Mosquito-borne diseases continue to pose a great threat to global public health systems due to increased insecticide resistance and climate change. Accurate vector identification is crucial for effective control, yet it presents significant challenges. IDX - an automated computer vision-based device capable of capturing mosquito images and outputting mosquito species ID has been deployed globally resulting in algorithms currently capable of identifying 53 mosquito species. In this study, we evaluate deployed performance of the IDX mosquito species identification algorithms using data from partners in the Southeastern United States (SE US) and Papua New Guinea (PNG) in 2023 and 2024. This preliminary assessment indicates continued improvement of the IDX mosquito species identification algorithms over the study period for individual species as well as average regional accuracy with macro average recall improving from 55.3 % [Confidence Interval (CI) 48.9, 61.7] to 80.2 % [CI 77.3, 84.9] for SE US, and 84.1 % [CI 75.1, 93.1] to 93.6 % [CI 91.6, 95.6] for PNG using a CI of 90 %. This study underscores the importance of algorithm refinement and dataset expansion covering more species and regions to enhance identification systems thereby reducing the workload for human experts, addressing taxonomic expertise gaps, and improving vector control efforts.

MmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are carried out in a variety of practical scenarios including pedestrians, a car, and an UAV, also known as a drone. With measured data, range-angle maps are created, and morphological operators are used to estimate the AoA of the targets. We also process radar range-angle images for improved visual representation. The proposed method will be extremely beneficial for practical ground stations, traffic control and monitoring frameworks for both on-ground and airborne vehicles.

The Internet of Things (IoT) is the most popular technology exist due to its huge number of application. One of the components of IoT is wireless sensor network (WSN). The WSN is the combination of huge amount of sensor nodes, which are responsible to generate the data for the IoT network. These sensor notes are very low power computing devices, due to that they also have low range of communication. The wireless sensor network generates the data and sends the data through the intermediate sensor node to the sink node. The sink node sometime also called gateway node or Base Station (BS), which collects and aggregates the data and transmit it to the cloud for further storage and processing, which is again a component of IoT. The data reaches the gateway node using different routing protocol with the aim of less energy cost. As we are talking about low power sensor nodes the power efficiency is the most importance performance metrics while developing the routing protocol. In this paper, a cluster-based routing protocol for IoT enabled WSN network has been proposed. The cluster-based routing protocol known as the energy efficient protocol due to its lower Energy consumption for cluster by aggregating the data within the cluster head and reducing the number of data transmission to the gateway node. In this paper, the cluster-head selection and cluster formation are defined, and simulation has been conducted. Further, the proposed algorithm compared with the existing research on cluster-based routing protocol in the basis of different metrics such as energy consumption, throughput and the lifetime of the network.

Mosquito-borne diseases continue to pose a great threat to global public health systems due to increased insecticide resistance and climate change. Accurate vector identification is crucial for effective control, yet it presents significant challenges. IDX - an automated computer vision-based device capable of capturing mosquito images and outputting mosquito species ID has been deployed globally resulting in algorithms currently capable of identifying 53 mosquito species. In this study, we evaluate deployed performance of the IDX mosquito species identification algorithms using data from partners in the Southeastern United States (SE US) and Papua New Guinea (PNG) in 2023 and 2024 (till June). This preliminary assessment indicates continued improvement of the IDX mosquito species identification algorithms over the study period for individual species as well as average regional accuracy with macro average recall improving from 55.3% [Confidence Interval (CI) 48.9, 61.7] to 80.2% [CI 77.3, 84.9] for SE US, and 84.1% [CI 75.1, 93.1] to 93.6% [CI 91.6, 95.6] for PNG using a CI of 90%. This study underscores the importance of algorithm refinement and dataset expansion covering more species and regions to enhance identification systems thereby reducing the workload for human experts, addressing taxonomic expertise gaps, and improving vector control efforts.