Delivery drones typically perform delivery by suspending the parcel vertically or landing the drone to drop off the package. However, because of the constrained landing area and the requirement for precise navigation, delivering items to customers who reside in multi-story apartment complexes poses a unique challenge. This research paper proposes a novel drone delivery system for multi-story apartment buildings with balconies that employ two methods for Vertical Grid Screening (VGS), i.e., Grid Screening (GS) and Square Screening (SS), to detect unique markers to identify the precise balcony that needs to receive the product. The developed drone has a frame size of 295 mm and is equipped with a stereo camera and a ranging sensor. The research paper also explores the scanning and trajectory methods required for autonomous flight to accurately approach the marker location. The proposed machine learning system is trained on a YOLOv5 model for image recognition of the marker, and four different models and batch sizes are compared. The 32-batch size with a 960 × 1280 resolution model provides an average of 0.97 confidence for an extended range. This system is tested outdoors and shows an accuracy of 95% for a planned trajectory with 398 ms detection time as a solution for last-mile delivery in urban areas.

A descriptive guide to develop smart sensor nodes for Internet of Things (IoT) application has been presented in the paper. The current technology of low-power wide area network (LPWAN) which transmits data with low power consumption over a wide area has been illustrated in the scope of this project. An overview of the transition of IoT devices from a limited range personal area network to wide area network is justified. A sensor node has been developed and tested to validate the implementation of a LoRa network, which can be an exemplar for sensor nodes for various applications.

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stable and balanced transfer that precisely localizes the receiver drone. The proposed EBS drone mitigates the effects of downwash due to the vertical proximity between the drones by implementing diagonal alignment with the receiver, reducing the distance to 0.5 m between the two drones. CFD analysis shows that diagonal instead of perpendicular alignment minimizes turbulence, and the authors verify the actual system for change in output airflow and thrust measurements. The CMP marker-based localization method enables position lock for the EBS drone with up to 0.9 cm accuracy. The performance of the transfer mechanism is validated experimentally by successful mid-air transfer in 5 seconds, where the EBS drone is within 0.5 m vertical distance from the receiver drone, wherein 4m/s turbulence does not affect the transfer process.

A vehicle capable of using sensors to detect and control its driving actions is called an autonomous vehicle. The development of autonomous vehicles caters to many application areas in the technological advancement of society. This research paper shows a demonstration and implementation of an autonomous vehicle based on a convolutional neural network. The vehicle uses a 1/10th scale RC car as its primary base for the system control with the camera as its primary input. For the computing platform, a Raspberry Pi 4 microprocessor board is used. To enhance the capabilities, the ultrasonic sensor has been implemented in the system as well. The unique aspect of this project is the system design, the CAD modeling, and the track built used to train and test the self-driving capability of the car. The CNN model and the software algorithm also are exclusive to this research project. This research has potential in a variety of application areas in education and also for robotics and autonomous car enthusiasts.

Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.

Black Soldier Fly larvae (BSFL) play a vital role in waste management by efficiently converting organic waste into protein-rich feed, thereby reducing environmental impact and supporting sustainable agriculture. To optimize BSFL production, precise monitoring of adult flies is essential. This paper introduces FlyCount, a system developed with neuromorphic vision sensors and a custom spike detection algorithm for real-time, accurate fly counting. The system’s architecture integrates advanced event stream processing with dynamic thresholding, achieving 95% accuracy across 30 trials, validated against manual counts. FlyCount also visualizes fly trails, providing real-time insights into movement patterns. Moreover, variations in count plots enable monitoring of peak fly departures from the hatch, offering critical data for optimizing hatching and collection processes. This data-driven approach enhances the scalability of waste processing systems, fostering sustainability and a brighter future, while also representing a foundational step toward a broader goal of leveraging neuromorphic systems to tackle the complex challenges facing humanity.