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Abstract This study presents a novel method leveraging surface wave-assisted photonic spin Hall effect (PSHE) to construct physical unclonable functions (PUFs). PUFs exploit inherent physical variations to generate unique Challenge–Response pairs, which are critical for hardware security and arise from manufacturing discrepancies, device characteristics, or timing deviations. We explore PSHE generation-based PUF design, expanding existing design possibilities. With recent applications in precise sensing and computing, PSHE offers promising performance metrics for our proposed PUFs, including an inter-Hamming distance of 47.50% , an average proportion of unique responses of 62.5% , and a Pearson correlation coefficient of − 0.198. The PUF token demonstrates robustness to simulated noise. Additionally, we evaluate security using a machine learning-based attack model, employing a multi-layer perceptron (MLP) regression model with a randomized search method. The average accuracy of successful attack prediction is 9.70% for the selected dataset. Our novel PUF token exhibits high non-linearity due to the PSHE effect, resilience to MLP-based attacks, and sensitivity to process variation.

Abstract This work presents a dielectric material-based optical Tamm state (OTS) excitation technique with modified dispersion characteristics for photonic spin hall effect (PSHE) enhancement. The dispersion analysis of the structure is carried out to validate OTS’s localization and corresponding PSHE generation for a given polarization at 632.8 nm incident wavelength. The exceptional points are optimized by considering thickness-dependent angular dispersion analysis. PSHE-based transverse displacement (PSHE-TD) is dependent on the defect layer thickness. The optimized structure provides 10.73 $$\times \lambda$$ × λ (or 6.78 $$\upmu$$ μ m) PSHE-TD at an incidence angle of 41.86 $${}^{\circ }$$ ∘ . The PSHE-TD of the optimized structure is sufficiently high due to the much narrower resonance than the plasmonic-based structures. Further, the structure’s potential to function as a PSHE-TD-based optical sensor is assessed. The optimized structure shows an analytical average sensitivity of about 43,789 $$\upmu$$ μ m/RIU showing its capability to detect the analytes with refractive index variations in the $$10^{-4}$$ 10 - 4 range. The structure demonstrates a three-time sensitivity improvement compared to similar resonance designs. Considering only dielectric materials in the proposed structure and considerably enhanced PSHE-TD, the development of highly efficient PSHE-TD-assisted commercial structures is anticipated.

During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs.

Triboelectric Nanogenerators (TENGs), renowned for converting mechanical energy into electrical energy, have evolved significantly since their inception. This paper focuses on enhancing TENG performance, specifically in the contact-separation (CSM) mode paired-electrode configuration. A nanograting of varying periods is introduced on the electrodes, and the corresponding impact on device performance is evaluated. The performance is compared with the conventional planer structure. The analytical results exhibit a notable increase in open-circuit voltage (Voc) by up to 148% and charge during short-circuit condition (Qsc) improvement of up to 200%, compared to a planar structure of similar dimensions at 200 nm grating period. Interestingly, a direct correlation between grating period and voltage generation is observed, emphasizing the critical role of material and design choices. Furthermore, the impact of metallic gratings on TENG performance is found to be minimal. These results emphasize TENGs' potential in minute displacement sensors and highlight the importance of meticulous design considerations to optimize efficiency.

Abstract In this manuscript, a heterostructure-based topological nanophotonic structure is proposed for improved sensing performance. The topological effect is realized by connecting two dissimilar one-dimensional photonic crystal structures having overlapped photonic bandgaps. The structural parameters are optimized to regulate and alter the dispersion characteristics, which results in the opposite Zak phases. This demonstrates a robust topologsical interface state excitation at a 1737 nm operating wavelength. Further, a topological cavity structure having resonance mode at 1659 nm is formed by replacing the interface layers with a defect layer. The mode excitation is confirmed by analyzing the electric field confinement at the interface. The sensing capability of the structure is analytically evaluated by infiltrating different analytes within the cavity. The analytical results demonstrate the device’s average sensitivity of around 774 nm/Refractive index unit (RIU) along with an average high Q-factor and figure of merit of around 5.2 × 104 and 2.6234 × 104 RIU−1, respectively. Because of the higher interface mode field confinement, the proposed structure exhibits a 92% higher sensitivity, 98% improved Quality factor, 206% improvement in figure of merit, and 86% higher interface field confinement than conventional Fabry–Perot resonator structures. Thus, the proposed topological cavity structure shows its broad sensing ability (Refractive Index: 1.3–1.6) along with a low-cost, simple fabrication and characterization process, promoting the development of highly sensitive planner nanophotonic devices.

Abstract This manuscript presents a dielectric resonator structure with altered dispersion characteristics to enhance the photonic spin Hall effect (PSHE). The structural parameters are optimized to enhance the PSHE at 632.8 nm operating wavelength. The thickness-dependent angular dispersion analysis is carried out to optimize the structure and obtain the exceptional points. The PSHE-induced spin splitting shows a high sensitivity to the optical thickness of the defect layer. This gives a maximum PSHE-based transverse displacement (PSHE-TD) of around 56.66 times the operating wavelength at an incidence angle of 61.68°. Moreover, the structure’s capability as a PSHE-based refractive index sensor is also evaluated. The analytical results demonstrate an average sensitivity of around 33,720 μm/RIU. The structure exhibits around five times higher PSHE-TD and approximately 150% improvement in sensitivity than the recently reported values in lossy mode resonance structures. Due to the purely dielectric material-assisted PhC resonator configurations and significantly higher PSHE-TD, the development of low-cost PSHE-based devices for commercial applications is envisaged.

This paper presents two tandemly stacked forward and inverse deep neural networks to model and optimize cylindrical shaped transmissive meta-atoms for UV–Vis regime. Conventional modeling of these subwavelength meta-atoms calls for repetitive solution of Maxwell equations over each instance of a mesh grid using some high-end commercial EM simulator. In contrast, Deep Learning (DL)-based approaches can significantly expedite this process owing to their abilities to intelligently learn and approximate Maxwell's equations without explicitly solving them, hence anticipating the electromagnetic (EM) response of a given meta-atom within a split-second. Despite of the advantages of DL-based solutions for metasurface design and optimization, dataset collection for training DL models still requires time-tedious conventional approaches. Here, we aim to enhance the learning performance while reducing the dataset requirements and the complexity of the proposed forward and inverse networks by enriching them with more underlying physical facts and substantial knowledge of a particular problem. Therefore, in addition to the list of geometrical parameters of the cylindrical meta-atoms, the proposed forward neural network is also fed with some additional material and physics related parameters as a part of its input, including EM spectral information, material type (dielectric or plasmon), maximum height (based on the fabrication constraints), and period of the cylindrical rod. This results in enhancing the model's performance while utilizing minimum training samples, and predicting the EM transmission amplitude and phase with negligible error tolerance, i.e. MSE: 2.1 × 10−3, for a given dielectric or plasmonic meta-atom at various operating wavelengths (within the targeted regimes). Optimization of such meta-structures is another challenging task that requires hit and trial intuitive guesses, lengthy parametric sweeps and time taking conventional intelligent algorithms. Therefore, an inverse design neural network is stacked behind the trained forward modeling network and their tandem assembly is trained together to predict the best set of dimensions and the most suitable material to achieve the desired response. The inverse design network is also fed with the same additional information including all the aforementioned parameters, as the forward mapping network. In order to assess the impact of incorporating this supplementary problem specific information, a comparative study regarding the number of hidden layers and the amount of training dataset size is carried out for the forward and tandem inverse neural networks. This comparative analysis shows that the loss is greatly decreased, and the networks learns efficiently using the proposed scheme with more knowledge about the problem under consideration. As a result, we show that even with a smaller training dataset and lesser number of hidden layers, the model can realize an appropriate MSE.

In this paper, Junctionless Twin Gate Trench Channel (JL-TGTC) MOSFET with individual gate control is realized. The device gives full functionality of 2-input digital ‘AND’ and ‘NAND’ logics. The simulation depicts the results in the form of various parameters such as cutoff current, transfer characteristics, and potential profiles. All the simulations regarding device structure and functionality are done on TCAD. This new type of MOS device has improved applicability in low-voltage digital electronics such as sequential circuits etc.

Achieving broadband and multifunctional response through a single optical device is a highly sought-after capability for various applications like microscopy and optical imaging, which provide detailed morphological information of a targeted object. Traditional optical systems require a chain of elements to achieve such functionality, resulting in bulky systems hindering integration with modern photonics devices. Metasurfaces are exquisitely designed and exhibit unprecedented light-manipulation capability at the nanoscale, thus creating easy-to-integrate miniaturized photonic devices. Recently, broadband multifunctional metasurfaces with polarization’s degree of freedom have attracted considerable interest due to their exceptional ability of wavefront shaping, which may find their applications in imaging and data communication. However, realizing single-cell driven, highly efficient multifunctional metasurfaces operating over broadband wavelengths remains challenging. This study presents a unique helicity-dependent broadband multifunctional metasurface platform to manipulate visible light for futuristic imaging applications. The proposed platform can integrate multiple optical phenomena into a single-layered metadevice to generate several uncorrelated, spin-dependent responses across the targeted visible spectrum (470–650 nm). For proof of concept, we demonstrated several all-dielectric transmissive metadevices carrying various spin-multiplexed phase profiles, efficiently generating diffraction-limited focusing and structured light beams with different characteristics. Each designed metasurface was studied under selected visible wavelengths, and the diffracted light exhibited excellent and stable broadband performance. With the advantages of broadband multifunctional platform and miniaturized metadevices, the presented strategy may open new vistas for various applications, including biomedical imaging, optical interconnects, and microscopy.

The concept of achieving perfect absorption of electromagnetic (EM) waves has engendered substantial interest in diverse applications, encompassing photoelectric conversions, infrared imaging, energy harvesting, and photovoltaics. Conventionally, absorber materials of choice have revolved around the utilization of refractory metals. However, this article proposes a pioneering proposition that uses quasi-two-dimensional (quasi-2D) material i.e., MXene, and uses ceramics material aluminum nitride (AlN) to forge a polarization-insensitive broadband absorber. This ultra-broadband meta-absorber showcases an expanse of absorbance across a comprehensive spectral range, spanning from the ultraviolet (UV) to Near-infrared (NIR) regions, specifically extending from 200 nm to 2000 nm. To previse the angular stability of the meta-absorber, the absorptivity was examined under oblique incidence in both transverse electric (TE) and transverse magnetic (TM) polarizations exhibiting the robustness of broadband absorber. MXene shows exceptional mechanical flexibility, facilitating seamless integration into flexible electronics. Their large specific surface area holds significant promise for high-performance energy storage in applications including energy harvesting, photovoltaics, solar cells, and sensors. These unique characteristics position them at the forefront of material science, driving advancements in next-generation technologies.

Interdigitated capacitive (IDC) sensors have been extensively researched for human-machine interfaces (HMIs) due to consistent response and easy manufacturing. However, the manual fabrication process and single mode of operation, like buttons, limit their use for future developments of HMIs. We reported a maskless laser lithography-based fabrication process for developing chromium metal electrodes on glass. Chromium metal fabricates sensors on a glass substrate because of its durability. IDC touch sensors are sensitive to the pressure of human finger touch to perform multimode of operation intelligently by distinguishing between no touch, low-pressure touch (LPT) and high-pressure touch (HPT). Sensors exhibit ~ 25 pF/N sensitivity for the force of finger touch, 0.4-sec response, recovery time and durability > 20,000 tests. Touch sensors are arrayed to create pressure-sensitive multimode HMIs for wireless and intelligent mouse cursor movement (MCM) control, numeric keyboard, and smart door locking (SDL) system. Six capacitive pressure sensitive (CPS) sensors-based HMIs utilized to control the low and high speed of the mouse cursor in all four directions and perform the left and right click operations. Numeric keyboard of eight CPS sensors is developed to respond wirelessly to numbers and special characters to a laptop. SDL system demonstrated using the CPS HMI consists of numeric sensors from 0-9, reset and done sensors to utilize it for security applications. HMI sensors responded numerically (0-9) for LPT and special characters for HPT. An algorithm is developed to operate the HMI for the SDL system and use special characters along with numeric, which immensely increases the strength of passwords for security applications. With daily life using substrate material glass, multimode of operation, easily arrayed, and customization, the presented HMIs have the potential to contribute to future developments in intelligent user interfaces, portable computer peripherals, security applications, ATMs, and smart home systems.

Broadband Terahertz (THz) metamaterial absorbers with tunable absorption characteristics have been attracting a lot of attention due to their high demand and utilization in advanced optical systems. Therefore, we propose a single-layer graphene-supported metamaterial absorber with a combination of square, circular and cross-shaped meta-resonators, exhibiting an outstanding absorption of more than 90% from 2.3 THz to 6.4 THz. The overall size of the unit cell is 15 × 15 μm2, which holds graphene-based meta-resonators etched over a lossy dielectric substrate, backed by a 0.2 μm thick gold as a perfect reflector. This design architecture achieves the ultra-broadband absorption features by continuous design evaluation and optimization. The graphene-based metamaterial absorber (GMMA) reveals a polarization-insensitive response, and it also provides above 70% absorption for obliquely incident angles over the large operating bandwidth (2.3–6.4 THz). The polarization conversion ratio (PCR) also approached zero over the wideband of 4.1 THz and its maximum value of 16% was recorded at 4 THz. Furthermore, the amplitude spectra of the proposed GMMA is modulated from 19% to above 90% by stimulating the chemical potential of graphene from 0 eV to 0.7 eV. The simplified design configuration, broadband absorption features, and tunable capability of the GMMA provide the pathway to develop high-speed optical switches, THz detectors, and other opto-electronic devices.

Smart Energy (SE) has emerged as a critical technology in tackling global challenges like climate change while addressing the rising energy demands driven by today’s data-intensive industrial revolution. SE integrates information and communication technologies into energy systems, optimizing them to meet these challenges effectively. At the core of SE operations are smart meters, playing a fundamental role in ensuring efficient functionality. These devices collect data, which is then leveraged to derive Business Intelligence (BI) for operations across the entire spectrum, from the sensing infrastructure to the cloud, primarily utilizing the Internet of Things (IoT) technology framework. With the increasing complexity of operations and the growing demand for optimization and enhanced functionality, the SE technology stack is evolving to integrate across all layers and domains. This integration has led to the stratification of computational load across IoT layers, intensifying the dependence on smart meter data for BI. Consequently, smart meters themselves have evolved to become more functional and complex. This paper’s novelty lies in its comprehensive exploration of the integration of BI with smart meter data. It delves into various aspects, including the different layers of intelligent operations within SE systems, the current state of the art, and diverse implementations of smart meters and their applications across operational locations, ranging from consumers to fog computing. The paper concludes by identifying research gaps and future directions, offering insights into the evolving requirements for the next generation of SE systems and the necessary adaptations in smart metering infrastructure to support these roles. This work contributes to a better understanding of the evolving landscape of data and computation in the context of SE, facilitating more efficient and effective energy management solutions.

In recent years, the way that machine learning is used has undergone a paradigm shift driven by distributed and collaborative learning. Several approaches have emerged to enable pervasive computing and distributed learning in ubiquitous Internet of Things (IoT) systems. Numerous decentralized strategies have been proposed to deal with the limitations of centralized learning, including privacy and latency due to sharing local data, while utilizing distributed computations as a promising substitute to centralized learning. However, such distributed learning schemes come with new security and privacy concerns that should be addressed. Thus, in this paper, we first provide an overview for the emerging paradigms developed for distributed learning. Then, we performed a comprehensive survey for the privacy and security challenges associated with distributed learning along with the presented solutions to overcome them. Furthermore, we highlight key challenges and open future research directions toward implementing more robust distributed systems.

This article reports a wide-band power divider with ultra-wide harmonic suppression. The filtering power divider consists of a Wilkinson power divider and a filter merged into a single structure. For filtering purposes, a novel suppressor cell is designed using resonators of various shapes. The power divider exhibits an operational frequency of 1.64 GHz, encompassing a fractional bandwidth (FBW) of 57 percent, ranging from 1.151 GHz to 2.08 GHz. Notably, it effectively suppresses a total of 24 harmonics, attaining a rejection level exceeding −15 dB. The measured in-band isolation, input return loss, insertion loss, and output return loss are determined at 1.64 GHz to be −23 dB, −36 dB, −3.03 dB, and −18 dB, respectively. These results showcase the superior performance of the proposed design compared to existing state-of-the-art solutions.

The continuous scaling of the on-chip devices and interconnects increases the complexity of the design space and becomes a crucial factor in the fabrication of modern integrated circuits. The ever decreasing of interconnect pitch along with process enhancement into the nanometer regime had shifted the paradigm from a device-dominated to an interconnect-dominated methodology. In the design methodology, Model Order Reduction (MOR) reduces the size of large-scale simulation of on-chip interconnect to speed up the performance of design tools and chip validation. In approximating the original system, the passivity preserving MOR technique of using spectral zeros as positive real interpolation points preserves the stability and passivity of the system. In this work, statistical distribution techniques are proposed for the selection of spectral zeros. The proposed method is based on using the gaussian, uniform, binomial, and weibull distributions to select spectral zeros to better match moments with the least absolute error between the original and reduced-order systems. The results show that the reduced-order model developed using the Gaussian distributed Spectral zeros Projection (GSP) method offers higher accuracy and numerical stability compared to other distributions.

Due to their abundance, affordability, and clean energy production, Renewable Energy Sources (RESs) have emerged as crucial sources of electricity production. As a result, considerable effort has been made to integrate renewable energy sources into the grid to help it meet energy demands. One of the challenges is the Maximum Power Extraction (MPE), where the maximum power needs to be extracted from each RES. The cost of implementation rises linearly with the number of RESs if the MPE arrangements are being made for each RES individually. Additionally, the MPE through multiple RES systems gets more challenging, especially when several PhotoVoltaic (PV) strings are connected and each PV string receive non-uniform irradiance. This paper proposes an integrated MPE control system for a multi-RES grid-connected system. The proposed solution uses a single microcontroller to maximize power from all sources to overcome the linearly growing cost problem. Moreover, we propose an improved Multi-Dimensional Cuckoo (MDC) algorithm for MPE to tackle the non-uniform irradiation problem with multi-string PV, in contrast to prior works with grid-connected multiple sources. The proposed technique is first put up against the individual MPE control system, and then it is put up against the Jaya algorithm that is documented in the literature for a typical one-dimensional MPE.

A better understanding of ElectroEncephaloGraphy (EEG) and ElectroCorticoGram (ECoG) signals would get us closer to comprehending brain functionality, creating new avenues for treating brain abnormalities and developing novel Brain-Computer Interface (BCI)-related applications. Deep Neural Networks (DNN)s have lately been employed with remarkable success to decode EEG/ECoG signals for BCI. However, the optimal architectural/training parameter values in these DNN architectures have yet to receive much attention. In addition, new data-driven optimization methodologies that leverage significant advancements in Machine Learning, such as the Transformer model, have recently been proposed. Because an exhaustive search on all possible architectural/training parameter values of the state-of-the-art DNN model (our baseline model) decoding the motor imagery EEG and finger tension ECoG signals comprising the BCI IV 2a and 4 datasets, respectively, would require prohibitively much time, this paper proposes an offline model-based optimization technique based on the Transformer model for the discovery of the optimal architectural/training parameter values for that model. Our findings indicate that we could pick better values for the baseline model’s architectural/training parameters, enhancing the baseline model’s performance by up to 14.7% in the BCI IV 2a dataset and by up to 61.0% in the BCI IV 4 dataset.

The globalization of the Integrated Circuits supply chain has increased threats from untrusted entities involved in the process. Several mechanisms, such as logic locking, watermarking and split manufacturing, are widely used to ensure hardware security. This study describes a novel method for creating hardware watermarks inspired by finite-state machines. It makes use of the unique physical property of magnetic tunnel junctions that are based on spin-orbit torque. The design strategy is described in detail, including the use of an EDA tool to analyze and take advantage of the unique switching properties of MTJ, their non-volatility, and their reliance on an external magnetic field to direct information through a predetermined order of states in a manner akin to an FSM. Furthermore, the performance prospects are analyzed using Monte Carlo simulations. For the 5% and 10% of process variation in the key MTJ parameters, the accuracy of 100% and 99.80%, respectively, are achieved. In control signal voltage variation, a tolerance of 9% (0.91V) is observed. The required state transition is not altered, demonstrating a tolerable sensitivity to temperature variation from 250K to 350K. The security aspects and methodology for the approach are explained to ensure a more robust and practical application, and finally, a comparison is made with other FSM-based watermarks.

Abstract In this manuscript, a novel photonic crystal resonator (PhCR) structure having an exponentially graded refractive index profile is proposed to regulate and alter the dispersion characteristics for the first time. The structure comprises silicon material, where porosity is deliberately introduced to modulate the refractive index profile locally. The structural parameters are optimized to have a resonant wavelength of 1550 nm. Further, the impact of various parameters like incidence angle, defect layer thickness, and analyte infiltration on device performance is evaluated. Finally, the sensing capability of the proposed structure is compared with the conventional step index-based devices. The proposed structure exhibits an average sensitivity of 54.16 nm/RIU and 500.12 nm/RIU for step index and exponentially graded index structures. This exhibits the generation of a lower energy resonating mode having 825% higher sensitivity than conventional resonator structures. Moreover, the graded index structures show a 45% higher field confinement than the conventional PhCR structure.

Graphene-based metamaterials are gaining popularity for developing various reconfigurable and electrically tunable optical devices – especially in terahertz (THz) and infrared (IR) bands. Therefore, in this paper, we aim to investigate the broadband metamaterial-based absorber that efficiently absorbs the THz radiation ranging from 2.2 to 4.6 THz. The proposed absorber comprises a simple meta-square ring of graphene, which possesses different slots in its structure to induce multiple plasmonic resonances. It is observed that the proposed absorber manifests above 95% absorption for the normally incident THz waves, and it also maintains its absorption value over 80% for different obliquely incident operating conditions. Furthermore, the proposed absorber shows polarization-insensitive features. In addition, the absorption characteristics regulate from 95% to 15% by adjusting the chemical potential of graphene from 1 eV to 0.1 eV. Some of the salient features of the proposed absorber is largest reported bandwidth for single layer absorber with smallest footprint without sacrificing polarization insensitivity or amplitude tunability. From the application point of view, it could provide the pathway for implementing switching, cloaking, smart absorbers, and detection phenomena in the THz range.

Wireless devices have become extremely inexpensive and popular in recent years. The two most significant advantages of wireless devices over wired ones are convenience and flexibility. Considering this, a wireless mouse pad prototype for access has been developed in this study. A capacitive sensors-based mouse pad with basic operations and conventional features has been developed using sensing arrays on paper. A facile, do-it-yourself fabrication process was utilized to develop a cost-effective, thin, wearable, and cleanroom-free wireless mouse cursor control (MCC) pad. The ablation process was used to cut the traces of conductive tape and paste them onto the paper to develop the MCC pad. The pad was connected with Espressif Systems (ESP)32 to wirelessly control the cursor of mobile and laptop. The capacitive touch sensor array-based pad is easy to reproduce and recycle. This pad can contribute to future advancements in thin human-machine interfaces, soft robotics, and medical and healthcare applications.

Abstract This work presents an exact closed‐form analytical model of the space charge limited (SCL) current density for planar, cylindrical, and spherical geometries in fractional dimensional spaces (Fα) where α represents the fractional dimensions having a range 0 < α ⩽ 1. Decreasing value of α shows an increasing degree of surface roughness. This analytical model approaches Langmuir–Blodgett equations for sufficiently close anode (Ra) and cathode (Rc) radii for integer dimensions (α = 1). To calculate SCL current density for any geometry, variational calculus (VC) is used to derive the differential equations in fractional (α) dimensions. The effect of electrode radius ratios on space charge limited current is investigated here considering the surface roughness.

In this work, a performance assessment of a double perovskite Cs2BiCuI6 solar cell with the typical lead-based perovskite CH3NH3PbI3 as an active layer is presented using SCAPS-1D software. The theoretical analysis depicts the improvement in power conversion efficiency (PCE) for Cs2BiCuI6-based solar cells. This shows that in the coming future, the hybrid double perovskite Cs2BiCuI6 possess the capability to replace toxic CH3NH3PbI3-based solar cell which would help to create lead-free harmless solar cells. Detailed structural parameters optimization such as the absorbing layer thickness, and charge carrier mobility, and variation in interface defect density is carried out to enhance the device efficiency. This gives a PCE of 24.84 % for a hybrid double perovskite-based solar cell. The optimized values are 1013 cm−3, and 5 cm2 V−1 s−1 for interface defect density and carrier mobility respectively which also enhances the fill factor to 82 % with open circuit voltage of 1.28 V and 24.54 mA/cm2 short circuit current density Jsc.

The unprecedented global spread of the coronavirus pandemic COVID-19 has significantly promoted novel Internet-of-things (IoT)-based solutions to prevent, combat, monitor, or predict virus spread in the population. The proliferation of these technologies has fostered their utilization for different practical use-cases to offer reinforced control, discipline, and safety. This paper proposes an end-to-end smart navigation framework that uses Social IoT (SIoT) and Artificial Intelligence (AI) techniques to ensure pedestrians’ navigation safety through a given geographical area. The aim is to mitigate the risks of exposure to the virus and impose social distancing practices while avoiding high-risk areas identified from the SIoT data. First, we create weighted graphs representing the social relations connecting the different IoT devices in the area of interest. Second, we regroup the devices into communities according to their SIoT relations that consider their locations and owners’ friendship levels. Next, we extract CCTV recorded videos to estimate the level of social distancing practice on different roads using a computer vision model. Accordingly, the road segments are assigned weights representing their safety levels based on the extracted data. Afterward, a graph-based routing algorithm is executed to recommend the route to follow while achieving a trade-off between speed and safety. Finally, the proposed framework is generalized to enable multi-user coordinated navigation. The feasibility of the proposed approach on real-world maps and IoT datasets is corroborated in our simulation results showing an ability to balance safety and travel distance, which can be adjusted according to the user’s preferences.

Modeling and design of on-chip interconnect continue to be a fundamental roadblock for high-speed electronics. The continuous scaling of devices and on-chip interconnects generates self and mutual inductances, resulting in generating second-order dynamical systems. The model order reduction is an essential part of any modern computer-aided design tool for prefabrication verification in the design of on-chip components and interconnects. The existing second-order reduction methods use expensive matrix inversion to generate orthogonal projection matrices and often do not preserve the stability and passivity of the original system. In this work, a second-order Arnoldi reduction method is proposed, which selectively picks the interpolation points weighted with a Gaussian kernel in the given range of frequencies of interest to generate the projection matrix. The proposed method ensures stability and passivity of the reduced-order model over the desired frequency range. The simulation results show that the combination of multi-shift points weighted with Gaussian kernel and frequency selective projection dynamically generates optimal results with better accuracy and numerical stability compared to existing reduction techniques.

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices’ status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices’ features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

The increasing threat of security attacks on hardware security applications has driven research towards exploring beyond CMOS devices as an alternative. Spintronic devices offer advantages like low power, non-volatility, inherent spatial and temporal randomness, simplicity of integration with a silicon substrate, etc., making them a potential candidate for next-generation hardware security systems. In this work, we explore the Giant Spin Hall effect driven spin-orbit torque magnetic tunnel junction implementing physically unclonable function. The effect of process variation is considered in key MTJ parameters like TMR ratio, free and oxide layer thickness following Gaussian distribution, and Monte-Carlo simulations to determine the effect of the process variations. A unique challenge-response pair is obtained utilizing the inherent variations in magnetization dynamics of the free layer due to process variations.

With the advancement of beyond CMOS devices, a new approach to utilize the inherent physics of such emerging structures for various applications is of great interest in recent research. Spintronics-based devices offer key advantages like ease of fabrication with Si-substrate, non-volatile memory, low operational voltage, and non-linear device characteristics, which have shown potential for several emerging fields of study. Hardware security is one of the key interest areas which heavily relies on CMOS-based ICs, and the defense and attack mechanism is mostly based on CMOS-based structures. This work explores several emerging structures based on 2T/3T magnetic tunnel junctions (MTJ) for possible logic locking applications in hardware security systems. We demonstrate the effect of MTJ-based devices to implement logic locking even in the presence of process variations, and its ability of robustness to device imperfections has been evaluated using monte carlo simulations for practical applications.

Solid and liquid particles in the atmosphere, referred to as airborne particulate matter (PM), have been rising significantly over the past two decades. Exposure to PM carries significant health risks such as lungs damage, heart disease, cancer, and death. PM2.5 is a subgroup of PM particles that are smaller than 2.5 µm and is a major concern as it is more harmful to health and more difficult to detect. One problematic component of PM2.5 is magnetite nanoparticles (

Nowadays, security is one of the living essentials, and there is a dire need for reliable, secure, and smarter locking systems. The stand-alone smart security systems are of great interest as they do not involve keys, cards, or unsecured communication in order to prevent carrying, loss, duplication, and hacking. Here, we report an invisible touch sensors-based smart door locking system (DLS). The passive transducer-based touch sensors are fabricated through a facile do-it-yourself (DIY) based fabrication process by pasting the hybrid geometry copper electrodes on cellulose paper. The employment of biodegradable, and non-toxic materials like paper and copper tape makes this configuration a good candidate for green electronics. For additional security, the keypad in the DLS is made invisible by covering it with paper and spray paint. One can only open the door by knowing the password as well as the location of each key on the sensor keypad. The system can efficiently recognize the exact pattern of passwords without any false values. Invisible touch sensors-based locking systems can easily contribute to the security applications in homes, banks, automobiles, apartments, lockers, and cabinets.

Abstract Multi‐functional metasurfaces have attracted great attention due to the significant possibilities to realize highly integrated and ultra‐compact meta‐devices. Merging nano‐printing and holographic information multiplexing is one of the effective ways to achieve multi‐functionality, and such a merger can increase the information encoding capacity. However, the current approaches rely on stacking layers and interleaving, where multiple resonators effectively combine different functionalities on the cost of efficiency, design complexity, and challenging fabrication. To address such challenges, a single meta‐nanoresonator‐based tri‐functional metasurface is proposed by combining the geometric phase‐based spin‐decoupling and Malus's law intensity modulation. The proposed strategy effectively improves information capacity owing to the orientation degeneracy of spin‐decoupling rather than layer stacking or super‐cell designs. To validate the proposed strategy, a metasurface demonstrating two helicity‐dependent holographic outputs is presented in far‐field, whereas a continuous nano‐printing image is in near‐field. It is also employed on CMOS‐compatible and cost‐effective hydrogen amorphous silicon providing transparent responses for the whole visible band. As a result, the proposed metasurface has high transmission efficiency in the visible regime and verifies the design strategy without adding extra complexities to conventional nano‐pillar geometry. Therefore, the proposed metasurface opens new avenues in multi‐functional meta‐devices design and has promising applications in anti‐counterfeiting, optical storage and displays.​

Car-following theory has received considerable attention as a core component of Intelligent Transportation Systems. However, its application to the emerging autonomous vehicles (AVs) remains an unexplored research area. AVs are designed to provide convenient and safe driving by avoiding accidents caused by human errors. They require advanced levels of recognition of other drivers’ driving-style. With car-following models, AVs can use their built-in technology to understand the environment surrounding them and make real-time decisions to follow other vehicles. In this paper, we design an end-to-end car-following framework for AVs using automated object detection and navigation decision modules. The objective is to allow an AV to follow another vehicle based on Red Green Blue Depth (RGB-D) frames. We propose to employ a joint solution involving the You Look Once version 3 (YOLOv3) object detector to identify the leader vehicle and other obstacles and a reinforcement learning (RL) algorithm to navigate the self-driving vehicle. Two RL algorithms, namely Q-learning and Deep Q-learning have been investigated. Simulation results show the convergence of the developed models and investigate their efficiency in following the leader. It is shown that, with video frames only, promising results are achieved and that AVs can adopt a reasonable car-following behavior.

Goal: The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US’ diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. Method: In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically $k$-means, is employed to group US counties based on demographic and economic similarities. Then, time series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. Results: To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. Conclusion: Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.

Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided.

Current urbanization trends are leading to heightened demand of smarter technologies to facilitate a variety of applications in intelligent transportation systems. Automated crowdsensing constitutes a strong base for ITS applications by providing novel and rich data streams regarding congestion tracking and real-time navigation. Along with these well-leveraged data streams, drivers and passengers tend to report traffic information to social media platforms. Despite their abundance, the use of social media data in ITS has gained more and more attention as of now. In this article, we develop an automated Natural Language Processing (NLP)-based framework to empower and complement traffic reporting solutions by text mining social media, extracting desired information, and generating alerts and warning for drivers. We employ the fine-tuned Bidirectional Encoder Representations from Transformers classification model to filer and classify data. Then, we apply the Question-Answering model to extract necessary information characterizing the reported incident such as its location, occurrence time, and nature of the incidents. Afterwards, we convert the collected information into alerts to be integrated into personal navigation assistants. Finally, we compare the recently posted incident reports from both official authorities and social media in order to provide more complete incident pictures and suggest some open research directions.

Considerable attention has been given to leverage a variety of smart city applications using unmanned aerial vehicles (UAVs). The rapid advances in artificial intelligence can empower UAVs with autonomous capabilities allowing them to learn from their surrounding environment and act accordingly without human intervention. In this paper, we propose a spatiotemporal scheduling framework for autonomous UAVs using reinforcement learning. The framework enables UAVs to autonomously determine their schedules to cover the maximum of pre-scheduled events spatially and temporally distributed in a given geographical area and over a pre-determined time horizon. The designed framework has the ability to update the planned schedules in case of unexpected emergency events. The UAVs are trained using the Q-learning (QL) algorithm to find effective scheduling plan. A customized reward function is developed to consider several constraints especially the limited battery capacity of the flying units, the time windows of events, and the delays caused by the UAV navigation between events. Numerical simulations show the behavior of the autonomous UAVs for various scenarios and corroborate the ability of QL to handle complex vehicle routing problems with several constraints. A comparison with an optimal deterministic solution is also provided to validate the performance of the learning-based solution.

The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. Insurance companies are at the forefront in adopting cutting-edge operations, processes, and mathematical models to maximize profit whilst servicing their customers claims. Traditional methods that are exclusively based on human-in-the-loop models are very time-consuming and inaccurate. In this paper, we develop a secure and automated insurance system framework that reduces human interaction, secures the insurance activities, alerts and informs about risky customers, detects fraudulent claims, and reduces monetary loss for the insurance sector. After presenting the blockchain-based framework to enable secure transactions and data sharing among different interacting agents within the insurance network, we propose to employ the extreme gradient boosting (XGBoost) machine learning algorithm for the aforementioned insurance services and compare its performances with those of other state-of-the-art algorithms. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. Furthermore, we propose an online learning solution to automatically deal with real-time updates of the insurance network and we show that it outperforms another online state-of-the-art algorithm. Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.

Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to physically travel to specific locations to perform a set of spatial assignments (i.e., tasks are related to a specific geographical location besides time). To efficiently perform such tasks and guarantee the best possible quality of returned results, optimizing the worker recruitment and task assignment processes must be conducted. Because both workers and task requesters impose certain criteria, this procedure is not obvious. To tackle this issue, we propose a novel formulation of the SMCS recruitment where task matching and worker scheduling are jointly optimized. A Mixed Integer Linear Program (MILP) is first developed to optimally maximize the quality of matching measured as a weighted score function of different recruitment metrics while determining the trajectory of each selected worker executing tasks. To cope with NP-hardness, we propose a heuristic SMCS recruitment approach allowing the achievement of sub-optimal matching and recruitment solution by iteratively solving a weighted bipartite graph problem. Simulation results illustrate the performance of the SMCS framework for selected scenarios and show that our proposed SMCS recruitment algorithm outperforms an existing greedy recruitment approach. Moreover, compared to the optimal MILP solution, the proposed SMCS recruitment approach achieves close results with significant computational time saving.

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach.

In recent years, metamaterials and metasurfaces have prospered in many fields of “science and technology,” covering the entire electromagnetic spectrum. Metasurface devices constituting of a set arrangement of meta-atoms translate into modern-day miniaturized means to achieve planar, ultrathin, multifunctional electromagnetic (EM) systems. Metasurfaces are ideal candidates to develop next-generation, lightweight, and fabrication-friendly optical components as they impart local and space-variant phase changes on incident EM waves, providing more comprehensive control over EM wavefronts. This attribute has been instrumental in realizing a variety of special beams for high-capacity data transmission and superresolution imaging. Furthermore, from the perspective of efficiency, the below-par performance of previously explored plasmonic-based metasurfaces can be enhanced by employing all-dielectric metasurfaces. All-dielectric metasurfaces with high refractive indices have high resonance quality factors, low cost, and CMOS fabrication compatibility. 2D materials-based metasurface design has succeeded in further reducing the device footprints for better integration in optoelectronic devices. The conventional, time- and computation-intensive EM solvers have largely been assisted by artificial intelligence techniques, resulting in quicker metasurface designing. This review focuses on the state-of-the-art meta-devices employed for wavefront manipulations of optical waves. The design variants and applications of metasurfaces constitute a prolific field for future research to meet existing challenges and make the devices more suitable for real-time applications.

Smart energy has evolved over the years to include multiple domains integrated across multiple technology themes, such as electricity, smart grid, and logistics, linked through communication technology and processed in the cloud in a holistic way to deliver on global challenges. Advances in sensing, communication, and computation technologies have been made that enable better smart system implementations. In smart energy systems, sensing technologies have spanned multiple domains with newer techniques that are more accurate, have greater dynamic ranges, and are more reliable. Similarly, communication techniques have now evolved into very high-speed, flexible, and dynamic systems. Computation techniques have seen a quantum leap with greater integration, powerful computing engines, and versatile software stacks that are easily available and modifiable. Finally, the system integration has also seen advances in the form of management, automation, and analytics paradigms. Consequently, smart energy systems have witnessed a revolutionary transformation. The complexity has correspondingly grown exponentially. With regard to smart meters, the measurement component has to scale up to meet the demands of the evolved energy eco-system by relying on the advancements offered. The internet of things (IoT) is a key technology enabler in this scenario, and the smart meter is a key component. In recent years, metering technology has evolved in both complexity and functionality. Therefore, it must use the advances offered by IoT to deliver a new role. The internet of things (IoT) is a key technology enabler in this scenario and the smart meter a key component. In recent years, metering technology has evolved in both complexity and functionality. To deliver on its new role, it must use the advances offered by IoT. In this review, we analyze the smart meter as a combination of sensing, computing, and communication nodes for flexible and complex design paradigms. The components are, in turn, reviewed vis-à-vis the advances offered by IoT. The resultant gaps are reported for future design challenges in the conclusion. The identified gaps are the lack of usage of the full spectrum of the available technology and the lack of an inter-disciplinary approach to smart meter design.

Two-dimensional materials (2DMs) have been vastly studied for various electrochemical sensors. Among these, the sensors that are directly related to human life and health are extremely important. Owing to their exclusive properties, 2DMs are vastly studied for electrochemical sensing. Here we have provided a selective overview of 2DMs-based electrochemical sensors that directly affect human life and health. We have explored graphene and its derivatives, transition metal dichalcogenide and MXenes-based electrochemical sensors for applications such as glucose detection in human blood, detection of nitrates and nitrites, and sensing of pesticides. We believe that the areas discussed here are extremely important and we have summarized the prominent reports on these significant areas together. We believe that our work will be able to provide guidelines for the evolution of electrochemical sensors in the future.

Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as an emerging technology offering a plethora of applications touching various aspects of our lives, such as surveillance, agriculture, entertainment, and intelligent transportation systems (ITS). Furthermore, due to their low cost and ability to be fitted with transmitters, cameras, and other on-board sensors, UAVs can be seen as potential flying Internet-of-things (IoT) devices interconnecting with their environment and allowing for more mobile flexibility in the network. This paper overviews the beneficial applications that UAVs can offer to smart cities, and particularly to ITS, while highlighting the main challenges that can be encountered. Afterward, it proposes several potential solutions to organize the operation of UAV swarms, while addressing one of their main issues: their battery-limited capacity. Finally, open research areas that should be undertaken to strengthen the case for UAVs to become part of the smart infrastructure for futuristic cities are discussed.

There is a great demand for human–machine interfaces (HMIs) in emerging electronics applications. However, commercially available plastic-based HMIs are primarily rigid, application-specific, and hard to recycle and dispose of due to their non-biodegradability. This results in electronic and plastic waste, potentially damaging the environment by ending up in landfills and water resources. This work presents a green, capacitive pressure-sensitive (CPS), touch sensor-based keypad as a disposable, wireless, and intelligent HMI to mitigate these problems. The CPS touch keypads were fabricated through a facile green fabrication process by direct writing of graphite-on-paper, using readily available materials such as paper and pencils, etc. The interdigitated capacitive (IDC) touch sensors were optimized by analyzing the number of electrode fingers, dimensions, and spacing between the electrode fingers. The CPS touch keypad was customized to wirelessly control a robotic arm’s movements based on the touch input. A low-pressure touch allows slow-speed robotic arm movement for precision movements, and a high-pressure touch allows high-speed robotic arm movement to cover the large movements quickly. The green CPS touch keypad, as a disposable wireless HMI, has the potential to enforce a circular economy by mitigating electronic and plastic waste, which supports the vision of a sustainable and green world.

The role of humidity sensors in different industries and field applications, such as agriculture, food monitoring, biomedical equipment, heating, and ventilation, is well known. However, most commercially available humidity sensors are based on polymers or electronic materials that are not degradable and thus contribute to electronic waste. Here, we report a low-cost, flexible, easy-to-fabricate, and eco-friendly parallel-plate capacitive humidity sensor for field applications. The sensor is fabricated from copper tape and tissue paper, where copper tape is used to create the plates of the capacitor, and tissue paper is used as a dielectric sensing layer. Along with the low cost, the high sensitivity, better response and recovery times, stability, and repeatability make this sensor unique. The sensor was tested for relative humidity (RH), ranging from 40% to 99%, and the capacitance varied linearly with RH from 240 pF to 720 pF, as measured by an Arduino. The response time of the sensor is ~1.5 s, and the recovery time is ~2.2 s. The experiment was performed 4–5 times on the same sensor, and repeatable results were achieved with an accuracy of ±0.1%. Furthermore, the sensor exhibits a stable response when tested at different temperatures. Due to the above advantages, the presented sensor can find ready applications in different areas.

In this paper, a generic scheduling framework to manage a fleet of micro unmanned aerial vehicles (UAVs) is proposed. The objective is to employ multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in a geographical area of interest over a long period of time. The proactive scheduling framework considers several constraints and challenges, including the technical specifications of the UAVs and the limited battery capacities. In addition, the platform considers the necessity to regularly send back the UAVs to a docking station for battery recharging. A mixed integer nonlinear programming problem aiming at minimizing the total energy consumption and the number of employed UAVs is formulated to guarantee the non-redundant exploitation of the resources. Afterward, a series of linearization steps are introduced to convert the problem into a mixed integer linear programming one so that it can be optimally solved. To reduce the complexity of the problem, a dynamic time horizon discretization approach adapted to the characteristics of the problem is performed beforehand. The proposed UAV scheduling framework is formulated in a generic manner and can be applied in multiple domains comprising short- and/or long-term UAV missions while ensuring uninterrupted service.

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems. Mobile crowdsourcing enabling automatic sensing tasks constitutes an excellent mean to complement existing technologies. In this paper, we exploit the high amount of data that can be collected by on-board and infrastructure-based sensors to evaluate traffic network statuses and improve the navigation of vehicles in urban areas. The objective is to design real-time route planning algorithms that determine fastest trajectories for both single and multiple destinations, in a real-time manner based on the frequent data inputs. We first formulate the routing problems as integer linear programs (ILPs) and then, design iterative approaches levels to iteratively solve the ILPs while considering updated traffic data. Afterwards, lower complexity sub-optimal graph-based algorithms are designed to solve the real-time routing problems. Unlike traditional navigation solutions, the proposed approaches update the vehicle trajectory after a certain period characterized by timely correlated data. Uncertainty and erroneous data inputs are also considered to determine fastest and least risky trajectory. Our results show that crowdsourcing-based real-time navigation outperforms outperform traditional navigation solutions by selecting less congested roads and avoiding blocked streets.