Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Wireless body area network (WBAN) applications have broad utility in monitoring patient health and transmitting the data wirelessly. WBAN can greatly benefit from wearable antennas. Wearable antennas provide comfort and continuity of the monitoring of the patient. Therefore, they must be comfortable, flexible, and operate without excessive degradation near the body. Most wearable antennas use a truncated ground, which increases specific absorption rate (SAR) undesirably. A full ground ultra-wideband (UWB) antenna is proposed and utilized here to attain a broad bandwidth while keeping SAR in the acceptable range based on both 1 g and 10 g standards. It is designed on a denim substrate with a dielectric constant of 1.4 and thickness of 0.7 mm alongside the ShieldIt conductive textile. The antenna is fed using a ground coplanar waveguide (GCPW) through a substrate-integrated waveguide (SIW) transition. This transition creates a perfect match while reducing SAR. In addition, the proposed antenna has a bandwidth (BW) of 7–28 GHz, maximum directive gain of 10.5 dBi and maximum radiation efficiency of 96%, with small dimensions of 60 × 50 × 0.7 mm³. The good antenna’s performance while it is placed on the breast shows that it is a good candidate for both breast cancer imaging and WBAN.

Ultra-wideband (UWB) technology can offer broad capacity, short-range communications at a relatively low level of energy usage, which is very desirable for wireless body area networks (WBANs). The involvement of the human body in such a device poses immense difficulties for both the architecture of the wearable antenna and the broadcast model. Initially, the bonding between the wearable antenna and the human body should also be acknowledged in the early stages of the design, so that both the potentially degrading output of the antenna as a consequence of the body and the possibility of exposure for the body may be handled. Next, the transmission path in WBAN is affected by the constant activity of the human body, leading to the time-varying dispersion of electromagnetic waves. Few researchers were interested in this field, and some substantial progress has recently been considered. On the other hand, this paper covered both wearable and Non-wearable UWB antenna designs and applications with respect to their substrate characteristics. Finally, this review prospectively exposes the upgraded developments of (ON-OFF) body antennas in the area of wearable and Non-wearable UWB and their implementations in the WBAN device and aims to evaluate the latest design features that inspire the performance of the antennas.

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

This paper represents a miniaturized, dual-polarized, multiple input–multiple output (MIMO) wearable antenna. A vertically polarized, leaf-shaped antenna and a horizontally polarized, tree-shaped antenna are designed, and the performance of each antenna is investigated. After designing the MIMO antenna, it is loaded with stubs, parasitic spiral, and shorting pins to reduce the coupling effects and remove the unwanted resonances. Afterward, the two-port MIMO cells are spaced by 2 mm and rotated by 90° to create three more cells. The antennas are designed using two layers of denim and felt substrates with dielectric constants of 1.2 and 1.8, and thicknesses of 0.5 mm and 0.9 mm, respectively, along with the ShieldIt™ conductive textile. The antenna covers a bandwidth of 4.8–30 GHz when the specific absorption rate (SAR) meets the 1 g and 10 g standards. Isolation greater than 18 dB was obtained and mutual coupling was reduced after integrating shorting pins and spiral parasitic loadings. A maximum radiation efficiency and directive gain of 96% and 5.72 dBi were obtained, respectively, with the relatively small size of 11 × 11 × 1.4 mm³ for the single element and final dimensions of 24 × 24 × 1.4 mm³ for the full assembly. The antenna’s performance was examined for both on-body (breast) and free space conditions using near-field microwave imaging. The achieved results such as high fidelity, low SAR, and accuracy in localization of the tumour indicate that the MIMO antenna is a decent candidate for breast cancer imaging.