Quantum Random Number Generation Using a Quanta Image Sensor

A new quantum random number generation method is proposed. The method is based on the randomness of the photon emission process and the single photon counting capability of the Quanta Image Sensor (QIS). It has the potential to generate high-quality random numbers with remarkable data output rate. In this paper, the principle of photon statistics and theory of entropy are discussed. Sample data were collected with QIS jot device, and its randomness quality was analyzed. The randomness assessment method and results are discussed

Single photon detection for quantum technologies

During this thesis work, two approaches to single-photon detection at telecom wavelengths have been investigated. The first one focuses on improving the performance of commercially available semiconductor single photon avalanche diodes (SPADs) to meet quantum communication requirements, and the second one describes the development and characterization of large active-area superconducting nanowire single-photon detectors (SNSPDs). On the application side, quantum random number generation (QRNG) was implemented using two schemes based on two different types of single photon detectors activated by a LED

Temporal jitter in free-running InGaAs/InP single-photon avalanche detectors

Negative-feedback avalanche diodes (NFADs) provide a practical solution for different single-photon counting applications requiring free-running mode operation with low afterpulsing probability. Unfortunately, the timing jitter has never been as good as for gated InGaAs/InP single-photon avalanche diodes. Here we report on the timing jitter characterization of InGaAs/InP based NFADs with particular focus on the temperature dependence and the effect of carrier transport between the absorption and multiplication regions. Values as low as 52 ps full-width at half-maximum were obtained at an excess bias voltage of 3.5 V and an operating temperature of around −100°C

Deep learning for semantic segmentation

This reference is related to chapter 3 of the book.International audienc

Quantum Random Number Generation Using a Quanta Image Sensor

A new quantum random number generation method is proposed. The method is based on the randomness of the photon emission process and the single photon counting capability of the Quanta Image Sensor (QIS). It has the potential to generate high-quality random numbers with remarkable data output rate. In this paper, the principle of photon statistics and theory of entropy are discussed. Sample data were collected with QIS jot device, and its randomness quality was analyzed. The randomness assessment method and results are discussed

Fault-Tolerant FPGA-Based Nanosatellite Balancing High-Performance and Safety for Cryptography Application

With the growth of the nano-satellites market, the usage of commercial off-the-shelf FPGAs for payload applications is also increasing. Due to the fact that these commercial devices are not radiation-tolerant, it is necessary to enhance them with fault mitigation mechanisms against Single Event Upsets (SEU). Several mechanisms such as memory scrubbing, triple modular redundancy (TMR) and Dynamic and Partial Reconfiguration (DPR), can help to detect, isolate and recover from SEU faults. In this paper, we introduce a dynamically reconfigurable platform equipped with configuration memory scrubbing and TMR mechanisms. We study their impacts when combined with DPR, providing three different execution modes: low-power, safe and high-performance mode. The fault detection mechanism permits the system to measure the radiation level and to estimate the risk of future faults. This enables the possibility of dynamically selecting the appropriate execution mode in order to adopt the best trade-off between performance and reliability. The relevance of the platform is demonstrated in a nano-satellite cryptographic application running on a Zynq UltraScale+ MPSoC device. A fault injection campaign has been performed to evaluate the impact of faulty configuration bits and to assess the efficiency of the proposed mitigation and the overall system reliability

Explainability of Image Semantic Segmentation Through SHAP Values

International audienceThe introduction of Deep Neural Networks in high-level applications is significantly increasing. However, the understanding of such model decisions by humans is not straightforward and may limit their use for critical applications. In order to address this issue, recent research work has introduced explanation methods, typically for classification and captioning. Nevertheless, for some tasks, explainability methods need to be developed. This includes image segmentation that is an essential component for many high-level applications. In this paper, we propose a general workflow allowing for the adaptation of a state of the art explainability methods, especially SHAP, to image segmentation tasks.The approach allows for explanation of single pixels as well image areas. We show the relevance of the approach on a critical application such as oil slick pollution detection on the sea surface. We also show the applicability of the method on a more standard multimedia domain semantic segmentation task. The conducted experiments highlight the relevant features on which the models derive their local results and help identify general model behaviours

Explainability of Image Semantic Segmentation Through SHAP Values

International audienceThe introduction of Deep Neural Networks in high-level applications is significantly increasing. However, the understanding of such model decisions by humans is not straightforward and may limit their use for critical applications. In order to address this issue, recent research work has introduced explanation methods, typically for classification and captioning. Nevertheless, for some tasks, explainability methods need to be developed. This includes image segmentation that is an essential component for many high-level applications. In this paper, we propose a general workflow allowing for the adaptation of a state of the art explainability methods, especially SHAP, to image segmentation tasks.The approach allows for explanation of single pixels as well image areas. We show the relevance of the approach on a critical application such as oil slick pollution detection on the sea surface. We also show the applicability of the method on a more standard multimedia domain semantic segmentation task. The conducted experiments highlight the relevant features on which the models derive their local results and help identify general model behaviours

Explainability of Image Semantic Segmentation Through SHAP Values

International audienceThe introduction of Deep Neural Networks in high-level applications is significantly increasing. However, the understanding of such model decisions by humans is not straightforward and may limit their use for critical applications. In order to address this issue, recent research work has introduced explanation methods, typically for classification and captioning. Nevertheless, for some tasks, explainability methods need to be developed. This includes image segmentation that is an essential component for many high-level applications. In this paper, we propose a general workflow allowing for the adaptation of a state of the art explainability methods, especially SHAP, to image segmentation tasks.The approach allows for explanation of single pixels as well image areas. We show the relevance of the approach on a critical application such as oil slick pollution detection on the sea surface. We also show the applicability of the method on a more standard multimedia domain semantic segmentation task. The conducted experiments highlight the relevant features on which the models derive their local results and help identify general model behaviours

Effect of Benzothiadiazole and Salicylic Acid Resistance Inducers on Orobanche foetida Infestation in Vicia faba

The broomrape or orobanche (Orobanche foetida) is considered as an important agricultural problem of faba bean (Vicia faba var. minor) production in Tunisia. The effect of salicylic acid (SA) and benzothiadiazole (BTH) on the induction of faba bean resistance to O. foetida was studied. Three application methods (seed soaking, foliar spraying and watering) were used. SA and BTH treatments reduced broomrape infestation under controlled conditions in pot and Petri dish experiments. In pot experiment, SA and BTH treatments reduced broomrape total number. Seed soaking treatments were more effective than foliar spraying and watering. In Petri dish experiment, O. foetida seed germination and the number of orobanche tubercles were reduced. The most efficient method was watering for SA and BTH treatments. This reduction was associated to a delay in the tubercle formation. The different application methods of SA and BTH treatment attest that the induced systemic resistance to O. foetida can be used in integrated management of broomrapes