete Power Control Levels

Tsinos,

Spantideas,

Giannopoulos

et al. 2023

Wireless Communications and Mobile Computing

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show abstract

“…The convergence of the field of emotional intelligence with artificial intelligence (AI) and machine learning (ML) enables the development of robotic systems that can accurately perceive and react to human emotions [12]. Emotion, a complex and subjective feeling encompassing physiological, psychological, and behavioral aspects, arises from internal or external stimuli in the human being.…”

Section: Introductionmentioning

confidence: 99%

SlowR50-SA: A Self-Attention Enhanced Dynamic Facial Expression Recognition Model for Tactile Internet Applications

Neshov,

Christoff,

Sechkova

et al. 2024

Electronics

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Emotion recognition from facial expressions is a challenging task due to the subtle and nuanced nature of facial expressions. Within the framework of Tactile Internet (TI), the integration of this technology has the capacity to completely transform real-time user interactions, by delivering customized emotional input. The influence of this technology is far-reaching, as it may be used in immersive virtual reality interactions and remote tele-care applications to identify emotional states in patients. In this paper, a novel emotion recognition algorithm is presented that integrates a Self-Attention (SA) module into the SlowR50 backbone (SlowR50-SA). The experiments on the DFEW and FERV39K datasets demonstrate that the proposed model achieves good performance in terms of both Unweighted Average Recall (UAR) and Weighted Average Recall (WAR) metrics, achieving a UAR (WAR) of 57.09% (69.87%) on the DFEW dataset, and UAR (WAR) of 39.48% (49.34%) on the FERV39K dataset. Notably, SlowR50-SA operates with only eight frames of input at low temporal resolution, highlighting its efficiency. Furthermore, the algorithm has the potential to be integrated into Tactile Internet applications, where it can be used to enhance the user experience by providing real-time emotion feedback. SlowR50-SA can also be used to enhance virtual reality experiences by providing personalized haptic feedback based on the user’s emotional state. It can also be used in remote tele-care applications to detect signs of stress, anxiety, or depression in patients.

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“…These platforms are able to allow real time interaction between students and their teachers, utilizing high quality videos to resemble the experience of on-site teaching. Moreover, distant learning solutions based on Augmented Reality (AR) and Virtual Reality (VR) technologies allow students to follow curricula activities from home [7]. Furthermore, the use of 5G leads to adoption of new devices such as Head-Mounted Display (HMD), VR/AR devices and high-definition monitors.…”

Section: Introductionmentioning

confidence: 99%

Digital Tools, Technologies, and Learning Methodologies for Education 4.0 Frameworks: A STEM Oriented Survey

Boltsi,

Kalovrektis,

Xenakis

et al. 2024

IEEE Access

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The correlation between learning and teaching technologies is multidimensional and complex, thus, the digital tools that can help students turning information into knowledge define the crucial role of pedagogical practice. Open access to hardware and software tools is a focal point for advancing learning. In sciences, lab-based experiments constitute a vital part of curricula that bridge the gap between theory and practice. Access to remote lab tools is possible by pairing simulators, emulators and actual equipment, located in various geographical locations. Under this premise, Education 4.0 defines a new learning framework aligned with the fourth industrial revolution digital requirements. These constitute smart sensors, Artificial Intelligence (AI) and Robotics. In this context, University graduates need to prepare themselves with complex problem solving and interdisciplinary problems. To this end, contemporary Science, Technology, Engineering and Mathematics (STEM) teaching practices should be present along the way. Digitally competent students need to learn from a modern and adjustable curriculum tailored to cover necessary skills related to Internet of Things (IoT). Therefore, the digitization of University campuses, including smart IoT, Information and Communication Technology (ICT) and Fifth Generation (5G) automation infrastructures is necessary. In this work, we carry out an extensive STEM oriented survey which highlights and categorizes all contemporary digital tools and technologies needed to support future graduates. Moreover, we focus on contemporary pedagogical and didactic approaches to support Education 4.0 skills. We match contemporary methods with lab types, expected learning outcomes and open software and hardware tools, as imposed by the Education 4.0 framework. Our scenarios mainly focus on University education and consider smart sensors, IoT, 5G technologies, which are the basic building components for students' digital competencies to meet Education 4.0 requirements.

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Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey

Cited by 12 publications

References 144 publications

Over-the-Air Computation with Quantized CSI and Discrete Power Control Levels

Over-the-Air Computation with Quantized CSI and Discrete Power Control Levels

SlowR50-SA: A Self-Attention Enhanced Dynamic Facial Expression Recognition Model for Tactile Internet Applications

Digital Tools, Technologies, and Learning Methodologies for Education 4.0 Frameworks: A STEM Oriented Survey

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