A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

Network selection in cognitive radio enabled Wireless Body Area Networks

In recent years,the great interest in Wireless Body Area Networks(WBANs)has been aroused significantly due to the advancement in wireless communications.In wireless communication,all WBAN nodes that monitor the human body's vital functions transfer information to a central sink node,which is directly connected to a Cognitive Radio enabled Controller called CRC.To transfer this information from a CRC to an e-health server,it requires long-range wireless networks,such as UMTS,LTE,WiMAX,WiFi,and satellite internet provider.It is challenging for a CRC to select the best networks for different WBAN data traffic,such as emergency mandatory,delay sensitive,and general monitoring.This paper proposes a scheme for selecting the best network from the available networks depending on the Quality of Service(QoS)requirements for different WBAN applications.Different multiple attribute decision-making algorithms are used in the proposed scheme.Numerical results and discussion reveal that the proposed scheme is effective in making a good network selection in situations where there is a conflict among different QoS requirements for different WBAN applications.

YFa and analogs:Investigation of opioid receptors in smooth muscle contraction

AIM:To study the pharmacological profile and inhibition of smooth muscle contraction by YFa and its analogs in conjunction with their receptor selectivity. METHODS:The effects of YFa and its analogs (D-Ala2) YFa, Y (D-Ala2) GFMKKKFMRF amide and Des-Phe-YGGFMKKKFMR amide in guinea pig ileum (GPI) and mouse vas deferens (MVD) motility were studied using an isolated tissue organ bath system, and morphine and DynA (1-13) served as controls. Acetylcholine was used for muscle stimulation. The observations were validated by specific antagonist pretreatment experiments using naloxonazine, naltrindole and norbinaltor-phimine norBNI. RESULTS:YFa did not demonstrate significant inhibition of GPI muscle contraction as compared with mor-phine (15% vs 62%, P = 0.0002), but moderate inhibition of MVD muscle contraction, indicating the role of κ opioid receptors in the contraction. A moderate inhibition of GPI muscles by (Des-Phe) YFa revealed the role of anti-opiate receptors in the smooth muscle contraction. (D-Ala-2) YFa showed significant inhibition of smooth muscle contraction, indicating the involvement of mainly δ receptors in MVD contraction. These results were supported by specific antagonist pretreatment assays. CONCLUSION:YFa revealed its side-effect-free analgesic properties with regard to arrest of gastroin-testinal transit. The study provides evidences for the involvement of κ and anti-opioid receptors in smooth muscle contraction.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.

Assessment of Organochlorine Pesticide Residues in the Surface Sediments of River Yamuna in Delhi, India

The present study reports the concentration levels and distribution patterns of the organochlorine pesticide residues in the surface sediments of river Yamuna in the Indian capital state, Delhi. Analytical measurements were carried out for twenty organochlorine pesticides (OCPs) in the Pre-monsoon, Monsoon and Post-monsoon seasons, at six different sampling locations along the 22 km stretch of the river Yamuna in Delhi. The results revealed contamination of the surface sediments with several persistent organochlorine pesticides. Endrin aldehyde, Endosulfan sulfate and DDT showed the highest percentage composition of OCP at all the sampling sites in all the three seasons. The total organochlorine pesticides level ranged from 157.71 - 307.66 ng/g in Pre-monsoon to 195.86 - 577.74 ng/g in Monsoon and 306.9 - 844.45 ng/g in the Post-monsoon season. This not only demonstrates the pollution of the river with pesticide residues, but also the necessity of a continuous long-term monitoring of the affected environment.

The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection

Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.

Traffic aware load balancing in AOMDV for mobile Ad-hoc networks

The wireless networks have a very bright future in networks and communications because of which they have taken a high interest of the researchers.As the users increased the purpose to use MANETs,they also became more diverse and wide due to which better performance is needed in MANETs.QoS is needed for applications for an efficient communication and load balancing is a feature in the routing protocol that can help in a better use of the resources and can help to increase the performance of the network.We propose a new approach for load balancing in AOMDV routing protocol for MANETs that can enhance the network performance by selecting paths using the temporal load on the intermediate nodes and by distributing the load amongst the free nodes while transmission of data,which is proved by simulations in NS-2.