This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.

Invertebrates are abundant in horticulture and farming environments, and can be detrimental. Early pest detection for an integrated pest-management system with an integration of physical, biological, and prophylactic methods has huge potential for the better yield of crops. Computer vision techniques with multispectral images are used to detect and classify pests in dynamic environmental conditions, such as sunlight variations, partial occlusions, low contrast, etc. Various state-of-art, deep learning approaches have been proposed, but there are some major limitations to these methods. For example, labelled images are required to supervise the training of deep networks, which is tiresome work. Secondly, a huge in-situ database with variant environmental conditions is not available for deep learning, or is difficult to build for fretful bioaggressors. In this paper, we propose a machine-vision-based multispectral pest-detection algorithm, which does not require any kind of supervised network training. Multispectral images are used as input for the proposed pest-detection algorithm, and each image provides comprehensive information about different textural and morphological features, and visible information, i.e., size, shape, orientation, color, and wing patterns for each insect. Feature identification is performed by a SURF algorithm, and feature extraction is accomplished by least median of square regression (LMEDS). Feature fusion of RGB and NIR images onto the coordinates of Ultraviolet (UV) is performed after affine transformation. The mean identification errors of type I, II, and total mean error surpass the mean errors of the state-of-art methods. The type I, II, and total mean errors, with 6.672% UV weights, were emanated to 1.62, 40.27, and 3.26, respectively.

In the last few decades, the main problem which has attracted the attention of researchers in the field of aerial robotics is the position estimation or Simultaneously Localization and Mapping (SLAM) of aerial vehicles where the GPS system does not work. Aerial robotics are used to perform many tasks such as rescue, transportation, search, control, monitoring, and different military operations where the performance of humans is impossible because of their vast top view and reachability anywhere. There are many different techniques and algorithms which are used to overcome the localization and mapping problem. These techniques and algorithms use different sensors such as Red Green Blue and Depth (RGBD), Light Detecting and Range (LIDAR), Ultra-Wideband (UWB) techniques, and probability-based SLAM which uses two algorithms Linear Kalman Filter (LKF) and Extended Kalman filter (EKF). LKF consists of 5 phases and this algorithm is only used for linear system problems but on the other hand, EKF algorithm is also used for non-linear system. EKF is found better than LKF due to accuracy, practicality, and efficiency while dealing SLAM problem.

For the past decade, the main problem that has attracted researchers’ attention in aerial robotics is the position estimation or Simultaneous Localization and Mapping (SLAM) of Unmanned Aerial Vehicles (UAVs) where the GPS signal is poor or denied. This article reviews the strengths and weaknesses of existing methods in the field of aerial robotics. There are many different techniques and algorithms that are used to overcome the localization and mapping problem of these UAVs. These techniques and algorithms use different sensors, such as Red Green Blue-Depth (RGB_D), Light Detecting and Ranging (LIDAR), and Ultra-wideband (UWB). The most common technique is used, i.e., probability-based SLAM, which uses two algorithms: Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF). LKF consists of five phases and this algorithm is just used for linear system problems. However, the EKF algorithm is used for non-linear systems. Aerial robots are used to perform many tasks, such as rescue, transportation, search, control, monitoring, and different military operations because of their vast top view. These properties are increasing their demand as compared to human service. In this paper, different techniques for the localization of aerial vehicles are discussed in terms of advantages and disadvantages, practicality and efficiency. This paper enables future researchers to find the suitable SLAM solution based on their problems; either the researcher is dealing with a linear problem or a non-linear problem.

In this age, the automotive surveillance service is a well-known proven product implemented in conventional transport systems and widely known vehicle entities worldwide. It is a system that can be very robust and reliable. Through this work, GPS (Global Positioning System is implemented, which is used to locate and identify any automobile. This concept is an integrated device that can steadily track a speeding object and communicate the device’s status whenever its identification is required. It must obtain the NodeMCU (ESP8066 module) for the network connection device, which is routinely connected to the GPS device and the Arduino board through which it functions. The NodeMCU is used to relay the device’s status simultaneously from a distant location. The context of the application, “Bus Management System”, which is designed in MIT, provided an android studio for researchers to design any application interface. The network interface could be reviewed on the accounts-receivable public network in the form of charts. The program shows responses to the mobile application; it only requires logging in with a data network. The application, “Bus Management System”, displays the sensor values in terms of GPS coordinates statistics. This IoT based transport assistive system is cost effective and less complex from other existing IoT based transport assistive system.

Quadcopters are finding their place in everything from transportation, delivery, hospitals, and to homes in almost every part of daily life. In places where human intervention for quadcopter flight control is impossible, it becomes necessary to equip drones with intelligent autopilot systems so that they can make decisions on their own. All previous reinforcement learning (RL)-based efforts for quadcopter flight control in complex, dynamic, and unstructured environments remained unsuccessful during the training phase in avoiding the trend of catastrophic failures by naturally unstable quadcopters. In this work, we propose a complementary approach for quadcopter flight control using prediction error as an effective control policy reward in the sensory space instead of rewards from unstable action spaces alike in conventional RL approaches. The proposed predictive coding biased competition using divisive input modulation (PC/BC-DIM) neural network learns prediction error-based flight control policy without physically actuating quadcopter propellers, which ensures its safety during training. The proposed network learned flight control policy without any physical flights, which reduced the training time to almost zero. The simulation results showed that the trained agent reached the destination accurately. For 20 quadcopter flight trails, the average path deviation from the ground truth was 1.495 and the root mean square (RMS) of the goal reached 1.708.