Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Arthropods' eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor-based imaging systems. Our biomimetic imager shows the potential of integrating nature's unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing.

Download full-text PDF

Source
http://dx.doi.org/10.1126/scirobotics.adl3606DOI Listing

Publication Analysis

Top Keywords

artificial compound
12
stereoscopic artificial
8
compound eyes
8
three-dimensional space
8
wide field
8
field view
8
praying mantis
8
spatiotemporal object
8
object sensing
8
sensing tracking
8

Similar Publications

Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations.

View Article and Find Full Text PDF

The contamination of agricultural soils with military-grade explosives such as 2,4,6-trinitrotoluene (TNT), 1,3,5-trinitro-1,3,5-triazaccyclohexane (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclohexane (HMX) is an emerging concern in post-conflict regions, where food crops may take up these compounds. This study presents a novel analytical approach for detecting explosive residues in wheat (Triticum aestivum L.) grown on contaminated substrates.

View Article and Find Full Text PDF

Next-Generation Food Drying: Specialized and Smart Approaches to Boost Efficiency and Quality.

Compr Rev Food Sci Food Saf

September 2025

Department of Life Science (Food Science and Technology Division), GITAM University, Visakhapatnam, Andhra Pradesh, India.

Drying is a critical unit operation in food processing, essential for extending shelf life, ensuring microbial safety, and preserving the nutritional and sensory attributes of food products. However, conventional convective drying techniques are often energy-intensive and lead to undesirable changes such as texture degradation, loss of bioactive compounds, and reduced product quality, thereby raising concerns regarding their sustainability and efficiency. In response, recent advancements have focused on the development of innovative drying technologies that offer energy-efficient, rapid, and quality-preserving alternatives.

View Article and Find Full Text PDF

Leveraging artificial intelligence and antibiotic data to facilitate the design of novel fungicides.

Pest Manag Sci

September 2025

National Pesticide Engineering Research Center, State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, People's Republic of China.

Background: Rapid advances in generative artificial intelligence (AI) are accelerating the process of pesticide development. However, transfer learning-based de novo design focuses on generating molecules that are highly similar to existing inhibitors, which may limit the exploration of novel scaffolds and thereby constrain innovative breakthroughs in pesticide development.

Results: This study proposes a new strategy for fungicide design using antibiotics.

View Article and Find Full Text PDF

Corrosion of mild steel in marine environments poses a major challenge for industrial sustainability. This study aims to develop an eco-friendly corrosion protection approach by combining phenolic extracts (PE) from extremophile plants with Zn₂-Al-layered double hydroxides (LDH) to form hybrid inhibitors for S235JR steel in artificial seawater (3.5% NaCl).

View Article and Find Full Text PDF