Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.
Thermogenic Responses in Brown Fat Cells Are Fully UCP1-dependent

To examine the thermogenic significance of the classical uncoupling protein-1 (UCP1), the thermogenic potential of brown adipocytes isolated from UCP1-ablated mice was investigated.Ucp1(−/−) cells had a basal metabolic rate identical to wild-type; the mitochondria within them were coupled to the same degree. The response to norepinephrine in wild-type cells was robust (≈10-fold increase in thermogenesis);Ucp1(−/−) cells only responded ≈3% of this. Ucp1(−/−) cells were as potent as wild-type in norepinephrine-induced cAMP accumulation and lipolysis and had a similar mitochondrial respiratory complement. In wild-type cells, fatty acids induced a thermogenic response similar to norepinephrine, but fatty acids (and retinoate) were practically without effect inUcp1(−/−) cells. It is concluded that no other adrenergically induced thermogenic mechanism exists in brown adipocytes except that mediated by UCP1 and that entopic expression of UCP1 does not lead to overt innate uncoupling, and it is suggested that fatty acids are transformed to an intracellular physiological activator of UCP1. High expression of UCP2 and UCP3 in the tissue was not associated with an overt innate highly uncoupled state of mitochondria within the cells, nor with an ability of norepinephrine or endo- or exogenous fatty acids to induce uncoupled respiration in the cells. Thus, UCP1 remains the only physiologically potent thermogenic uncoupling protein in these cells.
An experimental site with a complex of polarimetric, combined active-passive sensors of of S-, C-, Ku-, and Ka-band of frequencies for soil and snow remote sensing and surveillance

A complex of polarimetric (dual polarization), spatio-temporally combined active-passive devices of S (~3GHz), C (~5.6GHz), Ku (~20GHz), and Ka (~37GHz) band of frequencies is represented, for bare and vegetated soils, waved water surface and land snow cover microwave reflective and emissive characteristics simultaneous, multi-frequency, polarimetric and spatially coincident measurements. The complex is dedicated to solve problems applied to soil (bear and vegetated) and snow moistures retrieval, to near water surface wind and wave field parameters retrieval, by microwave means of remote sensing, as well as applied to surface and sub-surface targets detection and identification tasks solution. The complex is set in ECOSERV ROC's control-test experimental site, in Armenia, which is equipped by facilities for microwave devices absolute calibration, by spatially distributed stations for in-situ measurements of soil moisture and temperature, and has a local meaning small weather station. This paper has an aim to attract attention of researchers who are interested in such kind measurements and to invite them to perform their own or joint measurements using available facilities.
Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands

Abstract. Millions of dollars have been spent on brush management, or removal of unwanted woody vegetation, as a conservation practice to control the presence of woody species. Land managers need an inexpensive means of monitoring the effects of brush management conservation methods for decreasing degradation in rangeland systems. In this study, free, publically available, high-resolution (1 m) imagery from the National Agricultural Imagery Program (NAIP) and moderate-resolution (30 m) Landsat-5 Thematic Mapper (TM) imagery were combined to produce a large-scale technique for mapping woody cover. High-resolution imagery-based estimates of woody cover were found to be reasonable (RMSE=3.8%, MAE=2.9%) surrogates for ground-based woody cover. An equation for TM-derived woody cover was developed. TM scenes of woody cover (TMWC) were produced and validated using NAIP and ground-based data. Results showed that the developed relation produced viable (RMSE=8.5%, MAE=6.4%) maps of woody cover that could be used to successfully track the occurrence of brush removal, as well as monitor the presence or lack of subsequent reemergence. This work provides land managers with an operational means of determining where to allocate resources to implement brush management, as well as a cost-effective method of monitoring the effects of their efforts.
X-Ray Laser Studies At LLE

New target geometries for collisional excitation x-ray laser experiments (in nickel) were proposed, analyzed and experimentally studied on the GDL laser. Experiments using a short line focus lens with new target geometries showed general agreement with predictions. The new geometries are designed to yield a higher gain and reduced refraction due to: (i) a higher plasma density, (ii) a wider lateral density profile, and (iii) a concave lateral density profile. These new geometries were: (i) two parallel exploding (thin) foils, irradiated from one side only; (ii) two ablating (thick) foils, one of which is irradiated on its inner face, and (iii) an exploding foil in front of an ablating foil, irradiated by a single laser beam incident on the thin foil. New experiments with a long line focus are in progress. The intensity ratio of Ne-like and F-like Ni lines is used to deduce the temperature, and these results together with the absolute intensity yields the density profile. The results show improvement achieved with the new target geometries: the density is higher (leading to a higher gain), and the concave density profile results in collimation rather than divergence of the x-ray laser beam. Theoretical developments included: (i) prediction of high gain on new type transitions in neon like ions, involving the excitation of an inner (2s) electron, and (ii) development of a ray tracing code for an amplifying medium of varying (e.g., collimating) lateral density profile. Results using this code are shown for convex as well as concave lateral density profiles.
Use of a maternity support binder for relief of pregnancy-related back pain.

OBJECTIVE To examine the feasibility, acceptability, and effectiveness of a support binder for low back pain in pregnancy. DESIGN Pilot study, using a prospective, two-group design with repeated measures. SETTING Ambulatory maternity clinic in a tertiary care teaching hospital. PARTICIPANTS Women of at least 20 weeks gestation with low back pain, but no preexisting back or disc disease. Thirty women assigned to the intervention group and 10 to a comparison group. INTERVENTIONS Participants completed a pain assessment at pretest. Intervention participants received a maternity support binder to wear while awake for 2 weeks. At an appointment 2 weeks later, a posttest questionnaire and a taped interview were administered. The comparison group participants received the support binder after the second appointment. MAIN OUTCOME MEASURES Back pain intensity, duration, and effect on daily activities were assessed using a pain in pregnancy questionnaire. RESULTS The intervention group had significant reduction in mean pain scores and effect of pain on daily activities, including family, house and yard, recreational, exercise, and sleep. Interaction of group by time was significant for change in pain and effect on family, house and yard, and exercise activities. CONCLUSION The use of a support binder for pregnancy-related low back pain is a promising intervention and was well-accepted by the participants.
Risk management in primary apicultural production. Part 2: a Hazard Analysis Critical Control Point approach to assuring the safety of unprocessed honey

In managing risks associated with the human consumption of honey, all sectors of the production chain must be considered, including the primary production phase. Although the introduction of the Hazard Analysis Critical Control Point (HACCP) system has not been made compulsory for purposes of quality and safety control in farming operations, European legislation makes many references to the key role of primary production in food safety management and the HACCP system has been indicated as the preferred tool to ensure that consumers are provided with safe foods. This article describes a systematic HACCP-based approach to identifying, preventing and controlling food safety hazards occurring in primary apicultural production. This approach serves as a useful tool for beekeepers, food business operators, veterinary advisors, and for Food and Veterinary Official Control Bodies in their planning and conducting of audits and for establishing priorities for the evaluation of training programmes in the apicultural sector.
Self-Interacting Dark Matter from a Non-Abelian Hidden Sector

There is strong evidence in favor of the idea that dark matter is self interacting, with the cross section-to-mass ratio σ/m∼1 cm^2/g∼1 barn/GeV. We show that viable models of dark matter with this large cross section are straightforwardly realized with non-Abelian hidden sectors. In the simplest of such models, the hidden sector is a pure gauge theory, and the dark matter is composed of hidden glueballs with a mass around 100 MeV. Alternatively, the hidden sector may be a supersymmetric pure gauge theory with a ∼10 TeV gluino thermal relic. In this case, the dark matter is largely composed of glueballinos that strongly self interact through the exchange of light glueballs. We present a unified framework that realizes both of these possibilities in anomaly-mediated supersymmetry breaking, where, depending on a few model parameters, the dark matter may be composed of hidden glueballinos, hidden glueballs, or a mixture of the two. These models provide simple examples of multicomponent dark matter, have interesting implications for particle physics and cosmology, and include cases where a subdominant component of dark matter may be extremely strongly self interacting, with interesting astrophysical consequences.
In-vivo determination of chewing patterns using FBG and artificial neural networks

This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Aditionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.
Super-resolution image reconstruction from UAS surveillance video through affine invariant interest point-based motion estimation

In traditional super-resolution methods, researchers generally assume that accurate subpixel image registration parameters are given a priori. In reality, accurate image registration on a subpixel grid is the single most critically important step for the accuracy of super-resolution image reconstruction. In this paper, we introduce affine invariant features to improve subpixel image registration, which considerably reduces the number of mismatched points and hence makes traditional image registration more efficient and more accurate for super-resolution video enhancement. Affine invariant features are invariant to affine transformations, including scale, rotation, and translation. They are extracted from the second moment matrix through the integration and differentiation covariance matrices. The experimental results show that affine invariant interest points are more robust to perspective distortion and present more accurate matching than traditional Harris/SIFT corners. In our experiments, all matching affine invariant interest points are found correctly. In addition, for the same super-resolution problem, we can use much fewer affine invariant points than Harris/SIFT corners to obtain good super-resolution results.
Towards industrial applications of terahertz real-time imaging

Recent innovations in photonics and nanotechnology are now enabling terahertz (THz) research to be applied in many industrial fields such as homeland security, information and communications technology (ICT), biology and medical science, non-destructive tests or quality control of food and agricultural products. Still many challenges are to be addressed, the main one being to provide THz systems with sufficient signal to noise ratio when operated in real industrials conditions. In addition, cost is a key lock that hampers the spread of this technology but it is clear that cost-effective sources and detectors compatible with standard microelectronics will drive down the overall cost, and in particular will make THz imaging accessible for industrial use. In order to bring THz imaging to industry, Leti has been developing over the past decade complementary CMOS-compatible uncooled imaging 2D-array technologies: antenna-coupled bolometers and Field Effect Transistor detectors. In addition, CEATech built a test platform dedicated to the development of industrial prototypes of photonics technologies. In particular, in collaboration with i2S, this platform includes the TZCAM camera equipped with Leti’s 320×240 bolometric pixel array and gives access to a full industrial THz imaging chain that is essential for maturation of this emerging technology. This paper gives an overview of these developments and illustrates industrial applications with examples of uncooled THz imaging tests, e.g. opaque object 2D inspection or 3D tomography.
Refraction index measurement using long period grating fabricated by symmetrical-hole fiber

A Long-Period Fiber Grating (LPFG) fabricated by CO2 laser in a symmetrical two-hole single-mode fiber (STHSMF) for refraction index (RI) measurement is demonstrated. The solved sugar liquid of different concentration was filled into the holes by capillary force and its RI was experimentally measured. The results show that resonant peak shifts toward shorter wavelengths with its resonance peak intensity changes a little when the external refractive index increases. RI sensitivity of 141.67 nm/RIU have been achieved with R2 of 0.975. The temperature sensitivity of the STHSMF is also investigated.
VOLCANIC SEISMOLOGY

■ Abstract Recent developments in volcanic seismology include new techniques to improve earthquake locations that have changed clouds of earthquakes to lines (faults) for high-frequency events and small volumes for low-frequency (LF) events. Spatial mapping of the b-value shows regions of normal b and high b anomalies at depths of 3–4 and 7–10 km. Increases in b precede some eruptions. LF events and very-long-period (VLP) events have been recorded at many volcanoes, and models are becoming increasingly sophisticated. Deep long-period (LP) events are fairly common, but may represent several processes. Acoustic sensors have greatly improved the study of volcanic explosions. Volcanic tremor is stronger for fissure eruptions, phreatic eruptions, and higher gas contents. Path and site effects can be extreme at volcanoes. Seismicity at volcanoes is triggered by large earthquakes, although mechanisms are still uncertain. A number of volcanoes have significant deformation with very little seismicity. Tomography has benefited from improved techniques and better instrumental arrays.
Automatically assessing properties of dynamic cameras for camera selection and rapid deployment of video content analysis tasks in large-scale ad-hoc networks

Video analytics is essential for managing large quantities of raw data that are produced by video surveillance systems (VSS) for the prevention, repression and investigation of crime and terrorism. Analytics is highly sensitive to changes in the scene, and for changes in the optical chain so a VSS with analytics needs careful configuration and prompt maintenance to avoid false alarms. However, there is a trend from static VSS consisting of fixed CCTV cameras towards more dynamic VSS deployments over public/private multi-organization networks, consisting of a wider variety of visual sensors, including pan-tilt-zoom (PTZ) cameras, body-worn cameras and cameras on moving platforms. This trend will lead to more dynamic scenes and more frequent changes in the optical chain, creating structural problems for analytics. If these problems are not adequately addressed, analytics will not be able to continue to meet end users’ developing needs. In this paper, we present a three-part solution for managing the performance of complex analytics deployments. The first part is a register containing meta data describing relevant properties of the optical chain, such as intrinsic and extrinsic calibration, and parameters of the scene such as lighting conditions or measures for scene complexity (e.g. number of people). A second part frequently assesses these parameters in the deployed VSS, stores changes in the register, and signals relevant changes in the setup to the VSS administrator. A third part uses the information in the register to dynamically configure analytics tasks based on VSS operator input. In order to support the feasibility of this solution, we give an overview of related state-of-the-art technologies for autocalibration (self-calibration), scene recognition and lighting estimation in relation to person detection. The presented solution allows for rapid and robust deployment of Video Content Analysis (VCA) tasks in large scale ad-hoc networks.
Comparison of Two Learning Methods of the Tree Augmented Naïve Bayesian Network Classifier

Generative learning and discriminative learning are two different classifier learning methods. Bayesian network classifiers belong to in nature generative classifiers because the learners always attempt to find the Bayesian network that maximizes likelihood rather than classification accuracy. In order to improve the classification performance, many researchers is trying to train the generative classifier in a discriminative way. This paper introduces two learning approaches of a restricted Bayesian network classifier, tree augmented naive Bayesian network (TAN), and compares them from several different aspects through the experiments. The experimental results demonstrate that there are diversity between the generative learning and the discriminative learning of the TAN classifier
Effects of different types of RSS data on the system accuracy of indoor localization system

Indoor localization system becomes a substantial issue in recent research, especially in terms of the accuracy. Location based services have been used in many mobile applications as well as wireless sensor networks. High accuracy and fast convergence are very important issues for a good localization system. However, the type of obtained received signal strength (RSS) data is very important in order to get high accuracy. In this paper, we introduce three types of RSS data, which are: measured RSS, simulated RSS and average combined RSS. Bayesian network based on fingerprinting technique is used to investigate the effect of the three different types of RSS. The results show the effect of the three different RSS data on the accuracy of estimated location. The measured RSS has achieved an average accuracy of 4.3 meters using 10 training points while the average combined RSS has achieved a good accuracy of 2.1meters.
Block-level algorithm classification based on RF side-channel

Internet of Things (IoT) and other similar devices often have little to no security and thus can be readily exploited in any number of ways. In this work, we collect radio frequency (RF) emissions from simple processors on several IoT devices and apply machine learning techniques to detect modifications (corrupted or injected via malware) in ‘known’ software running on the processor. We can detect these modifications due to the correlation between RF emissions and the digital state of the devices. Every bit flip produces a small but potentially detectable electrical pulse. Our approach to developing the recognition algorithm is to adapt to the variability created by the input data by recognizing the sequences in which instruction blocks are executed. Seemingly minor changes to input values can have a detectable effect on the measured RF side channel. We collect RF data from a variety of IoT devices with clock speeds varying from 16-96 MHz. A 1-GHz Riscure RF near-field antenna probe was placed within a millimeter of the IoT device, RF emissions were acquired, and software controls triggered data collection. A classification architecture was trained using object code portioned into blocks to develop the truth data. We then applied new data to the trained block classifier. This approach detects deviations in individual blocks and block sequences as a whole, allowing a greater level of detection resolution than just binary ‘Yes/No’ classification. Initial testing results showed greater than 90% classification accuracy for block-level modifications, and we can detect deviations from truth data with 100% accuracy.
Optical contacting

Optical contacting is a very interesting technology to realise stable assemblies. We will describe the latest results that were achieved thanks to Silicate Bonding with different unusual materials as Silicon, Silicon Carbide and present some examples of complex assembly: 1. Double Fabry-Perot qualified for space application 2. Stabilised bench for ultra stables wavelength lock-up system.
Compact algorithm for noise level detection

Due to the advances of imaging devices technologies, high quality video has become easy to produce. There are still problems to be solved though. Gaussian noise is always mixed in images by photo-electric conversion. Noise reducer (NR) is a signal processing method to cope with the issue. However, noise level is required for NR to work effectively. Since signal processing methods for video should work in real-times, the noise level also should be detected in real-times as well. In this paper we propose a fast and accurate noise level detection algorithm.