Moreover, Deruelle et al. (2004) found that children with ASD exhibited better performance compared to a control group when using high rather than low spatialfrequency (LSF) in an identity-matching task. Previous studies (e.g. Shulman & Wilson, 1987) demonstrated that LSF images conveyed more configural features than local ones, whereas the local features are primarily conveyed by high spatialfrequency (HSF). When applied to face recognition, the low-pass filter makes facial features vague, whereas when faces are high-pass filtered, facial features appears to be emphasized. Deruelle et al. (2004) explained that fine details of facial features (i.e., local cues) are available when the stimulus contains HSFs but not when it contains only LSFs. Thus, the result indicates that children with ASD relied more on local (HSF) cues than on configural (LSF) cues when processing faces. This local advantage in face processing in children with ASD was discussed in line with the WCC theory (for an updated review, see Happé & Frith, 2006) and the EPF model (for an updated review, see Mottron et al., 2006). Deruelle et al. (2004) suggested that the local bias observed in children with ASD could appear in the early information processing like spatialfrequency decoding. Superior processing of face parts may explain a preference for HSFs in matching faces. Individuals with ASD might become more dependent on local cues and thereby disadvantaged when local cues are reduced (Lahaie et al., 2006).
We introduce a realistic frequency-dependent channel model for ultra-wideband (UWB) communication systems and develop a generalized broadband Capon spatialspectrum estimator for localization of multiple incoherently distributed scattering clusters. The proposed estimator is able to address the three crucial features of practical UWB impulse propagation: presence of local scattering for multiple incoherently distributed clusters, wideband array signals, and frequency-dependent dispersive eﬀects. The particle-swarm optimization, which is a recently invented high-performance optimizer based on the movement and intelligence of swarms, is then implemented to perform a multidimensional parameter search to jointly estimate the source central angles, the polynomial regression coeﬃcients for angle spreads, and the frequency-dependence of various clusters. Numerical experiments are also carried out to examine the performance of the algorithm under various environments and model mismatches.
The experiment has been performed with a tempera- ture tuned 981 nm VCSEL of 200 µm circular aperture and a volume Bragg grating (VBG) with a single reflec- tion peak at 981.1 nm, a reflection bandwidth of 0.2 nm full-width at half-maximum (FWHM) and a peak reflec- tivity of 99% . The external cavity for the frequency- selective feedback is arranged in a self-imaging configura- tion that maintains the high Fresnel number of the VC- SEL cavity and ensures local feedback compatible with self-localization (see Fig. 3). Small deviations from the self-imaging condition are not critical for the reported phenomena. The detection system comprises two charge- coupled-device cameras for near- and far-field imaging, and a scanning Fabry-Perot interferometer with a 10 GHz free spectral range to measure the optical spectrum. Sev- eral LCS appear at certain spatial locations defined by the traps when increasing the VCSEL injection current and display hysteretic behavior when decreasing the cur- rent again. The experiment described below is performed at a bias current at which both LCS involved are indi-
However, the IDE model discussed in ,  has a disadvantage in that the spatial mixing kernel used to describe correlations over space assumes homogeneity: all points in space are described by the same mixing kernel - an assumption that may be limiting in some circumstances. One advantage of the STARMAX model, in this regard, is that it allows for heterogeneity in the spatial correlations, whilst being amenable to data-driven identification. Therefore, this leads us to the conclusion that the three broad approaches to spatiotemporal modelling described above (STARMAX, basis function de- composition of the spatial field and data-driven identification) have attributes that have not yet been distilled into a single, powerful framework for system identification that incorporates the following: (i) decoupled number of sensor observations from model order; (ii) continuity-in-space; (iii) a heterogenous representation; (iv) data-driven methods for the identification of process dynamics. Deriving such a framework is the aim of this paper.
Wideband spectrum sensing for ﬁxed spectrum allocation The classical algorithms reconstruct the commonly sparse signal. However, in the coarse wideband spectrum sens- ing, the boundaries between diﬀerent kinds of primary users are ﬁxed due to the static frequency allocation of primary radios. For example, the bands 1710–1755 MHz and 1805–1850 MHz are allocated to GSM1800. Previous CWSS algorithms did not take advantage of the infor- mation of ﬁxed frequency allocation boundaries. Besides, according to the practical measurement, though the spec- trum vector is sparse globally, in some certain allocated frequency sections, they are not always sparse locally. For example, in a certain time and area, the frequency sections 1626.5–1646.5 MHz and 1525.0–1545.0 MHz allocated to international maritime satellite are not used, but the fre- quency sections allocated to GSM1800 are fully occupied. The wideband FRV is not only sparse, but also in sparse cluster distribution with diﬀerent length of clusters. It is the generalization of the so called block-sparsity [35,36]. This feature is extremely vivid in the situation that most of the monitored primary signals are spread spectrum signals.
As shown in Fig. 3, the ultraviolet bandwidth of the electromagnetic spectrum is divided into three regions: the Near Ultraviolet (NUV), the Far Ultraviolet (FUV), and the Extreme Ultraviolet (EUV). The regions are sometimes designated as A, B, and C. The three regions are distinguished by the energy level of the ultraviolet radiation and the "wavelength" of the ultraviolet light, which is related to energy. The NUV region is closest to optical or visible light band. EUV is closest to X- rays and is the most energetic of the three types. The FUV region lies between the near and extreme ultraviolet regions. The flames of missiles have the characteristics of UV light, and they are of the wavelength of 220nm~280nm in the "solar blind wave-band". The solar blind UV Intensifier Charge Couple Devise (ICCD) can detect the missiles and realize imaging. Because the UV targets be detected are weak signals mostly, need to use amplifiers to intense them, the weak target signal and noise are inevitably amplified.
In this section, the proposed truthful spectrum auction with a novel allocation mechanism is discussed in detail. We first formulate the winners determination problem as a 0/1 knapsack problem. And we modified the genetic algorithms to adapt to the spectrum auction we proposed. After that, a pricing scheme like Second-Price Auction is designed to ensure that all SUs can have a non-negative utility only by their real information.
Modern radar in situ testing requires that the test system has a full range of functions, measurement speed, high precision, easy to carry and other characteristics, the use of PXI bus-based automatic test equipment can be a good application of the above needs. According to the general needs of airborne radar testing, the need for RF signal spectrum, pulse envelope and waveform, transmit power, transmit spectrum and receive channel gain test. In the design of automated test system, the general will choose the corresponding function of the PXI hardware module, including VSA, high- speed digitizer, microwave power meter and other instrument modules, and then matching PXI chassis and zero slot controller, mouse and keyboard kit, you can set up to meet the needs of the radar test in situ test system. The VSA is responsible for measuring the spectral parameters of the signal. If the radar signal power and waveform parameters measured by the VSA, instead of the high-speed digitizer, RF power meter and other instrument modules, and the radar in situ test system will become smaller, more conducive to the field or ship carrying.
such as communication, confusion or ‘ dazzle ’ , these are not specifically investigated in this study. As outlined in Fig. 1A, fish with a similar pattern to the background, whether that background is plain (Fig. 1Ai) or patterned (Fig. 1Aiii), are more likely to be cryptic from the perspective of a predator, compared with if a pattern is highly contrasting with the background (Fig. 1Aii). To understand the design and success of various camouflage strategies, we must consider how colour patterns are viewed by relevant signal receivers (Endler, 1983). Therefore, we first measured the visual acuity of two reef fish predators using information on the anatomy of their eyes and the density of photoreceptors in the area of the eye most likely used for focussing a clear image (Collin and Pettigrew, 1989; Ullmann et al., 2012). This information was combined to apply relevant blurring to images used in behavioural assays and natural scenes, so that they represented a predator ’ s-eye-view of a scene. Next, we used behavioural experiments with the same two predatory fish species to investigate whether there is a reduction in the likelihood of attack for humbugs when viewed against backgrounds of similar and mismatched spatial frequencies (number of within- pattern elements), measured using fast Fourier transform (FFT) analysis [similar to previous methods (Cortesi et al., 2015b)]. Finally, we assessed field images from the Great Barrier Reef to quantify the spatialfrequency of humbug damselfish against natural coral backgrounds. We discuss the implications of our findings in relation to disruptive contrast strategies in both marine and terrestrial predator – prey relationships.
χ is a probability ranging between 0 (independent copula) and 1 (comonotonicity copula). As an aside, note that the co- efficient of tail dependence of the Gaussian copula is always 0 regardless of the value of the correlation coefficient, hence, the Gauss copula is asymptotically independent in both tails. The estimated χ values for the pairs Amsterdam-London and Paris-London, shown in Fig. 3, are 0.67 and 0.31, re- spectively. In other words, if an extreme peak gust from an extra-tropical cyclone is observed in London, there is a probability of 0.67 that an extreme peak gust value will be recorded in Amsterdam, and only a 0.31 probability that this would happen in Paris. The spatial structure of tail depen- dence is of obvious importance for re/insurance applications. The greater the spatial extent of this dependence in the do- main, the greater the aggregated damaging hazard from indi- vidual storm events, which will tend to produce thicker tails of storm risk distributions.
Abstract. According to the different sensitivity of human eyes to spatialfrequency,building the calculation methods of spatialfrequency and the character of contrast sensitivity, which comes up with a method that can test the influence of spatialfrequency change on image quality and it can be proved by experiment. The method is easy to operate and it can reflect more accurately about the visual perception of human eyes to image quality. The experiment results turns out that the contrast sensitivity is falling to high and low end human eyes.
Mobile stations are the mobile phones that are connected to the network through BTS through air interface. It has a transceiver through which it connects to the network. The past couple of decades the wireless communication has experienced a phenomenal growth and it has become an integral part of the society. The number of users and the services provide d and also its quality has been tremendously increased. The mobile wireless communication has different generations with some new advancements. Paper (1) and paper (2) gives the over view of different generations in development of mobile wireless technologies from 1G to 4G. Paper (2) discusses the disadvantages and advantages of one generation over the other and hence the evolution of wireless technologies. The GSM technology utilizes the bandwidth which lies in the range of 815-930MHz for uplink and 935-960MHz for downlink. Many methods are proposed in various papers to optimize the spectrum consumption and improve the performance of the network. The composite multisite is used increase the coverage where the antennas at different locations are merged into single cell as nodes by eliminating the inter-cell handovers. This increases the coverage of GSM network and network performance (4). The frequency reuse and cell splitting methods, & cell pattern (5) are used to increases the spectrum efficiency. When the frequency is reused co-channel interference and the adjacent channel interference are introduced which can be identified by Absolute Radio Frequency Channel Number (AFRCN). A channel with high level of frequency has to reassigned (6). The interference can also be reduced by Maximum Likelihood Sequence estimation followed by successive interference cancellation (7). The receiver decodes the strongest signal and extract the weaker signal among them which reduces the channel interference (8). Radio frequency hopping and Baseband hopping are also used to control the level of interference by frequency reuse.
benefits for usersaswell as network providers. Users get more predictable performance,because the interference is managed, and an extendedbattery life, because fine- grained synchronization enables lowduty-cycle operation. (FlashLinQ is designed to leverage anyof CDMA/GSM cellular timing , DVBH timing , GPStiming , along with in-band timing.) In addition, networkproviders get increased spectral and power efficiency. As anaside, we note that FlashLinQ can be deployed in a mixedlicensed-unlicensed spectrum configuration; indeed, licensedspectrum communication, since it is inherently more reliable, can be used as a control layer in which, for example, peersdiscover each other and negotiate the use of unlicensed spectrumfor bulk data traffic.
processing. The ease and speed of programming baseband operations in a SDR makes this technology a prime candidate for DSA networks. SDR technology advances the field of communications with respect to rapid prototyping, testing and deployment of new radio hardware and communications systems. New modulation schemes or coding techniques can be rapidly implemented and tested without building expensive custom hardware. SDR system software can be designed to interface with communications system design programs, thus enabling designers to rapidly move from simulation to implementation. Around 2000, Mitola proposed extending the capability of an SDR by adding the notions of environment sensing and artificial intelligence . These SDRs with intelligence, termed Cognitive Radios (CR), are capable of rapidly reconfiguring operating parameters due to changing requirements and conditions at the physical, network, and/or application layers of the system . These capabilities make it possible for cognitive radio systems and networks to flexibly and dynamically access the spectrum while simultaneously respecting the rights of incumbent license holders. Systems have been proposed that use various expert systems and rule engines , genetic algorithms , or ontology-based reasoning and inference engines . These systems and algorithms aim to provide the CR with the knowledge necessary to assemble a strategy for dealing with the current RF environment, as well as primary and secondary users. Cognitive Radios can operate in self-forming networks that collaboratively sense the
covers both the 2nd and the 3rd harmonics ranges (below we analyse only the 2nd harmonic range). The lower fre- quency limit (2nd harmonic) is chosen to cover the down- shifted emission from the supra-thermal electrons for both HFS and LFS cases. The beam collected by the ECE an- tenna is modeled by a bundle of rays, the intensity of the beam is distributed among the rays according to a Gaus- sian antenna pattern. Following Eq.(3), for each frequency, ω = 2 π f , the results are averaged over the f ± ∆ f / 2 range, where ∆ f is the band width of the corresponding channel in the radiometer (here ∆ f = 0 . 3 GHz and the frequency band function has been assumed rectangular, ω = (2 π∆ f ) − 1 ). For comparison, in Fig. 3 both the LFS
The PSF can be termed as the spatial distribution of optical intensity at the receiving detector for a trans- mitted spatial optical impulse [21, 34]. For the case of an ideal imaging system, the PSF has a distribution of a 2-D Dirac delta function. For the case of a practical imaging system with appropriate focusing, the PSF usually has the distribution of a narrow spatial pulse whose dimensions are mainly a function of the aber- rations of the imaging lens and the diffraction phenomenon at the receiver aperture . In a prac- tical pixelated system, both defocus and motion blur are likely to be present. Defocus is the lack of focus of the imaging lens, while motion blur is caused by the relative motion between the transmitter and the receiver during the exposure time of the imaging sys- tem. These two blurs together cause the received in- tensity pattern to be distributed over a larger space, resulting in a degraded PSF. Figure 4 shows the case of defocus-degraded PSF whose elements can be rep- resented as follows (for simplicity, a 3 × 3 case is shown):