Last generation instrument for agriculture multispectral data collection

In recent years, the acquisition and analysis of multispectral data are gaining a growing interest and importance in agriculture. On the other hand, new technologies are opening up for the possibility of developing and implementing sensors with relatively small size and featuring high technical performances. Thanks to low weights and high signal to noise ratios, such sensors can be transported by different type of means (terrestrial as well as aerial vehicles), giving new opportunities for assessment and monitoring of several crops at different growing stages or health conditions. The choice and specialization of individual bands within the electromagnetic spectrum ranging from the ultraviolet to the infrared, plays a fundamental role in the definition of the so-called vegetation indices (eg. NDVI, GNDVI, SAVI, and dozens of others), posing new questions and challenges in their effective implementation. The present paper firstly discusses the needs of low-distance based sensors for indices calculation, then focuses on development of a new multispectral instrument specially developed for agricultural multispectral analysis. Such instrument features high frequency and high resolution imaging through nine different sensors (1 RGB and 8 monochromes with relative band-pass filters, covering the 390 to 950 nm range). The instrument allows synchronized multiband imaging thanks to integrated global shutter technology, with a frame rate up to 5 Hz; exposure time can be as low as 1/5000 s. An applicative case study is eventually reported on an area featuring different materials (organic and non-organic), to show the new instrument potential. Last generation instrument for agriculture multispectral data collection. Available from: https://www.researchgate.net/publication/317596952_Last_generation_instrument_for_agriculture_multispectral_data_collection [accessed Jul 11, 2017]

Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories

The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a spontaneous change of device conductance with a similarity to biological mechanisms. They rely on the formation and self-disruption of a metallic conductive filament through an oxide layer, with a retention time ranging from a few milliseconds to several seconds, greatly tunable according to the maximum current which is flowing through the device. Here we prove a neuromorphic system based on volatile-RRAMs able to mimic the principles of biological decision-making behavior and tackle the Two-Alternative Forced Choice problem, where a subject is asked to make a choice between two possible alternatives not relying on a precise knowledge of the problem, rather on noisy perceptions

Redox memristors with volatile threshold switching behavior for neuromorphic computing

The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors

HYPERSPECTRAL SENSOR WITH AMBIENT LIGHT DETECTOR

An apparatus (1) for detecting reflectance by means of images, which apparatus (1) comprises: a vehicle (2) carrying a multi-spectral camera (5) mounted on it, provided with multiple first selective filters (6) optically coupled with corresponding first optical sensors (7); and an optical detection device (10) of the incident radiation mounted on the vehicle (2). The optical detection device (10) comprises: multiple second selective filters (11); a second optical sensor (12) optically coupled with second selective filters (11); and a focusing lenticular optical system (14), which is interposed between the second selective filters (11) and the second optical sensor (12), and is arranged to project the radiations coming from the second selective filters (11) onto the sensitive surface (13) of the second optical sensor (12)

Accuracy performance of a Time-of-Flight CMOS range image sensor system

This paper analyzes the performance of an Indirect Time Of Flight (ITOF) CMOS range image sensor. A number of experiments were performed to measure the actual performance of the system under test and to highlight its strength and weak points, with a focus on which limits are related to the design and which are intrinsic in the operating principle. The evaluated system allows fast and accurate measurements but pixel-level calibration is needed to achieve high accuracy over the whole image. Simple temporal or spatial filtering allow a significant reduction of the measurement uncertaint

Intra-frame techniques for high-dynamic range CMOS image sensors

High Dynamic range is one of the main research fields NeuriCam S.p.A. have been always involved in. This work is an excursus of NeuriCam approaches to this topic

Tunable synaptic working memory with volatile memristive devices

Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In particular, the working memory (WM) utilizes mechanisms that span orders of magnitudes of timescales, from milliseconds to seconds or even minutes. This plentitude of timescales is an essential ingredient of WM tasks like visual or language processing. This degree of flexibility is challenging in analog computing hardware because it requires the integration of several reconfigurable capacitors of different size. Emerging volatile memristive devices present a compact and appealing solution to reproduce reconfigurable temporal dynamics in a neuromorphic network. We present a demonstration of WM using a silver-based memristive device whose key parameters, retention time and switching probability, can be electrically tuned and adapted to the task at hand. First, we demonstrate the principles of WM in a small scale hardware to execute an associative memory task. Then, we use the experimental data in two larger scale simulations, the first featuring WM in a biological environment, the second demonstrating associative symbolic WM

High dynamic range CMOS image sensors in biomedical applications

The biomedical environment is one of the most recent and interesting application fields for CMOS image sensors. Low power consumption, high sensitivity and a simple interface are the main required features; nevertheless high dynamic range can be considered one of the more interesting and less investigated aspects. High Dynamic range is one of the main research fields NeuriCam has been involved in since its incipit. This work is an excursus of NeuriCam’s approaches to this topic

Last generation instrument for agriculture multispectral data collection

In recent years, the acquisition and analysis of multispectral data are gaining a growing interest and importance in agriculture. On the other hand, new technologies are opening up for the possibility of developing and implementing sensors with relatively small size and featuring high technical performances. Thanks to low weights and high signal to noise ratios, such sensors can be transported by different type of means (terrestrial as well as aerial vehicles), giving new opportunities for assessment and monitoring of several crops at different growing stages or health conditions . The choice and specialization of individual bands within the electromagnetic spectrum ranging from the ultraviolet to the infrared, plays a fundamental role in the definition of the so-called vegetation indices (eg. NDVI, GNDVI, SAVI, and dozens of others), posing new questions and challenges in their effective implementation. The present paper firstly discusses the needs of low-distance based sensors for indices calculation, than focuses on development of a new multispectral instrument specially developed for agricultural multispectral analysis. Such instrument features high frequency and high resolution imaging through nine different sensors (1 RGB and 8 monochrome with relative band-pass filters, covering the 390 to 950 nm range). The instrument allows synchronized multiband imaging thanks to integrated global shutter technology, with a frame rate up to 5 Hz; exposure time can be as low as 1/5000 s. An applicative case study is eventually reported on an area featuring different materials (organic and non-organic), to show the new instrument potential