The Global Positioning System (GPS) signals are used for navigation and positioning purposes by a diverse set of users. As a part of GPS modernization effort L5 has been recently introduced for better accuracy and availability service. This paper intends to study and simulate the GPS L1/L5 signal in order to fulfill the following two objectives. The first aim is to point out some important features/differences between current L1 (whose characteristics have been fairly known and documented) and new L5 GPS signal for performance evaluation purpose. The second aim is to facilitate receiver development, which will be designed and assembled later for the actual acquisition of GPS data. Simulation has been carried out for evaluation of correlation properties and link budgeting for both L1 and L5 signals. The necessary programming is performed in Matlab.

Due to the technological advancement in cellular networks, massive data traffic appends to the existing digital technologies. These emerging digital technologies face quality of service (QoS) challenges, particularly when it comes to maintaining the tradeoffs between customers and service providers. The cellular service providers are trying to meet the needs of end users by handling four substantially different types of data, i.e., Real Time, Video, Audio, and Text, with each type having its own requirements. To achieves an efficient QoS, different incentive-based algorithms were proposed. However, these schemes do not ensure a fair distribution of profit among the mobile network operator and access points. Furthermore, these schemes do not provide efficient QoS to the end user and cannot ensure a fair distribution of channels in crowning time. We propose an incentive-based scheme using game theory and two-stage Stackelberg approach for integrated data, offloading the decision-making process in a heterogeneous network. A single mobile base station and some integrated access points in a crowded metropolitan area are modeled in our proposed scheme. This station offers an economic incentive based on traffic types, and access points compete with each other to earn incentives for offload traffic. A mathematical game is derived to analyze the real-world scenario through simulation. The experimental method is applied to validate the numerical outcomes by comparing the results with other models.

Orthogonal frequency division multiplexing (OFDM) is a favourable technology for dynamic spectrum access (DSA) due to the flexibility in spectrum shaping. In spite of that, high sidelobes of OFDM subcarriers bring in considerable interference to the nearby users, particularly in OFDM based cognitive radio (CR) networks, where the secondary users (SUs) are capable of accessing the spectrum opportunistically. In this paper, two new techniques for the suppression of high sidelobes are proposed. The proposed techniques composed of an optimization scheme are followed by generalized sidelobe canceller. The proposed techniques can be considered as a two-level suppression technique in the sense that in the first level the sidelobe is reduced by using cancellation carriers (CCs), whose amplitudes are determined using genetic algorithm (GA) and differential evolution (DE), while in the second level further reduction of sidelobe is achieved using generalized sidelobe canceller (GSC). Simulation results show the power spectral density (PSD) performance of the proposed techniques in comparison with already existing techniques, demonstrating that the proposed techniques minimize the out-of-band radiation (OOBR) significantly, thus qualifying for more effective spectrum sharing.

Lack of visibility in a fire-and-smoke environment is one of the major factors that causes firefighting operational difficulties, injuries or loss of life. To counter this problem, distance or range-finding sensors are used to detect obstacles or map out the area in a fire and smoke environment. These sensors can be mounted on robots assisting firefighting or even firefighters themselves. This paper aims to assess the operational capability of ultrasonic distance sensors in fire-and-smoke environments. Moreover, we investigate how to extract useful information in limiting conditions. Specifically, we design experiments to test Sonar’s range-finding abilities, which are interfered with by burning different types of fuels. The experiments are performed for smoke without flame (smoke pallets), flame without smoke (propanol) and flame with smoke (kerosene) at different distances from Sonar. The results show that Sonar is very effective in smoke because smoke without flame does not increase the air temperature significantly. However, if interference consists of flame, air temperature increases; thus, Sonar outputs erratic data. We analyzed this erratic output of Sonar and provided a filtering algorithm that can eliminate the erratic and stray values from Sonar output and provide valuable information that is helpful in navigation, mapping, or obstacle detection.

Skin cancer is a significant global health concern. Early and accurate detection is crucial for enhancing patient outcomes. This study conducts an in-depth literature review to identify commonly used Convolutional Neural Network (CNN) variants, datasets, and key evaluation metrics to assess their performance in classifying benign and malignant skin lesions. Widely used CNN architectures, including ResNet, EfficientNet, DenseNet, AlexNet, VGG, GoogleNet, LeNet-5, Xception, and MobileNet were implemented. A comparative analysis is conducted based on metrics such as accuracy, precision, sensitivity, recall, and F1-score, highlighting the strengths and limitations of each algorithm. The results show that VGG-16 outperforms other models with an accuracy of 97%, followed by VGG-19 and Mobilenet-v2 with 88%. Lastly, this paper highlights the trade-offs between various metrics, providing critical insights for deploying AI-based skin cancer detection algorithms in clinical practice.

Data aggregation is a proven technique for mitigating issues in wireless sensor networks. In this paper, reference pattern-based data aggregation in a wireless sensor network is utilised for data aggregation to conserve energy and eliminate redundancy in a wireless sensor network. In traditional data aggregation, each node forwards its data to the cluster head, which aggregates it and then forwards it to the base station. This process takes higher energy. However, in energy-efficient secure pattern-based data aggregation (ESPDA), each sensor node initially generates a pattern code from a lookup table based on the sensed data and then transmits the data to the cluster head. Pattern codes are indicative of sensed data and compact in size. Production of pattern codes from a lookup table is problematic. First, creating a lookup table consumes more computational resources; second, searching for critical values within the corresponding interval also consumes more computational resources and energy. In this research, a new mechanism has been proposed to reduce the consumption of computational and energy resources. We used a reference-based mechanism to create the pattern codes. The simulation results indicate that the proposed mechanism is more energy and computation-efficient than ESPDA.