Exact Cover with light

We suggest a new optical solution for solving the YES/NO version of the Exact Cover problem by using the massive parallelism of light. The idea is to build an optical device which can generate all possible solutions of the problem and then to pick the correct one. In our case the device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. The nodes are connected by arcs in a special way which lets us to generate all possible covers (exact or not) of the given set. For selecting the correct solution we assign to each item, from the set to be covered, a special integer number. These numbers will actually represent delays induced to light when it passes through arcs. The solution is represented as a subray arriving at a certain moment in the destination node. This will tell us if an exact cover does exist or not.Comment: 20 pages, 4 figures, New Generation Computing, accepted, 200

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography

An integrated decision support system for environmentally-friendly management of the ethiopian fruit fly in greenhouse crops

The Ethiopian fruit fly (EFF), Dacus ciliatus, is a key, invasive pest of melons in the Middle East. We developed and implemented a novel decision support system (DSS) to manage this pest in a greenhouse environment in Southern Israel. Dacus ciliatus is commonly controlled in Israel with repeated calendar-sprayings (every 15 days) of pyrethroid pesticides. The current study compares the performance of a DSS against calendar-spraying management (CSM). DSS was based on EFF population monitoring and infestation. DSS took into consideration concerns and observations of expert managers and farmers. During 2014, EFF damage was concentrated in the spring melon production season. Fall and winter production did not show important damage. Damage during the spring of 2014 started to increase when average EFF/trap/day reached 0.3. This value was suggested as the threshold to implement pesticide spraying in DSS greenhouses. EFF/trap/day trends were derived from monitoring with conventional traps and a novel electronic remote sensing trap, developed by our group. CSM during the spring of 2015 included 3 EFF control sprays, while DSS-managed greenhouses were only sprayed once. At the end of the spring season, damage was slightly higher in DSS greenhouses (1.5%), but not significantly different to that found in CSM greenhouses (0.5%). Results support continuing DSS research and optimization to reduce/remove pesticide use against EFF in melon greenhouses. Interactions with farmers and managers is suggested as essential to increase adoption of DSS in agriculture. © 2019 MDPI AG. All rights reserved

Parallel and sequential optical computing

We present a number of computational complexity results for an optical model of computation called the continuous space machine. We also describe an implementation for an optical computing algorithm that can be easily defined within the model. Our optical model is designed to model a wide class of optical computers, such as matrix vector multipliers and pattern recognition architectures. It is known that the model solves intractable PSPACE problems in polynomial time, and NC problems in polylogarithmic time. Both of these results use large spatial resolution (number of pixels). Here we look at what happens when we have constant spatial resolution. It turns out that we obtain similar results by exploiting other resources, such as dynamic range and amplitude resolution. However, with certain other restrictions we essentially have a sequential device. Thus we are exploring the border between parallel and sequential computation in optical computing. We describe an optical architecture for the unordered search problem of finding a one in a list of zeros. We argue that our algorithm scales well, and is relatively straightforward to implement. This problem is easily parallelisable and is from the class NC. We go on to argue that the optical computing community should focus their attention on problems within P (and especially NC), rather than developing systems for tackling intractable problems

A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis

Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitata, Bactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap. © 2022, The Author(s)