Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper , we present the system design approach to meet this challenge with technique , The design space is diverse as it involves various types of primary user signals, We analysis of signal processing approaches and identify the regimes where these techniques are applicable. The goal of this paper is to present a practical system design view of spectrum sensing functionality.
Abstract
The Phenomenon of Extremism of Values (Maximum or Rare Value) an important phenomenon is the use of two techniques of sampling techniques to deal with this Extremism: the technique of the peak sample and the maximum annual sampling technique (AM) (Extreme values, Gumbel) for sample (AM) and (general Pareto, exponential) distribution of the POT sample. The cross-entropy algorithm was applied in two of its methods to the first estimate using the statistical order and the second using the statistical order and likelihood ratio. The third method is proposed by the researcher. The MSE comparison coefficient of the estimated parameters and the probability density function for each of the distributions were
... Show MoreAutomatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robu
... Show MoreDeveloping smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, three-dimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects,
... Show MoreThin films of iridium doped indium oxide (In2O3:Eu)with different doping ratio(0,3,5,7,and 9%) are prepared on glass and single crystal silicon wafer substrates using spray pyrolysis method. The goal of this research is to investigate the effect of doping ratio on of the structural, optical and sensing properties . The structure of the prepared thin films was characterized at room temperature using X-ray diffraction. The results showed that all the undoped and doped (In2O3:Eu)samples are polycrystalline in structure and nearly stoichiometric. UV-visible spectrophotometer in the wavelength range (200-1100nm)was used to determine the optical energy gap and optical constants. The optical transmittance of 83% and the optical band gap of 5.2eV
... Show MoreThin films of bulk heterojunction blend Ni-Phthalocyanine
Tetrasulfonic acid tetrasodium salt and dpoly
(3, 4-ethylenedioxythiophene) poly (styrenesulfonate) (NiPcTs:
PEDOT: PSS) with different (PEDOT:PSS) concentrations (0.5, 1, 2)
are prepared using spin coating technique with thickness 100 nm on
glass and Si substrate. The X-Ray diffraction pattern of NiPcTs
powder was studied and compared with NiPc powder, the pattern
showed that the structure is a polycrystalline with monoclinic phase.
XRD analysis of as-deposited (NiPcTs/PEDOT:PSS) thin films
blends in dicated that the film appeared at(100), (102) in
concentrations (0.5, 1) and (100) in concentration (2). The grain size
is increased with increasing
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi
... Show MoreFinding orthogonal matrices in different sizes is very complex and important because it can be used in different applications like image processing and communications (eg CDMA and OFDM). In this paper we introduce a new method to find orthogonal matrices by using tensor products between two or more orthogonal matrices of real and imaginary numbers with applying it in images and communication signals processing. The output matrices will be orthogonal matrices too and the processing by our new method is very easy compared to other classical methods those use basic proofs. The results are normal and acceptable in communication signals and images but it needs more research works.
Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices usin