Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional neural network (CNN) is a viable classification approach since HSIC depends on both spectral-spatial information. The 3-D CNN is a good alternative for improving the accuracy of HSIC, but it can be computationally intensive due to the volume and spectral dimensions of HSI. Furthermore, these models may fail to extract quality feature maps and underperform over the regions having similar textures. This work proposes a 3-D CNN model that utilizes both spatial-spectral feature maps to improve the performance of HSIC. For this purpose, the HSI cube is first divided into small overlapping 3-D patches, which are processed to generate 3-D feature maps using a 3-D kernel function over multiple contiguous bands of the spectral information in a computationally efficient way. In brief, our end-to-end trained model requires fewer parameters to significantly reduce the convergence time while providing better accuracy than existing models. The results are further compared with several state-of-the-art 2-D/3-D CNN models, demonstrating remarkable performance both in terms of accuracy and computational time.

Cognitive radio (CR) is considered as a key technology to overcome the problem of spectrum scarcity for wireless applications by the researchers. Efficient spectrum utilization is the most important and core purpose of CR systems after successful spectrum sensing. In this paper, different cases are investigated in which secondary users (SUs) can transmit their data after declaring the primary user (PU) is absent as a result of spectrum sensing. SU’s transmission in some of these cases may lead to interference in PU’s communication, which may cause inefficient spectrum utilization. In this paper, an optimization problem is formulated to optimize the sensing time that maximizes the spectrum utilization efficiency (SUE) under the constraints of high target detection probability and low probability of interference to PU. A trade-off between sensing time and spectrum utilization efficiency is shown in a given time frame for all possible cases. Theoretical and simulation results of proposed system show good match to each others. Finally proposed system is compared with conventional systems and 45% better performance in terms of optimal sensing time is achieved with maximum SUE.

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of $88.88\%$.

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (${\mathcal{P}}_d$) and probability of false alarm (${\mathcal{P}}_f$). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

This paper presents an analytical framework for the probability of spectrum hole utilization (PSHU) of a cognitive radio system with soft cooperative spectrum sensing (CSS) under a practical consideration of fixed frame structure. In practical systems, the length of a time-frame is generally fixed, where the time-frame consists of sensing, reporting, and transmission durations. Thus, increasing sensing and reporting time duration in cooperative spectrum sensing improves the probability of successful detection of the primary user’s (PU) presence or the absence but reduces transmission time duration, which results in a lower PSHU. A large reporting duration is required when more secondary users (SUs) report their sensed information to the fusion center (FC) and/or multiple bits are used by each SU in soft cooperative spectrum sensing. Thus, reporting time in terms of the number of SUs and reporting bits also have a similar effect on PSHU. Based on this interesting trade-off between PSHU and the sensing and reporting time duration, this paper analyzes the impact of an increasing number of SUs and reporting bits on PSHU.

In this paper a fusion algorithm for global positioning systems (GPS) and inertial navigation system (INS), using inertial measurement unit (IMU), is proposed for ground vehicle trajectory estimation during a GPS outage. As a standalone system an INS has its limitations and an error growth as a result of bias and drift inherent to this sensor restricts its application. A GPS can be used for vehicle navigation flawlessly, however the navigation performance may suffer due to intentional or unintentional GPS signal outage. The proposed GPS-INS fusion algorithm is able to handle any GPS blackout, restrict the INS error development and estimate a precise ownship position while utilising information from two route frameworks, i.e., the GPS and the INS. The proposed GPS-INS based matched state cascaded fusion (MSCF) algorithm, is validated by finding a decrease in it's root mean squared error (RMSE) using experiments and simulations.