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An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model's efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios. [ABSTRACT FROM AUTHOR]

Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study's findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications. [ABSTRACT FROM AUTHOR]

The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system's understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs. [ABSTRACT FROM AUTHOR]

A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework's exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets. [ABSTRACT FROM AUTHOR]

This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image space can lead to unpredictable movements of the robot in Cartesian space. While researchers have addressed this problem for mobile robots, quadrotors continue to face significant challenges. To tackle this issue, the adopted approach involves considering the separation of altitude control from the other variables, thus reducing the workspace. Furthermore, the movements of the quadrotor (pitch, roll, and yaw) are interdependent. Consequently, the connection between the inputs and outputs cannot be reversed. The task complexity becomes significant. To address this issue, we propose the following scenario: When the quadrotor is equipped with a downward-facing camera, flying at high altitude is sensible to spot a target. However, to minimize disturbances and conserve energy, the quadrotor needs to descend in altitude. This can result in the target being lost. The solution to this problem is a new methodology based on the principle of differential flatness, allowing the separation of altitude control from the other variables. The system first detects the target at high altitude, then plots a trajectory in the image coordinate system between the acquired image and the desired image. It is crucial to emphasize that this step is performed offline, ensuring that the image processing time does not affect the control frequency. Through the proposed trajectory planning, complying with the constraints of differential flatness, the quadrotor can follow the imposed dynamics. To ensure the tracking of the target while following the generated trajectory, the proposed control law takes the form of an Image Based Visual Servoing (IBVS) scheme. We validated this method using the RVCTOOLS environment in MATLAB. The DJI Phantom 1 quadrotor served as a testbed to evaluate, under real conditions, the effectiveness of the proposed control law. We specifically designed an electronic card to transfer calculated commands to the DJI Phantom 1 control joystick via Bluetooth. This card integrates a PIC18F2520 microcontroller, a DAC8564 digital-to-analogue converter, and an RN42 Bluetooth module. The experimental results demonstrate the effectiveness of this method, ensuring the precise tracking of the target as well as the accurate tracking of the path generated in the image coordinate system. [ABSTRACT FROM AUTHOR]

Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research's primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field. [ABSTRACT FROM AUTHOR]

A circularly polarized (CP) and wide-band monopole antenna with a miniaturized size is suggested in this study. The suggested structure is composed of a U-shaped radiator on the front side, a partial ground plane with two rectangle slots, and a quadrilateral-shaped parasitic strip on the back side of the FR4 substrate. A wide-band operation with S11 ≤ −10 dB was achieved by regulating the radiator and the partial ground that was placed on the second side of the antenna substrate. The CP was achieved when excited two modes with the same amplitude and a 90° phase difference. This could be generated by regulating the slots' dimensions in the ground plane. Moreover, a quadrilateral-shaped parasitic strip placed on the second side with the partial ground was utilized to extend the 3 dB axial ratio (AR) bandwidth. The suggested structure is simulated, prototyped, and measured to confirm the desired requirements with a total size of 30 × 32 mm2 (0.4 × 0.42 λ0 at 4 GHz). The tested outcomes have a bandwidth of S11 ≤ −10 dB (81.25%) (5.2 GHz, 3.8–9 GHz) and a 3 dB axial ratio (AR) bandwidth (30.7%) (1.63 GHz, 4.48–6.11 GHz). The antenna's different parameters are discussed, which recommend the suggested antenna to be used in UWB, sub 6 GHz, and WLAN wireless applications. [ABSTRACT FROM AUTHOR]

This paper is a comprehensive review of the recent literature studies on the developments and applications of millimeter-wave (mm-wave) dielectric resonator antennas (DRAs). Different designs and techniques for linear and circular polarized DRAs are discussed thoroughly. In addition, array and multiple-input multiple-output (MIMO) DRAs operating in the K, Ka, and V bands are illustrated. These applications are highly advantageous on many levels, resulting in the improved performance of the DRA in terms of obtaining a higher gain, lower losses, a higher efficiency, and a lower profile. This work reviews the fundamental research trends in antennas to meet the demands of fifth-generation (5G) communications and beyond. The reviewed studies are scholarly sources which contain measurement-based results. This paper concludes by highlighting the limitations of the studies and the implications for future research. [ABSTRACT FROM AUTHOR]

A novel approach is proposed to design a circularly polarized (CP) hemispherical dielectric resonator antenna (DRA) with a wide axial ratio (AR) bandwidth by incorporating an additional dielectric substrate between the antenna and the ground plane. This is in addition to the lower feeding substrate that is located between the ground plane on one side and the feeding microstrip line on the other side. Adding another substrate on top of the ground plane provided an additional degree of freedom in the design that facilitated the achievement of ab 18% AR bandwidth. In addition, an integrated hemispherical DRA and perforated substrate configuration was utilized to achieve optimum effective substrate permittivity and overcome the DRA alignment and assembly challenges while maintaining the achieved wide CP bandwidth. A close agreement was achieved between measurements and simulations. [ABSTRACT FROM AUTHOR]

A wideband hemispherical dielectric resonator antenna (DRA) with enhanced gain is proposed for a frequency band of 20 to 28 GHz. The precise alignment and assembly of the DRA represent key challenges at such frequencies that were addressed using three approaches: the first was based on outlining the DRA position on the ground plane, the second involved creating a groove in the compound ground plane in which the DRA is placed, and the third was based on the 3D-printing of the DRA on a perforated substrate. In all cases, the same DRA was utilized and excited in a higher-order mode using an annular ring slot. The high gain was achieved by exciting a higher-order mode, and the wideband was obtained by merging the bandwidths of the two excited modes. The alignment methods used expedite the DRA prototyping by saving substantial time that is usually spent in adjusting the DRA position with respect to the feeding slot. The proposed configurations were measured, with an impedance bandwidth of 33.33% and a maximum gain of 10 dBi observed. Close agreement was achieved between the measured and simulated results. [ABSTRACT FROM AUTHOR]

A compact multiple-input-multiple-output (MIMO) dielectric resonator antenna (DRA) that is suitable for internet of things (IoT) sensor networks is proposed with reduced coupling between elements. Two rectangular-shaped DRAs have been placed on the opposite sides of a Rogers substrate and each is fed using a coplanar waveguide (CPW) feed with slots etched in a dedicated metal ground plane that is located under the DRA. Moreover, locating the elements at the opposite sides of the substrate has improved the isolation by 27 dB without the need to incorporate additional complex structures, which has reduced the overall antenna size. Furthermore, a dual band operation is achieved since each antenna resonates at two frequencies: 28 GHz and 38 GHz with respective impedance matching bandwidths of 18% and 13%. As a result, the corresponding data rates are also increased independently. In addition to the advantages of improved isolation, compact size and dual band operation, the proposed configuration offers a diversity gain (DG), envelope correlation coefficient (ECC) and channel capacity loss (CCL) of 9.98 dB, 0.007, 0.06 bits/s/Hz over the desired bands, respectively. A prototype has been built with good agreement between simulated and measured results. [ABSTRACT FROM AUTHOR]