Search Results (19)

Unmanned Aerial Vehicles (UAVs) are integral to the development of smart city infrastructures, enabling essential services such as real-time surveillance, urban traffic regulation, and cooperative environmental monitoring. UAV-to-UAV communication networks, despite their adaptability, have significant limits stemming from onboard battery constraints, inclement weather, and variable flight trajectories. This work presents a thorough examination of energy and spectral efficiency in UAV-to-UAV communication over four frequency bands: 2.4 GHz, 5.8 GHz, 28 GHz, and 60 GHz. Our MATLAB R2023a simulations include classical free-space path loss, Rayleigh/Rician fading, and real-time mobility profiles, accommodating varied heights (up to 500 m), flight velocities (reaching 15 m/s), and fluctuations in the path loss exponent. Low-frequency bands (e.g., 2.4 GHz) exhibit up to 50% reduced path loss compared to higher mmWave bands for distances exceeding several hundred meters. Energy efficiency (${\eta }_e$) is evaluated by contrasting throughput with total power consumption, indicating that 2.4 GHz initiates at around 0.15 bits/Joule (decreasing to 0.02 bits/Joule after 10 s), whereas 28 GHz and 60 GHz demonstrate markedly worse ${\eta }_e$ (as low as ${10}^{-3}$–${10}^{-4}\mathrm{bits}/\mathrm{Joule}$), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain $4\times {10}^{-12}\mathrm{bps}/\mathrm{Hz}$ in near-line-of-sight scenarios, whereas 60 GHz lines encounter significant attenuation at distances above 200–300 m without sophisticated beamforming techniques. Polynomial-fitting methods indicate that the projected ${\eta }_e$ diverges from actual performance by less than 5% after 10 s of flight, highlighting the feasibility of machine-learning-based techniques for real-time power regulation, beam steering, or multi-band switching. While mmWave UAV communication can provide significant capacity enhancements (100–500 MHz bandwidth), energy efficiency deteriorates markedly without meticulous flight planning or adaptive protocols. We thus advocate using multi-band radios, adaptive modulation, and trajectory optimisation to equilibrate power consumption, ensure connection stability, and meet high data-rate requirements in densely populated, dynamic urban settings. Full article

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial–temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas—the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors’ behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process of redesigning the WUT campus, but also to support the process of discussion and activation of the community in the development of deliberative democracy and participatory shaping of space in general. Full article

This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem, where each bandit process is associated with a target and its TEC states evolve with different transition rules for different actions, i.e., either the target is tracked or not. However, non-linear measurement functions necessitate the inclusion of dynamic state information for updating future multi-step TEC states. To compute the marginal productivity (MP) index, the unscented sampling method is employed to predict dynamic and TEC states. Consequently, an unscented sampling-based MP (US-MP) index policy is proposed for selecting targets to track at each time step, which can be applicable to large networks with a realistic number of targets. Numerical evidence presents that the bandit model with the scalar Kalman filter satisfies sufficient conditions for indexability based upon partial conservation laws and extensive simulations validate the effectiveness of the proposed US-MP policy in practical scenarios with TEC states. Full article

To treat cardiovascular diseases (i.e., a major cause of mortality after cancers), endovascular-technique-based guidewire has been employed for intra-arterial navigation. To date, most commercially available guidewires (e.g., Terumo, Abbott, Cordis, etc.) are non-steerable, which is poorly suited to the human arterial system with numerous bifurcations and angulations. To reach a target artery, surgeons frequently opt for several tools (guidewires with different size integrated into angulated catheters) that might provoke arterial complications such as perforation or dissection. Steerable guidewires would, therefore, be of high interest to reduce surgical morbidity and mortality for patients as well as to simplify procedure for surgeons, thereby saving time and health costs. Regarding these reasons, our research involves the development of a smart steerable guidewire using electroactive polymer (EAP) capable of bending when subjected to an input voltage. The actuation performance of the developed device is assessed through the curvature behavior (i.e., the displacement and the angle of the bending) of a cantilever beam structure, consisting of single- or multi-stack EAP printed on a substrate. Compared to the single-stack architecture, the multi-stack gives rise to a significant increase in curvature, even when subjected to a moderate control voltage. As suggested by the design framework, the intrinsic physical properties (dielectric, electrical, and mechanical) of the EAP layer, together with the nature and thickness of all materials (EAP and substrate), do have strong effect on the bending response of the device. The analyses propose a comprehensive guideline to optimize the actuator performance based on an adequate selection of the relevant materials and geometric parameters. An analytical model together with a finite element model (FEM) are investigated to validate the experimental tests. Finally, the design guideline leads to an innovative structure (composed of a 10-stack active layer screen-printed on a thin substrate) capable of generating a large range of bending angle (up to 190°) under an acceptable input level of 550 V, which perfectly matches the standard of medical tools used for cardiovascular surgery. Full article

In this work, a closed loop control system is developed to optically localize and track micro-robots with high precision. These micro-robots (i.e., micro-conveyors) are in motion simultaneously across a smart surface.The developed method’s primary objectives are to optimize their trajectories, avoid collisions between them, and control their position with micrometric resolution. This article presents and characterizes the tracking of a single micro-conveyor, and the method works similarly when multiple micro-robots move over the surface. Our tracking method starts with a scanning phase, where a 2D steering mirror, placed above the smart surface, reflects a laser beam toward the conveying surface seeking for the target. Localization occurs when this light beam reaches the micro-conveyor. By adding a retro-reflective element, that reflects the light in the same direction of the the incident light, onto the surface of the micro-conveyor, the light will be reflected towards a photodetector. Depending on the feedback from the photodetector, the steering mirror rotates to track the trajectory of the micro-conveyor. The tip-tilt angular values of the steering mirror allows the micro-conveyor position to be obtained via calibrated localization system. The aim of this work is to regulate the micro-conveyor, within a closed-loop control system, to reduce the positional error between the actual and desired position. The actual position value is measured in real-time application using our developed optical sensor. Results for tracking in the x-and y-axis have validated the proposed method, with an average tracking error less than 30 µm within a range 150 mm × 150 mm. Full article

Upgraded design standards coupled with the damage caused by natural disasters have led to the development of smart materials with the potential to modernize current construction practices. This investigation proposes a nonlinear finite element (FE) model for evaluating the performance of beam–column joints (RC-BCJ) reinforced with shape memory alloys (SMA) and steel rebars. The model was validated based on accredited experimental data, followed by parametric analysis in ABAQUS to optimize the use of SMA bars for enhancing the seismic resistance of RC-BCJ without compromising their energy dissipation capacity. Parameters investigated include the (a) SMA–steel reinforcement ratio, (b) lengths of SMA bars, (c) elastic modulus of SMA, (d) compressive strength of concrete, and (e) axial load applied on the column. The finite element simulation results indicated that the model was capable of predicting the optimum length of SMA bars sufficient for relocating the plastic hinge away from the face of the column along the beam. Further, simulation results proved that the use of SMA bars in conjunction with steel reinforcement could be considered as an effective tool for enhancing the seismic performance of RC-BCJ joints. Among the parameters investigated, high-strength concrete was the most effective in improving joint resistance. Full article

In recent decades, many studies have been conducted on the use of smart materials in order to dampen and control vibrations. Lead zirconate titanate piezoceramics (PZT) are very attractive for such applications due to their ability of delivering high energy strain in the structure. A pair of piezoelectric actuators can actively dampen the resonances of the structure, but the damping effectiveness strongly relies on its location. Damping effectiveness can be substantially increased if the structure is fully covered with PZT actuator pairs and the voltage distribution on each pair is optimized. In this way, each actuator pair contributes to the vibration attenuation and only the driving voltage’s sign, distributed on each actuator pair, needs to be identified for each resonance. This approach is here applied to the case of Euler–Bernoulli beams with constant cross-section and the optimal voltage distribution is investigated for several boundary conditions. The theoretical model results were corroborated with finite element simulations, which were carried out considering beams covered by ten PZT actuator pairs. The numerical results agree remarkably well with the theoretical predictions for each examined case (i.e., free-free, pinned-pinned, and fixed-fixed). Full article

This paper proposes a new sustainable earth–cement building composite with multifunctional sensing features and investigates its properties through an experimental campaign. Earth and cement are proportioned as 2/7 in volume, while carbon microfibers are added in various amounts to achieve piezoresistivity, ranging from 0 to 1% with respect to the weight of the binder (i.e., earth + cement). The proposed material couples the construction performance with self-sensing properties in order to monitor the structural performance during the servile life of the building. The use of earth in the partial replacement of cement reduces the environmental footprint of the material while keeping sufficient mechanical properties, at least for applications that do not require a large load-bearing capacity (e.g., for plasters or for low-rise constructions). This paper analyzes the electrical and sensing behavior of cubic and beam samples through electrical and electromechanical tests. The results show that the samples with a filler percentage near the percolation zone, ranged between 0.025 and 0.25%, exhibit the best performance. From the cyclical compressive tests and linear developed models, it could be deduced that the filler content of 0.05% of carbon fibers, with respect to the binder weight, represents the best-performing smart composite for further investigation at higher scales. As demonstrated, the selected mix generated clear strain-sensing electrical signals, reaching gauge factors over 100. Full article

The approaching sixth-generation (6G) communication network will modernize applications and satisfy user demands through implementing a smart and reconfigurable system with a higher data rate and wider bandwidth. The controllable THz waves are highly recommended for the instantaneous development the new technology in wireless communication systems. Recently, reconfigurable intelligent surfaces (RIS), also called codded/tunable programmable metasurfaces, have enabled a conspicuous functionality for THz devices and components for influencing electromagnetic waves (EM) such as beam steering, multi-beam-scanning applications, polarization variation, and beam focusing applications. In this article, we proposed a graphene plasmonic two-port MIMO microstrip patch antenna structure that operates at a 1.9 THz resonance frequency. An E-shape MTM unit cell is introduced to enhance the isolation of the antenna from −35 dB to −54 dB. An implementation of controllable and reconfigurable surfaces based on graphene meta-atoms (G-RIS) placed above the radiating patches with a suitable separated distance to control the radiated beam to steer in different directions (±60°). The reconfigurable process is carried out via changing the (ON/OFF) meta-atoms states to get a specific code with a certain beam direction. The gain enhancement of the antenna can be implemented through an artificial magnetic conductor (AMC) based on graphene material. The G-AMC layer is located underneath the (MIMO antenna, G-RIS layer) to improve the gain from 4.5 dBi to 10 dBi. The suggested antenna structure results are validated with different techniques CST microwave studio and ADS equivalent circuit model. The results have asymptotic values. So, the proposed design of the MIMO antenna that is sandwiched between G-RIS and G-AMC is suitable for IoT applications. Full article

Evolution of mobile broadband is ensured by adopting a unified and more capable radio interface (RI). For ubiquitous connectivity among a wide variety of wireless applications, the RI enables the adoption of an adaptive bandwidth with high spectrum flexibility. To this end, the modern-day communication system needs to cater to extremely high bandwidth, starting from below 1 GHz to 100 GHz, based on different deployments. This instigates the creation of a platform called the Internet of Everything (IoE), which is based on the concept of all-round connectivity involving humans to different objects or things via sensors. In simple words, IoE is the intelligent connection of people, processes, data, and things. To enable seamless connectivity, IoE resorts to low-cost, compact, and flexible broadband antennas, RFID-based sensors, wearable electromagnetic (EM) structures, circuits, wireless body area networks (WBAN), and the integration of these complex elements and systems. IoE needs to ensure broader information dissemination via simultaneous transmission of data to multiple users through separate beams and to that end, it takes advantage of metamaterials. The precise geometry and arrangement of metamaterials enable smart properties capable of manipulating EM waves and essentially enable the metamaterial devices to be controlled independently to achieve desirable EM characteristics, such as the direction of propagation and reflection. This review paper presents a comprehensive study on next-generation EM devices and techniques, such as antennas and circuits for wearable and sub 6 GHz 5G applications, WBAN, wireless power transfer (WPT), the direction of arrival (DoA) of propagating waves, RFID based sensors for biomedical and healthcare applications, new techniques of metamaterials as well as transformation optics (TO) and its applications in designing complex media and arbitrary geometry conformal antennas and optical devices that will enable future IoE applications. Full article

Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning. Full article

The intervention on historic buildings through building energy retrofitting has become one of the current challenges of improving energy efficiency. Nonetheless, this building typology presents certain complexities. Among them, one of the most relevant is the protection on their façades due to the historical and/or artistic values of a given façade and, therefore, the addition of external thermal insulation is restricted. However, at the same time, in several of those buildings indoor surfaces do not present that architectural value, and then internal thermal insulation becomes a promising strategy for improving their thermal performance. Nevertheless, its application must be carefully evaluated to avoid possible pathologies caused by moisture problems. This paper aims to identify constructive solutions for interior insulation of walls free from moisture problems. For this purpose, a comprehensive analysis of a series of constructive solutions based on internal insulation has been carried out through hygrothermal simulations. The results show how the application of water-repellent impregnation becomes essential to guaranteeing the integrity of the envelope. In addition, the combination of insulations with or without inner membranes, such as smart vapor retarders or vapor diffusion barriers, has been evaluated detecting the solutions that best fit the objective. Finally, taking advantage of the great potential of 2D simulation tools, the post-processing of the data has been performed to apply the wood decay model, and thus assess the behavior of a very conflictive point in this type of intervention, i.e., the wooden beam-ends. The results in this critical point have shown how the application of the proposed constructive solutions becomes essential to guarantee the integrity of the element and how the application of traditional solutions could lead to a hazard that must be avoided. Full article

With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN). Full article

This work designed and implemented a new low-cost, Internet of Things-oriented, wireless smart sensor prototype to measure mechanical strain. The research effort explores the use of smart materials as transducers, e.g., a magnetorheological elastomer as an electrical-resistance sensor, and a cantilever beam with piezoelectric sensors to harvest energy from vibrations. The study includes subsequent and validated results with a magnetorheological elastomer transducer that contained multiwall carbon nanotubes with iron particles, generated voltage tests from an energy-harvesting system that functions with an array of piezoelectric sensors embedded in a rubber-based cantilever beam, wireless communication to send data from the sensor’s central processing unit towards a website that displays and stores the handled data, and an integrated manufactured prototype. Experiments showed that electrical-resistivity variation versus measured strain, and the voltage-generation capability from vibrations have the potential to be employed in smart sensors that could be integrated into commercial solutions to measure strain in automotive and aircraft systems, and civil structures. The reported experiments included cloud-computing capabilities towards a potential Internet of Things application of the smart sensor in the context of monitoring automotive-chassis vibrations and airfoil damage for further analysis and diagnostics, and in general structural-health-monitoring applications. Full article

Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks. Full article