The selection and assessment of single-photon detection modules is a crucial problem in satellite-based QKD systems. The system's overall efficiency, secure key rate and quantum bit error rate are all significantly influenced by single-photon detection modules. There is a knowledge gap about the practical performance of commercially available single-photon detectors because existing research frequently relies on theoretical characteristics. This paper introduces a study on the effect of the parameters of three commercial single photon detection modules from ID Quantique company: ID Qube, ID100, and ID281 on certain Bennett-Brassard 1984 protocol parameters such as secure key rate, mean photon number per pulse, quantum bit error rate

ABSTRACT

In this research been to use some of the semi-parametric methods the based on the different function penalty as well as the methods proposed by the researcher  because these methods work to estimate and variable selection of significant at once for single index model including (SCAD-NPLS method , the first proposal SCAD-MAVE method , the second proposal  ALASSO-MAVE method ) .As it has been using a method simulation time to compare between the semi-parametric estimation method studied , and various simulation experiments to identify the best method based on the comparison criteria (mean squares error(MSE) and average  mean squares error (AMSE)).

And the use

This study includes the direct influence of (single & multi) dose of Cold Atmospheric Plasma (CAP) on the no. of platelets for mice for different exposure time (15, 30, 60, and 120) sec. the influence of CAP on mice was measured after 1, 2, 3, 7, and 14 day from exposure.
The results obtained in this study indicate that the effect of low doses of CAP on platelets was stimulatory effect in the first few hours from exposure (1day) but the high dose was inhibitory, It was found that after two weeks of exposure that the number of platelets became normal comparable to the control one, and this indicates that plasma effect was removed after this period.

Photonic Crystal Fiber Fabry–Perot Interferometers (FPI) based on Surface Plasmon Resonance (SPR) was investigated in this paper in order to detect changes in photonic crystal fiber sensitivity with increasing temperature. FPI is composed of a PCF (ESM-12) solid core spliced with a single-mode fiber (SMF) on one side and a 40nm thick gold Nano film on the other. In order to obtain the SPR curve, the end of PCF can be spliced with the side of SMF before covering the gold film on the PCF. SPR results are included in the suggested sensor, based on the conclusions of the investigations. Resolution (R) is 0.0871, Signal-to-Noise Ratio (SNR) is 0.1867, a figure of merit (FOM) is 0.0069, and sensitivity (S) is 1.1481 . This sensor proposed is s

Cadmium sulfide photodetector was fabricated. The CdS nano
powder has been prepared by a chemical method and deposited as a
thin film on both silicon and porous p- type silicon substrates by spin
coating technique. Structural, morphological, optical and electrical
properties of the prepared CdS nano powder are studied. The X-ray
analysis shows that the obtained powder is CdS with predominantly
hexagonal phase. The Hall measurements show that the nano powder
is n-type with carrier concentration of about (-5.4×1010) cm-3. The
response time of fabricated detector was measured by illuminating
the sample with visible radiation and its value was 5.25 msec. The
specific detectivity of the fabricated det

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot.  Simulation results, whi

A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle
swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA)

The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

The dependence of the energy losses or the stopping power for the ion contribution in D- T hot plasma fuels upon the corresponding energies and the related penetrating factorare arrive by using by a theoretical approximation models. In this work we reach a compatible agreement between our results and the corresponding experimental results.

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St