Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

Phenotypic Diversity among Fennel (Foeniculum Vulgare) Germplasm of Pakistan

Fennel is facing continuous challenge with reference to biotic and abiotic stresses that can be solved with the knowledge of available germplasm of fennel in the country or worldwide. Selection of fennel genotype on the basis of research interest can never been accomplished without gene pool. The aim of the present study was to explore the phenotypic diversity among selective fennel accession and identify lines having high yielding potential. In the present study thirty fennel accessions were sown in PGRI, NARC. Irrigation practice was carried out during the growing period. Data was recorded during different growth stages and after harvesting. Nine morphological parameters under study include plant height, number of umbels/plant, umbel diameter, rays produced/umbel, fruits produced/umble, fruit color and fruit shape. Statistical analysis was performed using ANOVA, Tukey Honest Significance Test and Multivariate cluster Analysis using Minitab Software version 20.0. High diversity was observed among the quantitative traits of thirty accessions. Qualitative traits of accessions from similar region had considerable resemblance. Fennel germplasm collected from Punjab gives outstanding performance with reference to phenotypic traits. Accessions were identified as potential sources including: 21293 (maximum plant height, Punjab, Jhang, Chiniot), 21209 ( great height, Punjab, Faisalabad), 21737 (short height, Punjab, Layyah, Karore Chak-84) 21699 (maximum number of rays/umbel, Punjab, Pakpattan) and 21722 (maximum number of umbels , Punjab, Narowal, Talwandi Bhandran in short 21722 due to high yield was identified as potential sources to be included in future breeding programs for the improvement of fennel varieties

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications

An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution

Feedforward Neural Network-Based Data Aggregation Scheme for Intrabody Area Nanonetworks

An intrabody area nanonetwork (intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling noninvasive healthcare monitoring and disease diagnosis. In this article, we design a novel feedforward neural networks (FFNNs) based data aggregation scheme that integrates the attributes of artificial intelligence to boost the computational intelligence of intra-BANNs for prolonged network lifetime. In the proposed scheme, data division and labeling are performed to transmit detected information using two different types of packets with different sizes to save energy resources and to avoid redundant data transmission. FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to increase the transmission probability of critical information with minimum energy consumption and delay, whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimal delay and storage overhead. The detailed evaluation and comparison of our proposed framework with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50%–60% better than state-of-the-art schemes in terms of residual energy, delay, and packet loss

Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks

An intrabody nanonetwork (IBNN) is composed of nanoscale (NS) devices, implanted inside the human body for collecting diverse physiological information for diagnostic and treatment purposes. The unique constraints of these NS devices in terms of energy, storage and computational resources are the primary challenges in the effective designing of routing protocols in IBNNs. Our proposed work explicitly considers these limitations and introduces a novel energy-efficient routing scheme based on a fuzzy logic and bio-inspired firefly algorithm. Our proposed fuzzy logic-based correlation region selection and bio-inspired firefly algorithm based nano biosensors (NBSs) nomination jointly contribute to energy conservation by minimizing transmission of correlated spatial data. Our proposed fuzzy logic-based correlation region selection mechanism aims at selecting those correlated regions for data aggregation that are enriched in terms of energy and detected information. While, for the selection of NBSs, we proposed a new bio-inspired firefly algorithm fitness function. The fitness function considers the transmission history and residual energy of NBSs to avoid exhaustion of NBSs in transmitting invaluable information. We conduct extensive simulations using the Nano-SIM tool to validate the in-depth impact of our proposed scheme in saving energy resources, reducing end-to-end delay and improving packet delivery ratio. The detailed comparison of our proposed scheme with different scenarios and flooding scheme confirms the significance of the optimized selection of correlated regions and NBSs in improving network lifetime and packet delivery ratio while reducing the average end-to-end delay

An Efficient Routing Scheme for Intrabody Nanonetworks Using Artificial Bee Colony Algorithm

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme

EFFECTIVENESS OF NEURODYNAMICS IN COMPARISON TO MANUAL TRACTION IN THE MANAGEMENT OF CERVICAL RADICULOPATHY

Background: Cervical radiculopathy is a condition of pain and sensorimotor deficits due to cervical nerve root compression. The symptoms may include weakness, tingling, numbness and pain. C6, C7 nerve roots are most involved in cervical radiculopathy. Various modalities and therapeutic interventions are used and recommended for management of cervical radiculopathy including cervical collars, immobilization, manipulation, cervical traction TENS and therapeutic exercises.The aimof this study is to evaluate the efficacy of neurodynamics in comparison to manual traction in the management of cervical radiculopathy. Methods: An Interventional research was performed in the Department of Physiotherapy, Mayo hospital Lahore, Pakistan. 40 subjects aged between 18-60 years participated in the study. They were divided into two groups namely Group A and Group B with 20 subjects in each group. The duration of the study was 4 weeks with 4 sessions per week. GroupA received neurodynamics along with strengthening exercises while Group B received manual traction along with strengthening exercises. Neck Disability Index (NDI) scale was used as an outcome measure andpaired sample t-test was used for statistical analysis. Results: A significant improvement was found in both neurodynamics group and manual traction group for pain and functional status with p value< 0.05. Conclusion: This study concluded that the treatment techniques, neurodynamics and manual traction were effective in alleviating the symptoms associated with cervical radiculopathy in terms of decreasing pain intensity, increasing ranges of motion and improving functional capacity