Reflection Efficiency and Spectra Resolutions Ray-Tracing Simulations for the VOXES HAPG Crystal Based Von Hamos Spectrometer

The VOXES collaboration at INFN National Laboratories of Frascati developed a prototype of a high resolution Von Hamos X-ray spectrometer using HAPG (Highly Annealed Pyrolytic Graphite) mosaic crystals. This technology allows the employment of extended isotropic sources and could find application in several physics fields. The capability of the spectrometer to reach energy precision and resolution below 1 and 10 eV, respectively, when used with wide sources, has been already demonstrated. Recently, the response of this device, for a ρ = 206.7 mm cylindrically bent HAPG crystal using CuKα1,2 and FeKα1,2 XRF lines, has been investigated in terms of reflection efficiency by a dedicated ray-tracing simulation. Details of the simulation procedure and the comparison with the experimental results are presented. This study is crucial in order to retrieve information on the spectrometer signal collection efficiency, especially in the energy range in which the standard calibration procedures cannot be applied.

Explorations of A Real-Time VR Emotion Prediction System Using Wearable Brain-Computer Interfacing

The following research describes the potential of using a four-class emotion classification using a four-channel wearable EEG headset combined with VR for evoking emotions from each individual. Multiple researchers have conducted and established emotion recognition by using a 2-D monitor screen for stimulus responses but this introduces artifacts such as the lack of concentration on-screen or external noise disturbance and the bulky and cumbersome wires on an EEG device were difficult and time-consuming to set up thus restricting to only the trained professionals to operate this complex and sensitive medical equipment. Therefore, using a small and portable EEG headset where it was accessible for consumers was used for the brainwave signal collection. The wearable EEG headset collects the brainwave samples at 256Hz at specific locations of the brain (Tp9, Tp10, AF7, AF8) and samples were transformed via FFT to obtain the five bands (Delta, Theta, Alpha, Beta, Gamma) and were classified using random forest classifier. An emotion prediction system was then developed and the trained model was used to benchmark the 4-class emotion prediction accuracy from each individual using a 4-channel low-cost EEG headset. Subsequently, a real-time prediction system was implemented and tested. Early findings showed that it could achieve predictions as high as 76.50% for intra-subject classification results.

Robot Face and Its Integration to the Mobile Robot for Wireless Signal Collection in the Fingerprinting-Based Indoor Positioning System

The wireless data collection for instance the Received Signal Strength (RSS) of the Wireless Fidelity (Wi-Fi) remained unfavourable in the Indoor Positioning System utilizing the signal fingerprinting approach. This is because the enormous sampling time and routines works making it tedious human labour. To alleviate this issue, we propose to use a robot for wireless data collection. The robot, named ‘ICSiBOT’ is a service robot with multiple purpose such as assisting human in daily lives, guest or hospitality robot and man others. This paper mainly describes the ICSiBOT robot face with speech recognition technology and the integration of the robot face to the motion controller. The experimental was conducted to see the correlation between the synthesized instructions from the speech in terms of distance need to be travelled i.e., the location for wireless signal collection and translate them into actual distance travelled. The results showed that the robot is able to travel to the specific distance as instructed to the robot face.

Multimodal nonlinear endomicroscopic imaging probe using a double-core double-clad fiber and focus-combining micro-optical concept

Multimodal non-linear microscopy combining coherent anti-Stokes Raman scattering, second harmonic generation, and two-photon excited fluorescence has proved to be a versatile and powerful tool enabling the label-free investigation of tissue structure, molecular composition, and correlation with function and disease status. For a routine medical application, the implementation of this approach into an in vivo imaging endoscope is required. However, this is a difficult task due to the requirements of a multicolour ultrashort laser delivery from a compact and robust laser source through a fiber with low losses and temporal synchronization, the efficient signal collection in epi-direction, the need for small-diameter but highly corrected endomicroobjectives of high numerical aperture and compact scanners. Here, we introduce an ultra-compact fiber-scanning endoscope platform for multimodal non-linear endomicroscopy in combination with a compact four-wave mixing based fiber laser. The heart of this fiber-scanning endoscope is an in-house custom-designed, single mode, double clad, double core pure silica fiber in combination with a 2.4 mm diameter NIR-dual-waveband corrected endomicroscopic objective of 0.55 numerical aperture and 180 µm field of view for non-linear imaging, allowing a background free, low-loss, high peak power laser delivery, and an efficient signal collection in backward direction. A linear diffractive optical grating overlays pump and Stokes laser foci across the full field of view, such that diffraction-limited performance is demonstrated for tissue imaging at one frame per second with sub-micron spatial resolution and at a high transmission of 65% from the laser to the specimen using a distal resonant fiber scanner.

Design and Experiments of a Portable Seabed Integrated Detection Sonar

The integrated observation of seabed topography, sediment geomorphology and sub-bottom profile information is very important for seabed remote sensing and mapping. To improve the efficiency of seabed detection and meet the needs of portable development of detection equipment, we developed a portable seabed feature integrated detection sonar (PSIDS) with whcih a single sonar device can simultaneously detect the above three types of seabed information. The underwater transducer is mainly composed of the following three components: a parametric emission array as the sound source, a high frequency receiving linear array for multibeam echo signal collection, and a two-dimensional vector hydrophone for receiving the low-frequency sediment echo signal. Field experiments were conducted to validate the performance of the PSIDS on 11–17 January 2018 in Jiaozhou Bay, China. (1) PSIDS could perform the functions of both multibeam sonar and sub-bottom profiler; (2) The synchronously and integrated measurement of various seabed information was achieved by alternately emitting multibeam echo-sounding and sub-bottom profiling signal using parametric source. The detection results proved the feasibility and practicability of PSIDS to achieve multiple seafloor characteristics. PSIDS provides a new idea for developing integrated seabed detection sonar. In terms of convenience and data fusion, it is a good option to use this equipment for integrated seabed detection.

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.

Development and performance evaluation of a multi-modal optical spectroscopic sensor

Development of multi-modal optical spectroscopic sensors for sample analysis is often challenged by the requirement of signal collection and measurement devices utilized for specific techniques. In the present study, the...