\r\n\tThis book aims to address the new developments in the rapidly evolving field of evo-devo in the post genomics era. All recent biological and medical breakthroughs in the evo-devo field are welcomed. Finally, review articles encompassing recent advances, development current and future trends are also more than welcomed.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c17ab0c64ce206c75ad6cec64e05737",bookSignature:"Dr. Dimitrios P. Vlachakis, Prof. Elias Eliopoulos and Prof. George P. Chrousos",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10100.jpg",keywords:"genetics, genome, non coding RNAs, post translational modifications, methylation, ancestral genes, horizontal gene transfer, molecular regulators, gene expression levels, transcription factor, chromatin modifications, developmental biology, cell growth, cellular differentiation, stem cells, big data, algorithm design, cloud computing, next generation sequencing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 20th 2019",dateEndSecondStepPublish:"October 11th 2019",dateEndThirdStepPublish:"December 10th 2019",dateEndFourthStepPublish:"February 28th 2020",dateEndFifthStepPublish:"April 28th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"179110",title:"Dr.",name:"Dimitrios",middleName:"P.",surname:"Vlachakis",slug:"dimitrios-vlachakis",fullName:"Dimitrios Vlachakis",profilePictureURL:"https://mts.intechopen.com/storage/users/179110/images/system/179110.jpeg",biography:"Dr. Dimitrios Vlachakis is an Assistant Professor at the Genetics Laboratory at the Biotechnology Department of the Agricultural University of Athens, Greece. He leads the Genetics and Computational Biology Group and his main scientific interests revolve around the investigation of genetic polymorfisms, genetic variability in viral strains and the in silico drug design of novel antiviral and anticancer agents. To date, Dr. Vlachakis has published more than 90 original research articles in international peer-reviewed journals with impact factor, 100+ articles in international conference proceedings, 5 monograph ISBN books, 2 scientific patents and has been on the receiving end of numerous grants and awards. Since 2012 Dr. Vlachakis has been serving as the Editor in Chief and Associate Editor of relevant international journals.",institutionString:"Agricultural University of Athens",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Agricultural University of Athens",institutionURL:null,country:{name:"Greece"}}}],coeditorOne:{id:"300155",title:"Prof.",name:"Elias",middleName:null,surname:"Eliopoulos",slug:"elias-eliopoulos",fullName:"Elias Eliopoulos",profilePictureURL:"https://mts.intechopen.com/storage/users/300155/images/system/300155.jpg",biography:"Prof. Elias Eliopoulos leads the bioinformatics and structural biology group of the Genetics Laboratory of the Biotechnology Department of the Agricultural University of Athens. A biophysicist/crystallographer by training has considerable experience in biomolecular structure analysis, epitope mapping of protein receptors with experimental and computational methods, protein structure prediction, ligand and drug design, protein design, in silico antibody design and biosoftware development. Prof. Elias Eliopoulos has experience and international reputation on computational protein folding, ab initio and homology modeling of proteins derived from edge gene research and membrane protein modeling. He is also involved in many ligand computational design projects for pharmaceutical, medicinal and agricultural applications.",institutionString:"Agricultural University of Athens",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Agricultural University of Athens",institutionURL:null,country:{name:"Greece"}}},coeditorTwo:{id:"148006",title:"Prof.",name:"George P.",middleName:null,surname:"Chrousos",slug:"george-p.-chrousos",fullName:"George P. Chrousos",profilePictureURL:"https://mts.intechopen.com/storage/users/148006/images/system/148006.jpg",biography:"George P. Chrousos, MD, ScD born in Patras, Greece is a Greek-American pediatrician, endocrinologist, neurobiologist and one of the world\\'s foremost medical clinical researchers. He serves as Professor of Pediatrics and Endocrinology and Chairman of the First Department of Pediatrics at the National and Kapodistrian University of Athens School of Medicine, Athens, Greece. He currently also holds the UNESCO Chair on Adolescent Health Care at the University of Athens. He is one of the 250 most cited scientists internationally (ISI highly cited), included not only in the list of Clinical Medicine, but also in that of Biology and Biochemistry, and the highest cited clinical pediatrician or endocrinologist in the world.\r\nProf. Chrousos is intimately familiar with the strengths and weaknesses of both US and European biomedical science. He has an unwavering commitment to promote and nurture the international leadership and excellence of research in Developmental Biology, Human Growth and Development, Pediatrics, Reproductive Health, Obstetrics and Gynecology, and Family and Disabilities Research.",institutionString:"Medical School Athens",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"287827",firstName:"Gordan",lastName:"Tot",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/287827/images/8493_n.png",email:"gordan@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40154",title:"Ultrashort Laser Pulses for Frequency Upconversion",doi:"10.5772/48541",slug:"ultrashort-laser-pulses-for-frequency-upconversion",body:'Single-photon frequency upconversion is a nonlinear process where the frequency of the signal photon is translated to the higher frequency with the complete preservation of all the quantum characteristics of the “flying” qubits [1,2]. One promising application of the single-photon frequency upconversion is converting the infrared photons to the desired spectral regime (usually visible regime) where the high performance detectors are available for sensitive detections [3-6]. The infrared single-photon frequency upconversion detection technique has been successfully used in a variety of applications, including infrared imaging [7] and infrared ultra-sensitive spectroscopy [8,9]. Additionally, such upconversion detector has greatly benefited the applications stringently requiring efficient photon detection in optical quantum computation and communication [10-12]. Furthermore, due to the phase-matching requirement in the frequency upconversion process, the upconversion detectors have some unique features, such as narrow-band wavelength acceptance [13] and polarization sensitivity [14], both of which render them very useful for fiber based quantum systems [15]. In the recent decade, experiments bear that the photon correlation, entanglement, and photon statistics are all well preserved in the coherent frequency upconversion [16-18].
Although the demonstration of frequency upconversion technology for strong light could be dated to the late 70s of last century [19], the single-photon frequency upconversion was just achieved at the beginning of this century [20]. The advent and rapid evolution of fabricating the periodically reversed nonlinear media led to the widespread use of quasi-phase matching (QPM), which has opened up new operating regimes for nonlinear interactions [21]. Rapidly growing interest has been focused in recent years on proposing novel schemes for achieving single-photon frequency upconversion with high efficiency and low noise and expanding its applications in all-optical nonlinear signal processing and in quantum state manipulation. With the help of extracavity enhancement, highly efficient up-conversion at single-photon level has been demonstrated by using bulk periodically poled lithium niobate (PPLN) crystals [3]. By means of intracavity enhancement, a stable and efficient single-photon counting at 1.55 μm was achieved without the requirement of sensitive cavity servo feed-back [4,22]. Recently, frequency upconversion based on the PPLN waveguide attracts more and more attention [9,23]. All of the above single-photon frequency upconversion systems were pumped by the strong continuous wave (CW) lasers which would inevitably bring about severe background noise due to parasitic nonlinear interactions. Frequency upconversion based on pulsed pump arises to provide an effective solution [24]. To make sure that every signal photon can be interact with the pulsed pump field, synchronous pumping frequency upconversion system is thus introduced, which greatly improves the total detection efficiency. VanDevender et al. reported a frequency upconversion system based on electronic synchronization of the pump source and signal source with a repetition rate of just 40 kHz [5]. For satisfying applications of high-speed single-photon detection, fast and efficient single-photon frequency upconversion detection system operating at tens of MHz was realized based on the all-optical synchronized fiber lasers [25-27].
Thanks to the ultrashort optical pulses, intense peak power to obtain unit conversion efficiency could be achieved with a modest average pump power, thus loosening the restriction for the available output power from the pump laser [3,4]. Additionally, the background noise induced by the strong pump can be effectively reduced due to the pulsed excitation. Thus ultrashort pulses constitute ideal means as the pump source of frequency upconversion. The pulsed pumping system could not only permit efficient photon counting of infrared photons, but also quantum manipulation of single photons. One can realize a quantum state router by having a control on the multimode pump source [28]. Current researches also demonstrate simultaneous wavelength translation and amplitude modulation of single photons from a quantum dot by pulsed frequency upconversion [29]. Additionally, the concept of a quantum pulse gate is presented and an implementation is proposed based on spectrally engineered frequency upconversion [30].
In this chapter, we review the recent experimental progress in single-photon frequency upconversion with synchronous pulse pumping laser. The signal photons were tightly located within the synchronous pump pulses [25]. For improving the conversion efficiency, the specific control of the synchronized pulses was required, which led to the development of temporally and spectrally controlled single-photon frequency upconversion for pulsed radiation [26,27]. The compact fiber-laser synchronization system for fast and efficient single-photon frequency upconversion detection is of critical potential to stimulate promising applications, such as infrared photon-number-resolving detector (PNRD) [18,31] and ultrasensitive infrared imaging at few-photon level. This chapter is organized as follows. After this brief introduction, we present the basic theory for frequency upconversion in quantum frame in Section 2. Section 3 presents the experimental realization of synchronous pulsed pumping. Applications based on the synchronous pumping frequency upconversion system will be highlighted in Section 4 by the examples of infrared photon-number-resolving detection and several-photon-level infrared imaging. We conclude the chapter in Section 5 by emphasizing that the synchronous pumping frequency upconversion system will benefit numerous applications not only in infrared photon counting and also in manipulation of the quantum state of single photons.
Frequency upconversion based on sum frequency generation (SFG) is an optical process by which two optical fields combine in a quadratic nonlinear medium to generate a third field at a frequency equal to the sum of the two inputs as shown in Fig. 1. The theory of the SFG is well established for a long time and applied in a variety of classical and quantum optical applications. In this section, we go through the fundamental theory of SFG and derive the important results in quantum architecture. We investigate the quantum features according to different pumping methods for frequency upconversion, respectively. Specially, we will concentrate on the detailed discussion of the quantum characteristics in the multimode pumping scheme.
Schematic illustration of the frequency upconversion process.
In quantum optics, the frequency upconversion is a nonlinear optical process, in which a photon at one frequency is annihilated and another photon at a higher frequency is created. We start with a Hamiltonian for three-wave mixing:
where
Since for most cases, the upconverted frequency wave was in vacuum state at the input of the nonlinear medium. So the initial condition at the input facet of the nonlinear medium could be written as
where
where L is the interaction length in the nonlinear crystal. The corresponding creation operators can be found by taking the Hermitian conjugates of these equations.
Eq. (5) indicates that frequency translation of any quantum state at
At the output of the nonlinear medium, the average photon number of infrared signal can be calculated from expected value of the photon number operator:
where
Meanwhile, the average number of the SFG photons can be likewise given by
From Eq. (6) and Eq. (7), the upconversion efficiency can be given as
Additionally, we can get
The joint probability
Inserting Eq. (8) into the above equation, we have
Correlation between the upconverted SFG photons and unconverted signal photons.
which is dependent on the conversion efficiency of the frequency upconversion process.
Moreover, the intensity cross-correlation function
When input signal photons are in single photon state, meaning
All above results are obtained on the basis of perfect phase matching. The phase matching condition is sensitive in the SFG interaction due to the limit spectral bandwidth of the nonlinear medium. Taking into account the phase mismatching [32], the quantum state of the SFG photons can be expressed as
where
Could the complete quantum state transduction be feasible when the signal and the pump source were both in the multi-longitudinal modes? This is the instance of the synchronous pumping frequency upconversion system. Obviously, the frequency conversion process becomes more complicated due to the commutative nonlinear coupling of the pump modes and signal modes.
The Hamiltonian can be rewritten as
where the
The superposition state of the infrared signal photons and SFG photons are represented by the operators
where
We get the same results as that in a single-longitudinal mode situation (Eqs. (4, 5)). When
Here we can clearly see that either in single-mode regime or in multi-mode regime, the upconversion can, in principle, be used to transduce one photon at a given wavelength to another wavelength in preservation of all the quantum characteristics. Such quantum upconverter would not only be useful in the fields related to efficient infrared photon counting, but also helpful to implement novel quantum functions such as quantum interface and quantum gate or quantum shaper. In next section, experimental realization of the single-photon frequency upconversion with synchronously pumping will be demonstrated at length.
To achieve efficient and low-noise single-photon frequency upconversion system, various schemes have been proposed by the researchers for different applications in recent years. The upconversion technique typically requires a sufficiently strong pump to achieve unity nonlinear frequency conversion efficiency in a quadratic nonlinear crystal. The requisite strong pump can be achieved by using an external cavity or intracavity enhancement [3,4] or a waveguide confinement [9,23]. With such high intensity of pump, frequency up-conversion was implemented with almost 100% internal conversion efficiency. Nevertheless, a strong pump field inevitably brings about severe background noise because of parasitic nonlinear interactions, such as spontaneous parametric downconversion and Raman scattering. Pulsed pumping technique is given rise to circumscribe induced noise within the narrow temporal window of the pump pulses. Ultralow background counts of 150/s was reported with the help of a long-wavelength pump scheme [24]. In order to include every signal photon within the pump pulse for improving the total detection efficiency, researchers recently develop a coincidence single-photon frequency upconversion system [5,25-27]. In this section, we will focus on the synchronous pulsed pumping technique and its experimental results.
Fig. 3 shows the experimental setup of the synchronous single-photon frequency upconversion detection. The system was composed of a passive master-slave synchronization fiber-laser system and a single-photon frequency upconversion counting system. In the synchronization fiber-laser system, both two fiber lasers were passively mode-locked by the nonlinear polarization rotation in the fiber cavity, operating at the repetition rate of 17.6 MHz to satisfy the high-speed detection. The master laser was an Er-doped fiber ring laser. The ring cavity consisted with 1.5-m Er-doped fiber, standard single-mode fiber and dispersion-shifted fiber. By optimizing the lengths of the single-mode and dispersion-shifted fibers while maintaining the total cavity length, the dispersion in the cavity was well controlled. The spectrum of the output laser centered at 1563.8 nm with a full width at half maximum (FWHM) of 6.2 nm. A narrow spectral portion of the master laser was extracted by the FBG reflection for approaching the QPM bandwidth of the PPLN. This part was further amplified by an Er-doped fiber amplifier (EDFA) as the pump source. The maximum output power was about 60 mW and the output spectrum from the amplifier centered at 1557.6 nm with an FWHM of 0.56 nm [Fig. 4(a)]. The transmission from the FBG was injected as a seed to the slave laser cavity via a 1040/1557-nm wavelength-division multiplexer (WDM) to trigger the mode-locking of the Yb-doped fiber laser. An FBG was used as well at the slave laser output to control the spectral matching of the signal photons. The filtered spectrum was centered at 1040.0 nm with the bandwidth about 0.35 nm (FWHM) [Fig. 4(b)]. Injection-locking of the slave laser ensured the synchronization between the master and slave laser due to the fact that the transmission from the FBG shared exactly the same spectral mode separation with the reflection.
Experimental setup of the synchronous single-photon frequency upconversion detection. YDFL, ytterbium-doped fiber laser; EDFL, erbium-doped fiber laser; EDFA, erbium-doped fiber amplifier; Cir, circulator; Col, collimator; Atten, attenuator; WDM, wavelength-division multiplexer; FBG, fiber Bragg grating; PPLN, periodically poled lithium niobate crystal; APD, avalanche photodiodes.
With this master-slave configuration, the two synchronized lasers could be isolated from each other, and thus free from mutual perturbation. This all-optical synchronization technique is superior to other passive synchronization methods due to its simplicity and robustness [33-35]. If self-started mode-locking existed in the slave laser, synchronized injection-locking could be obtained as well, but only within a sensitive slave cavity length match and could not last long, which would not be implemented in the frequency upconversion system. The self-started mode-locking would compete with the injection-triggered mode-locking, leading to the instability. To reduce the instability, self-started mode-locking was avoided in the slave fiber laser by properly adjusting the laser polarization state in the cavity. In such a critical position, the slave laser could only be mode-locked with the presence of the seed injection. The cavity length of the slave laser was adjusted to be the same as the master laser by carefully moving one of the fiber collimators on the stage. Eventually, the slave laser was synchronized with the master laser at the same repetition rate. And the stability of synchronization was enhanced by optimizing the master laser injection polarization. With such setup, we achieved the maximum cavity mismatch tolerance of 25 μm with the long-term stability of several hours.
Spectrum of the pump source from the EDFL (a) and the signal source from the YDFL (b), respectively. Autocorrelation pulse profiles of the pump source (c) and the signal source from (d), respectively. Solid symbols are the experimental data and solid curves are the Gaussian fits to the data.
The signal pulse duration was measured by an autocorrelator with the FWHM bandwidth of 12 ps [Fig. 4(c)], corresponding to the actual pulse duration of 6 ps by taking the denominator of 2. In order to optimize the upconversion efficiency, the pump pulse duration relative to the signal pulse needs to be considered carefully [5]. The total conversion efficiency dependent on the pulse overlapping is given by
where P0 is the probability of upconversion dependent on pump intensity IP as
The simulated conversion efficiency shown in Fig. 5 indicates that the total efficiency increases as the pump pulse duration goes longer by assuming a constant FWHM duration (6 ps) for the signal pulse. When the pump pulse duration is much shorter than the signal pulse, the conversion efficiency is quite low as most of the photons distribute outside of the pump pulse temporal window. On the other hand, too long pulse duration will lead to reduction of the energy utilization efficiency and increase of the background counts. Therefore, to achieve a conversion efficiency over 90% with experimentally available pump intensity, it is theoretically necessary to make pulse duration ratio between the pump and signal slightly larger than 1.2.
Simulated conversion efficiency as a function of the pulse duration ratio between the pump and signal. The red star indicates the experimental situation.
Thanks to the spectral filtering by the FBGs in the experiment, the pulse durations of the master and slave lasers were stretched according to the time-frequency Fourier transform. With the help of cavity dispersion management in the master laser cavity, the desired temporal match was achieved. The pump pulse duration was measured to be 8.8 ps [Fig. 4 (d)]. In this way, the signal photons and pump pulses were well matched temporally, guaranteeing an efficient upconversion with a relatively low background noise. According to the theoretical simulation, the conversion efficiency can reach 95% as shown by the red star in Fig. 5.
In the experiment, the timing jitter between the signal and pump pulses was measured to be as low as 45 fs shown in Fig. 6. Such a low timing jitter would impose a negligible influence on the temporal distribution of the signal photons within the pump pulse window. So the single-photon signal could be synchronously gated with a sufficiently high stability for coincidence upconversion detection.
As shown in Fig. 3, the signal beam and pump beam were then combine by a 1040/1557-nm WDM before being focused at the center of the 50-mm-long PPLN crystal. The temperature of the crystal was optimized at 130.4 °C for the grating period of 11.0 μm. The temperature was high enough to avoid photorefractive effects [36]. The infrared object beam interacted with the pump beam, and was upconverted through SFG to generate the visible photon at 624 nm.
For a single-photon frequency upconversion system, besides efficiency and stability, background noise was also a very important metrics for the detector performance. To analyze the noise, the spectrum before the filtering system was recorded. No observable peaks around the wavelength of the sum-frequency photons appeared when no signal photons were incident, revealing that the major noise from pump-induced parametric fluorescence was almost eliminated in our experiment. Considering the pulsed pump mode, the noise was localized within a very short pump time window which was much shorter than any electronic gates applied on the APD, leading to a very low noise counts on the APD.
Timing jitter power spectral density and the integrated timing jitter in Fourier domain.
Conversion efficiency as a function of the pump power. Inset: background counts vs the pump power.
As a result, the maximum detection efficiency of 31.2% was achieved at the pump power of 59.1 mW, with the corresponding background counts of 2.8×103 s−1 shown in Fig. 7. The maximum conversion efficiency of the system was calculated to be 91.8% after taking into account the transmittance of the filters and the quantum efficiency of the Si-APD SPCM. Since the signal and pump pulses were focusing on PPLN crystal, the imperfection of the conversion efficiency might be mainly caused by the spatial mode mismatching of the pump and signal. Compared with the background counts of CW pumping (1×105 s−1) [3,4], it was about two orders smaller in synchronously pulsed pumping scheme. As a figure of merit, we calculated the noise equivalent power (NEP = hυ(2RBC)1/2/η) divided by the operation rate f, where hυ, RBC and η are the energy of a signal photon, the background noise, and the detection efficiency, respectively. NEP/f was an important parameter of the sensitivity of an optical detector, especially referring to the determinant of the data acquisition time in general and the key generation rate in quantum key distribution (QKD) systems [23]. At the peak detection efficiency, the NEP/f was as low as 2.6 × 10-24 W/Hz3/2, thus showing that such scheme is suitable for the fast and efficient infrared single-photon detection.
The synchronous pumping single-photon frequency upconversion system is not only applied in the infrared photon detection with high efficiency and low background noise. More importantly, due to the unique quantum features in coherence preservation, it may stimulate promising applications with compact all-fiber devices in temporal and spectral control of single-photon nonlinear photonics. In Section 4, some applications are presented.
We believe that the compact fiber-laser synchronization system for fast and efficient single-photon frequency upconversion is of critical potential to stimulate other promising applications, such as high-speed QKD and quantum interface. The synchronous frequency upconversion system will also benefit infrared photon number resolving detection or few-photon-level infrared imaging.
PNRD supports promising and important applications in few-photon detection, nonclassical photon statistics measurements, fundamental quantum optics experiments, and practical quantum information processing [37–39]. In the visible regime, PNRD with high quantum efficiency and low dark counts could be realized by employing silicon-based multipixel photon counter (Si-MPPC) [40]. Meanwhile at telecom wavelengths, PNRD could be achieved with InGaAs-based avalanche photodiodes (APD) operating in non-saturated mode [41]. However, PNRD for the wavelengths around 1 μm is still a bottleneck because both Si- and InGaAs-detectors are insensitive at those wavelengths. Recent advances in infrared photon detection technology show that coherent upconverison of quantum states is feasible, where the photon statistics would be conserved consequently [10,11]. Therefore, it is possible to count the visible replicas via frequency upconversion to realize PNRDs around 1 μm.
Based on coincidence frequency upconversion presented in Section 3, photon-number-resolving detection at 1.04 μm has been realized. The experimental setup was schematically illustrated in Fig. 8, consisting of two parts: synchronization fiber laser system for the pump pulses and signal photons, and photon-number-resolved detection system based on coincidence frequency upconversion and the Si-MPPC. The signal photons at 1.04 μm were then upconverted by the synchronized pump pulses at 1.55 μm in the PPLN crystal. Then the SFG photons passed through a group of filters before impinging on the Si-MPPC with a multimode fiber pigtail. The Si-MPPC (Hamamatsu Photonics S10362-11-100U) was composed of 10×10 APDs which were arranged on an effective active area of 1 mm2 with a photon detection efficiency of 16.0% (including the fiber coupling efficiency). When the incident photons were injected onto different pixels, the output voltage of the superposition from all APD pixels was proportional to the number of incident photons [40]. The histogram of the peak voltage from the output of Si-MPPC was showed in Fig. 9(a). The pulse area spectrum featured a series of peaks representing the different photon number states. By fitting each peak to Gaussian function, the probability distribution was obtained as shown in Fig. 9(b). The area under each Gaussian curve gave the number of events presenting that photon number state. The area of each peak could be normalized by the total area to give the probability distribution. As the input light was in coherent state, the upconverted photons statistics obeyed the Poissionian law. By fitting the experimental data according to Poisson distribution, we got the detected photon number of 4.65. With different incident photon flux, the photon numbers detected by SPCM were shown with circles in Fig. 9(c). Since SPCM could not discriminate more than one photon per shot, it was obviously saturated with large incident photon numbers. In contrast, as shown with squares in Fig. 9(c), the detected photon numbers of Si-MPPC linearly increased with the incident photon numbers. It showed that the Si-MPPC could correctly identify the photon numbers per pulse with a large dynamic range, which was promising in few-photon-level detection.
Experimental setup for the 1.04 μm photon-number-resolved detection. DM, dichroic mirror; GP, Glan prism; Si-MPPC, silicon multipixel photon counter.
Color online) (a) Output voltage amplitude histogram for the upconverted photons. (b) Photon number distribution. (c) Detected photon numbers by SPCM (circles) and Si-MPPC (squares) as a function of incident photon numbers.
The photon-number-resolving performance was improved by reducing the background counts with a synchronous pump as the coincidence gate and reducing the intrinsic parametric fluorescence influence with long-wavelength pumping. As a result, a total detection efficiency of 3.7% was achieved with a quite low noise probability per pulse of 0.0002. Such a low background noise probability could remarkably improve the sensitivity of the frequency-upconversion PNRD. The remarkable decrease in background noise would optimize applications such as quantum entanglement and quantum teleportation [42] and improve the signal-to-noise ratio of widely used light detection and ranging system, since the background counts would randomly couple into the modes of the quantum states, which may significantly affect the original photon number distribution [38]. The approach may find promising applications in various quantum optical experiments using nonclassical light sources to demonstrate the features of quantum states around 1 μm [43].
Realization of ultra-sensitive infrared imaging has critical importance for applications such as astronomy, medical diagnosis, night-vision technology and chemical sensing. Currently, the infrared imaging detectors are available, like the commonly used linear InGaAs photodiode array. However, suffering from the severe dark current, the sensitivity of such detectors is largely limited [44]. Even though liquid-nitrogen-cooling can provide a solution for a smaller dark current, the additional cryogenic cooling device reduces the feasibility for lots of applications. Unlike in the infrared regime, imaging in the visible regime can be readily implemented by silicon charged coupled devices (CCDs) with high resolution and high efficiency. Moreover, recent progresses in sensor technology have led to the development of electron multiplying CCDs (EMCCDs) which is capable of single-photon detection. By leveraging the high sensitivity of EMCCDs, ultra-sensitive infrared imaging can be expected with nonlinear frequency upconversion of the infrared electric field to the visible spectral region [10,11]. Here we will introduce a few-photon-level two-dimensional infrared imaging detector by coincidence frequency upconversion. The upconversion imaging apparatus is shown in Fig. 10.
Schematic for infrared imaging by frequency upconversion.
In our experiment, the pump and signal sources were taken from two fiber lasers mode-locked at 19.1 MHz [33]. The signal beam illuminated a transmission mask with a character “C” to form the object beam shown in Fig. 11 (a). By a dichroic mirror, the object beam and the pump beam were then combined into a 4-f imaging system with lens L1 (f1=250 mm) and L2 (f2=300 mm). Fourier plane was arranged right at the middle of 50-mm-long PPLN crysta. In order to facilitate the type I quasi-phase matching of the PPLN crystal, a Glan prism was employed before the crystal for enforcing the polarization [45]. The temperature of the crystal was optimized at 104.3 °C for the grating period of 11.0 μm. The upconverted image at 622 nm was captured by a silicon EMCCD (Andor iXon3 897) bearing 512×512 pixels. The pixel size was 16×16 μm, which was very suitable for high spatial resolution imaging. To improve the signal-to-noise ratio, the EMCCD was thermoelectrically cooled to -85 °C.
With the pump power of 40.0 mW, corresponding to a peak power of about 210 W, the internal conversion efficiency of 27% could be inferred by correcting for the filtering transmittance. The object beam was attenuated to 2.0 photons per pulse. The upconverted imaging photons were then registered by the EMCCD with integration time of 30 s and accumulated for 50 times. The upconverted image was shown in Fig. 11 (c), from which the character “C” could be identified. Thanks to the pulsed pump field together with the long-wavelength pumping, the background noise was remarkably reduced. The theoretical calculation based on coherent imaging theory was given in Fig. 11 (b), which was in good agreement with the experimental result. The image blurring was attributed to the spatial filtering relating to the point spread function in the upconversion imaging system [7]. To improve the resolution, large pump beam profile at the Fourier plane would be better to enwrap the transformed object field as much as possible. However, increasing the diameter of the pump beam would decrease the intensity, thus reduce the conversion efficiency. Thus optimization of the trade-off should be in consideration.
a) The coherently illuminated mask. The theoretical (b) and measured (c) upconverted images at the image plane.
In summary, we demonstrated full 2D infrared image upconversion at few-photon level with a high conversion efficiency of 31%. The infrared image at 1040 nm was upconverted to the visible regime where the imaging photons were registered by the silicon EMCCD with high sensitivity and resolution. The imaging performance was remarkably improved with the reduction of the background noise by the synchronized pulsed excitation of the 1549 nm pump source, as well as the long-wavelength pumping [46]. Such all-optical upconversion imaging technique can offer an attractive method for ultra-sensitive infrared imaging.
In this review chapter, the quantum theory of the frequency upconversion is introduced and indicates that either in single-mode regime and multi-mode regime, complete quantum transduction can be realized in principle with two necessary requirement, sufficiently intensive pump field and perfect phase matching. Several upconversion systems with high conversion efficiency are presented and discussed in detail. Synchronous pumping frequency upconversion system shows superior performance with high conversion efficiency and low background noise. Thanks to the short time window of the synchronized pump pulse together with long-wavelength pumping scheme, the detection sensitivity was improved remarkably by reducing the background noise. This technique facilitates not only many traditional applications, such as classical optical communication, imaging, photobiology, and astronomy, but also novel quantum optics applications, such as quantum interface to transfer quantum entanglement, linear optical quantum gates, single photon polarization switches, and nonlinear control of single photons.
Information-psychological impact (IPI) is the informational influence on people’s minds, which alters their perception of the reality, behavioural functions and, in some cases, even the functioning of their inner organs and body systems [1, 2, 3]. Information-psychological impact (IPI) may affect individuals, groups of people, communities and the whole society. IPI can be either positive or negative, depending on the intended purpose. Positive IPI is used for medical treatment purposes, rehabilitation, improvement of behavioural patterns and creative purposes. It can also be used to unite people for a good cause. Negative IPI is used for manipulating—directly or indirectly—individuals, groups of people or the whole society into actions that violate either their own interests or interests of others. Negative IPI may cause emotional, psychological and social tensions, deterioration of moral standards and behavioural norms, as well as moral and political disorientation. This, in turn, leads to dramatic changes in individual, group and public conscience and alterations in the moral, political, social and psychological environment within the society [1, 2, 3].
\nInformation-psychological impact is implemented by means of various tools and techniques. At the moment, negative information-psychological impacts are more common. They influence individuals, groups of people or the society by means of telecommunication systems, mass media and social networks. Negative IPIs are used to control the society, force certain opinions on various issues, recruit members to religious cults and terrorist groups and to alter people’s mental state. Among the examples of such IPIs are colour revolutions, the so-called “death groups” on social networking sites, as well as active recruitment campaigns to terrorist groups, which are based on films or video games aimed primarily at young people.
\nIt is thus very important to model IPIs in social networks in order to analyse and select the most effective methods of using positive IPIs and combating negative IPIs [1, 2, 4].
\nSocial networks are usually represented as graphs with multiple vertices (agents) and edges representing the links between the agents. Agents represent various subjects of the network, from individuals to large groups, organisations and communities. Links denote the relationships between the agents, such as information exchange, social relations and communication [4, 5, 6, 7, 8, 9]. The process of IPI can be divided into two stages: diffusion of the IPI and alteration of the agents’ opinions. Gubanov et al. [4] consider various models of social networks and divide the tasks connected with studying IPI into following groups: modelling of the informational influence, modelling of the information management and modelling of the information confrontation.
\nModels of the informational influence are used to study the behaviour of the subject affected by IPI. The influence may be intentional or unintentional. Social influence becomes obvious during communication or in case of comparison. Models of the informational influence are used for information management, as they help the managing subject to determine the kind of informational influence that will make the controlled subject behave in the desired way. The information management model, in turn, is used to model information confrontation, that is, the interaction of several subjects with conflicting interests who apply their informational influence to the same controlled subject [4]. There are a number of approaches to modelling the influence.
The Independent Cascade and Linear Threshold Models [4, 8, 10, 11, 12, 13]. In these models, the subject (a vertice of the graph) can be either active or inactive. The state may only change from active to inactive, not the other way round. The agent becomes active depending on the selected threshold. The threshold can be uniform for all agents or may be randomly selected according to a probabilistic distribution. These models do not take into account groups, game interaction between the subjects, individual activity of the subjects or incomplete awareness of the subjects.
Network autocorrelation models. In these models, the opinion and behaviour of the subject are affected by the opinion of the neighbouring subjects and represent the reaction of the subject to the IPI. The authors [14, 15, 16, 17, 18, 19] consider a determined time-digital linear process, where opinions (properties) of the subject are presented as vector \n
Ising models [20, 21]. Invented for studying the phenomenon of ferromagnetism, Ising model is often used to verify the results of numerical modelling. When studying the diffusion of IPI in social networks, the model helps to describe the changes in the behaviour of a large social group caused by the nearest neighbours. The influence of the nearest neighbours plays a key role, and the willingness of the group to accept a new idea serves as the analogue of the temperature.
Influence models based on Markov chains. Such models employ corresponding mathematical tools to represent the activities of every subject and the group as a whole. They are used to analyse social dynamics and determine the patterns of the group behaviour. The authors [22, 23, 24] consider the similarity of opinions of the subject, the authors [7, 24] focus on the time over which the opinions become similar, and the authors [7, 25] study the conditions under which a uniform final opinion is formed.
All the above-mentioned models represent the rules of interaction between the subjects or groups of subjects. However, they either do not at all represent the specifics and characteristics of the network influence and the interaction process or do this inadequately.
\nWhen a social network is considered as a set of agents [4, 26, 27, 28], we assume that every agent has a certain degree of influence on the other agents. It is therefore necessary to determine a small group of agents with the maximal level of influence, that is, to solve the influence maximisation problem [4, 10, 29]. These agents can be used as key nods for influencing other subjects of the social network or to monitor the social network in order to reveal the presence of IPI. The influence maximisation problem has been considered in papers focusing on the following issues.
Viral marketing [29], where a social network is represented by a Markov chain with each agent A having his own value that depends on the profit from sales to other agents influenced by agent A.
Influence maximisation in the models of innovations’ diffusion [10]. They include a set of active agents, and at a certain point in time, a new active agent can activate his neighbours with a set probability.
Voting process modelling [9], where every agent can, at any stage, change his opinion by accidentally voting for one of his neighbours and adapting their opinion. The agent is more likely to adapt the opinion supported by the majority of his neighbours.
Besides analysing the influence, management and confrontation, there is also a problem of diffusion of information-psychological impact in the information space [5]. Information may spread in the following directions [5, 30]: from a subject to another subject, from a subject to a group, from the information production centre to an individual subject or a group.
\nThe authors [5, 26, 27, 28, 31] suggest a multi-agent model of information diffusion. The model takes into account the growth of the number of agents over time. Agents may appear themselves, produce new agents, disappear from the subjects’ neighbourhood or receive links from other agents.
\nIn [5, 30], the life cycle of the information flow is represented by information diffusion models based on cellular automata. In these models, each cell of the automaton can have various states, such as “influence taken,” “influence not relevant,” or “influence rejected.” The information spreads according to probabilistic rules. The observed states of the objects alter simultaneously in discrete time intervals following the constant local probabilistic rules. The rules themselves depend on the state of variables describing the nearest neighbours of the agent or on the state of the subject itself. For instance, the authors [8, 32] present a model of word-of-mouth information transfer considering strong and weak links between the subjects.
\nIn order to analyse the information diffusion process, the authors [6, 33, 34] compare information diffusion to virus transmission using infiltration and contamination models such as SIR model and SIRS model.
\nRunkov [35] compares the structure of social networks and neural networks. Individual users are viewed as neurons. Using the information about the users’ activities, the neural network may forecast the kind of news they will be interested in [35, 36] also suggests using neural networks to forecast the behaviour of the subject of IPI and their recruitability to certain assignments, as well as to assess their reliability using the data available in the social network.
\nFrom the information security perspective, it is vital to identify IPI as soon as possible. For this purpose, the authors [4, 37] suggest monitoring the states of a small group of nods in the network using graph models. The problem is to determine the set of nods to be monitored. Deviations from the standard dynamics of transmission of some information messages may serve as an indicator of information-psychological impact. In order to analyse the dynamics of the information spread and determine the channels caused by external factors, wavelet analysis can be used [5, 15, 17].
\nDodonov and Lande [5] introduce the term information reservation for an isolated area of the information space and suggest certain modification to information diffusion models in order to model the dynamics of information flows in information reservations. Information reservations are information areas subject to constant information-psychological impact. They can be used for information and psychological control over the society.
\nWe should say, however, that all the suggested models do not fully consider social and psychological factors, such as the psychological state of the subjects during IPI diffusion in social networks. IPI diffusion process depends on the probabilistic characteristics of the subjects of the social network and the links between them. It is, therefore, interesting to study IPI diffusion taking into account social and psychological factors and the psychological state of the subjects of the social network.
\nThe aim of this paper is to model the process of IPI diffusion in social networks considering social and psychological factors and the psychological state of the subjects of the social network. This can be done using a cellular automaton model, as cellular automata can most adequately represent the process of IPI diffusion in a social network and the changes in the opinions of its subjects caused by their immediate neighbours, taking into account social and psychological factors.
\nWhen modelling and analysing the process of IPI diffusion, we regarded the social network as a two-dimensional cellular automaton. A two-dimensional cellular automaton is a set of finite automata (subjects of the social network) allocated on the reference frame and marked with integer coordinates \n
where \n
In the cellular automaton model, each cell changes its state while interacting with a limited number of other cells, normally adjacent ones with the same edge or vertex. Such models allow for a simultaneous change of the state of all cells following the general principle of the cellular automaton. Therefore, it is easy to see the connection between the processes occurring on the micro level and the processes of spatial interaction between the elements.
\nDue to the simplicity of their implementation and the ability to describe complex processes, cellular automata are widely used for the modelling of systems, which consist of a large number of nonlineary interacting particles (fluid and gas dynamics in various environments, fires, traffic, and so on), as well as for representing collective phenomena, such as turbulence, arrangement and chaos.
\nGiven below are the models we suggest for describing the process of information-psychological impact diffusion in social networks.
Information interaction within the social network is presented as a two-dimensional cellular automaton, whose grid is a two-dimensional array, where each cell is numbered with an ordered pair \n
The informational interaction in the social network is presented as a cellular automaton, whose grid is a free-scale network generated by a Barabási-Albert algorithm.
Each cell may be in one of the following states: highly positive, neutral (mild negative or positive attitude) or highly negative. Depending on its state and social and psychological characteristics, a cell may or may not spread the information (by influencing the neighbouring cells) [5, 30, 38]. The state and behaviour of cells change according to the set of rules for the suggested model. These rules take into account social and psychological factors as well as the psychological state of the subjects of the social network.
\nA state transition graph is presented in Figure 1. \n
State transition graph.
Each subject \n
Initial personal opinion \n
[−1; −0.64) interval—highly negative opinion that motivates the subject to spread the information \n
[−0.64; 0) interval—mild negative opinion that does not motivate the subject to spread the information \n
[0; 0.64) interval—mild positive opinion that does not motivate the subject to spread the information \n
[0.64; 1) interval—highly positive opinion that motivates the subject to spread the information \n
Level of trust \n
Communication skills \n
Information transfer coefficient \n
Level of perception \n
In order to evaluate the current (at a specific time interval \n
where \n
Whether subject \n
The subject affected by the IPI in the social network develops his own opinion about the received information, which depends on his individual parameters and the force of the IPI. The opinion can be positive or negative and may change over time under the influence of other factors. Depending on his opinion about the information and his communication skills, the subject may or may not spread the received IPI [43, 44, 45, 46].
\nThe effectiveness of the IPI can be defined by the following relation Eq. (4):
\nwhere \n
Users of the social network may be subject to various kinds of IPI aimed at different groups of people. IPIs may also differ by their purpose and the effectiveness of implementation. IPI in social networks may also be used to influence specific public officers.
\nUsing the results of the IPI modelling, we can perform a comprehensive assessment of the general level of information and psychological security and suggest practical recommendations on how to eliminate the negative effect of the information-psychological influence. The assessment can be based on the methodology for calculating the security indices in the military, political, economic and other spheres developed by the PIR Center [48, 49]. This means that the index of general information and psychological security (IGIPS) is calculated according to the following formula:
\nwhere \n
\n\n
where \n
The probability of the IPI being aimed at a specific public officer is calculated using Eq. (7) [2]:
\nwhere \n
The suggested method of assessing the IGIPS has the following advantages. It registers the increase in the degree of the IPI on the social network in good time. It registers the connection between the IPI on public officials and the decisions they make. It allows for calculating the index of information and psychological security and developing a strategy to decrease negative IPIs.
\n\nFigure 2 presents a flow chart of the algorithm for modelling IPI. During the initial stage, main parameters of the social network’s subjects are determined. The trust matrix is formed, and the communication skills of the subjects, their perception level, information transfer coefficient and the initial opinion about the given issue are determined [43, 44, 45, 46, 47].
\nFlow chart of the algorithm for modelling IPI.
During the first stage, which corresponds to the origin on the time axis \n
The second stage involves information diffusion and exchange of opinions between the subjects along the time axis \n
A cell may change its state receiving influence \n
The suggested algorithm was implemented on a 100 × 100 grid. The automaton was tested in the following way: the initial values were distributed following the normal distribution law; 10 random initiators of the IPI and 2 opponents were selected out of all the subjects; the automaton was tested 100 times, each test run including 1000 steps; average number of subjects in each of the states was determined. The initial personal opinion of subject \n
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n\n−\n0\n\n5\n0\n5\n\n\n.
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n\n−\n1\n\n\n−\n0\n\n5\n\n\n.
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n0\n5\n1\n\n\n.
\nFigure 4 demonstrates the functioning of the automaton, when \n
Random scale-free network generated by Barabási-Albert model.
The suggested algorithm was implemented using a random scale-free network generated by Barabási-Albert algorithm. The network consisted of 1000 nods. The results are given in Figure 6. The automaton was tested in the following way: the initial values were distributed following the normal distribution law; 90 random initiators of the IPI and 10 opponents were selected out of all the subjects; the automaton was tested 100 times, each test run including 300 steps; average number of subjects in each of the states was determined. The initial personal opinion of the subject \n
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n\n−\n0\n\n5\n0\n5\n\n\n.
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n\n−\n1\n\n\n−\n0\n\n5\n\n\n.
Distribution of cells according to the discrete time whenever \n\n\nV\nk\n\n∈\n\n0\n5\n1\n\n\n.
\nFigure 7 demonstrates the functioning of the automaton, when \n
Analysis of Figures 3–9 shows that
the character of the IPI diffusion within the social network is practically exponential;
when the subjects are neutral to the IPI (Figures 3a and 7a), just a small number of initiators can successfully perform the IPI;
when the subjects are negative or positive to the IPI (Figures 4a, 5a, 8a, and 9a), the IPI does not influence their state;
when the subjects do not trust each other and change their opinions to the opposite ones (Figures 3–5b and 7–9b), the number of subjects in states S3 and S4 is similar, irrespective of their initial state.
The results obtained using the suggested models agree with the results presented in Refs. [4, 5, 30]. These works consider the information diffusion, which is an individual case of IPI diffusion in social networks. As opposed to Refs. [4, 5, 30, 39, 40], the suggested model is not based on the probabilistic characteristics of the subjects of the social network but takes into account the social and psychological parameters of the subjects and their psychological state during IPI diffusion in social networks.
\nThe paper suggests a model for describing the diffusion process of information-psychological impact in social networks based on cellular automata. Cellular automata models can change the states of a large number of cells over a minimal period of time, which is very useful for the modelling of the process of information-psychological impact diffusion in social networks. The suggested models can thus represent the process of IPI diffusion in a social network and the corresponding changes in the opinions of its subjects caused by their immediate neighbours, taking into account social and psychological factors.
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