Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSimple and robust speckle detection method for fire and heat detection in harsh environments
158
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Standard laser based fire detection systems are often based on measuring variation of optical signal amplitude. However, mechanical noise interference and loss from dust and steam can obscure the detection signal, resulting in faulty results or inability to detect a potential fire. The presented fire detection technology will allow the detection of fire in harsh and dusty areas, which are prone to fires, where current systems show limited performance or are unable to operate. It is not the amount of light nor its wavelength that is used for detecting fire, but how the refractive index randomly fluctuates due to the heat convection from the fire. In practical terms this means that light obstruction from ambient dust particles will not be a problem as long as a small fraction of the light is detected and that fires without visible flames can still be detected. The standalone laser system consists of a Linux-based Red Pitaya system, a cheap 650 nm laser diode, and a PIN photo-detector. Laser light propagates through the monitored area and reflects off a retroreflector generating a speckle pattern. Every 3 seconds time traces and frequency noise spectra are measured and 8 descriptors are deduced to identify a potential fire. Both laboratory and factory acceptance tests have been performed with success.
rate research
Read More
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric augmentation method, a gradual self-labeling adaptation procedure, and a teacher-guided fine-tuning mechanism. Using our method, target domain samples can be leveraged to adapt object detection models without changing the model architecture or generating synthetic data. When applied to image corruptions and high-level cross-domain adaptation benchmarks, our method outperforms prior baselines on multiple domain adaptation benchmarks. SimROD achieves new state-of-the-art on standard real-to-synthetic and cross-camera setup benchmarks. On the image corruption benchmark, models adapted with our method achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6% AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method outperformed the best baseline performance by up to 8% AP50 on Comic dataset and up to 4% on Watercolor dataset.
Detection and direct power conversion of high energy and high intensity ionizing radiation could be a key element in next generation nuclear reactor safety systems and space-born devices. For example, the Fukushima catastrophe in 2011 could have been largely prevented if 1% of the reactors remnant radiation ($gamma$-rays of the nuclear fission) were directly converted within the reactor to electricity to power the water cooling circuit. It is reported here that the hybrid halide perovskite methylammonium lead triiodide could perfectly play the role of a converter. Single crystals were irradiated by a typical shut-down $gamma$-spectrum of a nuclear reactor with 7.61E14 Bq activity exhibit a high-efficiency of $gamma$-ray to free charge carrier conversion with radiation hardening. The power density of 0.3 mW/kg of methylammonium lead triiodide at 50 Sv/h means a four times higher efficiency than that for silicon-based cells. The material was stable to the limits of the experiment without changing its performance up to 100 Sv/h dose rate and 57 Sv H*(10) ambient total $gamma$-dose. Moreover, the {gamma}-shielding performance of methylammonium lead triiodide was found to be superior to both ordinary and barite concrete.
Single photon detectors are important for a wide range of applications each with their own specific requirements, which makes necessary the precise characterization of detectors. Here, we present a simple and accurate methodology of characterizing dark count rate, detection efficiency, and after-pulsing in single photon detectors purely based on their counting statistics. We demonstrate our new method on a custom-made, free-running single photon detector based on an InGaAs based avalanche photo diode (APD), though the methodology presented here is applicable for any type of single photon detector.
Residual speckles due to aberrations arising from optical errors after the split between the wavefront sensor and the science camera path are the most significant barriers to imaging extrasolar planets. While speckles can be suppressed using the science camera in conjunction with the deformable mirror, this requires knowledge of the phase of the electric field in the focal plane. We describe a method which combines a coronagraph with a simple phase-shifting interferometer to measure and correct speckles in the full focal plane. We demonstrate its initial use on the Stellar Double Coronagraph at the Palomar Observatory. We also describe how the same hardware can be used to distinguish speckles from true companions by measuring the coherence of the optical field in the focal plane. We present results observing the brown dwarf HD 49197b with this technique, demonstrating the ability to detect the presence of a companion even when it is buried in the speckle noise, without the use of any standard calibration techniques. We believe this is the first detection of a substellar companion using the coherence properties of light.
To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision. Multi-modal sensor signals could provide more information for such anomaly detection tasks; however, the fusion of high-dimensional and heterogeneous sensor modalities remains a challenging problem. We propose a deep learning neural network: supervised variational autoencoder (SVAE), for failure identification in unstructured and uncertain environments. Our model leverages the representational power of VAE to extract robust features from high-dimensional inputs for supervised learning tasks. The training objective unifies the generative model and the discriminative model, thus making the learning a one-stage procedure. Our experiments on real field robot data demonstrate superior failure identification performance than baseline methods, and that our model learns interpretable representations. Videos of our results are available on our website: https://sites.google.com/illinois.edu/supervised-vae .
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
