This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
We review elementary properties of random matrices and discuss widely used mathematical methods for both hermitian and nonhermitian random matrix ensembles. Applications to a wide range of physics problems are summarized. This paper originally appeared as an article in the Wiley Encyclopedia of Electrical and Electronics Engineering.
Organizational adaptation is a topic that has received only limited and fragmented theoretical treatment. Any attempt to examine organizational adaptation is difficult, since the process is highly complex and changeable. The proposed theoretical framework deals with alternative ways in which organizations define their product-market domains (strategy) and construct mechanisms (structures and processes) to pursue these strategies. The framework is based on interpretation of existing literature and continuing studies in four industries (college textbook publishing, electronics, food processing, and health care).
Abstract This paper presents novel MIMO microstrip patch antennas with dimensions of 40 × 80 × 1.6 mm³ incorporating a decoupling and pattern correction structure (DPCS) designed to mitigate mutual coupling and radiation pattern distortion, operating within 3.6–3.7 GHz. Using characteristic mode analysis (CMA), two key modes affecting coupling and pattern degradation are identified, with the DPCS strategically positioned to address these issues. Unlike other decoupling techniques, the DPCS requires no additional space or structural complexity, making it suitable for 5G MIMO systems. The proposed design achieves isolation up to 90 dB and enhances the realized gain of Port 2 by 3 dB at boresight in simulations. Fabricated antennas were measured, achieving peak isolation of 80 dB in an anechoic chamber. Additionally, measurements in a noisy environment confirmed the robustness of the design under realistic conditions. Measured radiation patterns verified the DPCS’s ability to correct the radiation pattern. Key MIMO performance metrics, including ECC (2 × 10⁻⁴), DG (≈ 10), CCL (< 0.2 bits/s/Hz), MEG (≈ -7 dB), and TARC (< -12 dB), affirmed the design’s superior performance. The proposed structure can be applied to a variety of applications such as high-density urban wireless networks and IoT systems, where maintaining high isolation and reliable communication are critical requirements.
This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process. Moreover, to extract facial emotional features more accurately, the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism. This module can deeply mine the dependency between channels and spaces. It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy. The study optimizes UniRepLKNet, a universal large kernel Convolutional Neural Network (CNN), and proposes the EmoRepLKNet neural network structure. In EmoRepLKNet, the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition. Combined with the wide receptive field of large kernel CNN, facial emotions can be recognized effectively. Experimental results show that on the facial emotion recognition dataset FER2013, EmoRepLKNet achieves an accuracy of 76.20%, outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet. Additionally, on the single-label portion of the RAF-DB dataset, the proposed method achieves an accuracy of 89.67%.
Abstract With the advent of 5G/6G technologies and the continued advancement of communication systems, the shift toward low-loss microwave dielectrics has become essential. In this context, inorganic complex microwave dielectric materials offer the potential for various combinations and partial or complete substitutions, resulting in a wide range of new compounds. The fabrication method, dopant concentration, and crystal structure significantly influence the electronic properties of these materials. Importantly, this work focuses on several recently reported titanium-, silicon-, and zirconium-based materials, such as CaTiO₃, MgTiO₃, MgSiO₃, and CaZrO₃. However, comprehensive review studies on microwave dielectric materials remain limited, and the fundamental relationship between their crystal structures and dielectric properties is still not fully understood. This review highlights microstructural characteristics-particularly grain size and density-in complex hybrid metal oxides. Additionally, it provides a comprehensive analysis of microwave dielectric properties, with a focus on ABX3 perovskites for electronic applications such as 5G/6G communication systems. To the best of our knowledge, there is no extensive reviews connecting the crystal structures of diverse hybrid complex metal oxides with their corresponding microwave dielectric properties.
The increasing adoption of Artificial Intelligence (AI) in several industries has created a demand for user-centered explanations that align with how users think and understand concepts. This paper presents EXACT (EXplainable AI with Cognitive Theories), a novel framework that combines cognitive theories that explain how people think and understand with cognitive functions, focusing on perception, memory and language abilities, to improve users’ comprehension of and engagement with artificial intelligence technologies. By aligning cognitive functions with the design principles of Human-Computer Interaction (HCI), which promote user-centered intuitive systems. the framework addresses challenges related to making AI understandable to users with various levels of cognitive abilities. As a proof-of-concept, a self-diagnosis tool was created to demonstrate the framework’s effectiveness. Then, 60 participants were divided into a control group and an experimental group. Participants completed six tasks designed to evaluate their perception, memory, and language-related cognitive functions. The experimental group outperformed the control group across all tasks, demonstrating significantly improved performance. Subjective metrics also supported these findings: the experimental group reported higher levels of understanding (4.60 vs. 2.87), confidence (4.67 vs. 3.07), and clarity (4.87 vs. 2.80) compared to the control group. These findings suggest that EXACT framework significantly enhances user’s functions when using AI systems. However, further research is needed to explore its broader applicability in other contexts and utilize other cognitive functions.
Amol U. Pawar, Ignasia H. Mahardika, Young S. Son
et al.
ABSTRACT Achieving carbon neutrality is urgent due to the critical issue of climate change. To reach this goal, the development of new, breakthrough technologies is necessary and urgent. One such technology involves efficient carbon capture and its conversion into useful chemicals or fuels. However, achieving considerable amounts of efficiency in this field is a very challenging task. Even in natural photosynthesis occurring in plant leaves, the CO2 conversion efficiency into hydrocarbons cannot exceed a value of 1%. Nevertheless, recently few reports show comparable higher efficiency in CO2 to gaseous products such as carbon monoxide (CO), but it is hard to find selective liquid fuel products with a high value of solar to liquid fuel conversion efficiency. Herein, a NiFe‐assisted hybrid composite dark cathode is employed for the selective production of solar‐to‐liquid fuels, in conjunction with a BiVO4 photoanode. This process results in the generation of significant amounts of formaldehyde, ethanol, and methanol selectively. The primary objective of this study is to design and optimize a novel photoelectrochemical (PEC) system to produce solar‐to‐liquid fuels selectively. This study shows the enhancement of the solar‐to‐fuel conversion efficiency over 1.5% by employing a hybrid composite cathode composed of NiFe‐assisted reduced graphene oxide (rGO), poly(4‐vinyl)pyridine (PVP), and Nafion.
Production of electric energy or power. Powerplants. Central stations