Dynamic control approaches of spectrum sensing in multi-band cognitive radio networks

In this paper,the dynamic control approaches for spectrum sensing are proposed,based on the theory that prediction is synonymous with data compression in computational learning. Firstly,a spectrum sensing sequence prediction scheme is proposed to reduce the spectrum sensing time and improve the throughput of secondary users. We use Ziv-Lempel data compression algorithm to design the prediction scheme,where spectrum band usage history is utilized. In addition,an iterative algorithm to find out the optimal number of spectrum bands allowed to sense is proposed,with the aim of maximizing the expected net reward of each secondary user in each time slot. Finally,extensive simulation results are shown to demonstrate the effectiveness of the proposed dynamic control approaches of spectrum sensing.     

A new chain-coding algorithm for binary images using run-length codes

This paper presents a new algorithm for converting a binary image into chain codes using its run-length codes. The basic idea of conventional chain-coding algorithm is to follow boundary pixels by convolving a 3 × 3 window with the image and to sequentially generate chain codes. The proposed algorithm has two phases, namely run-length coding and chain-code generation. We use connectivity information between runs as well as their coordinates in the phase of run-length coding. In the second phase (chain-code generation) the connectivity information extracted in the first phase is utilized for sequentially tracking runs containing the boundary pixels to be followed. This algorithm has an advantage that we can detect easily the inclusion relationship between boundaries at the same time as chain-code generation.     

Partitioned encryption and achieving simultaneity by partitioning

We present a method called Partitioned Encryption whose main property is its simplicity. It is an extension of Probabilistic Public-Key Encryption, which can be used in designing cryptographic protocols and can be applied to distributed problem solving. We also give a modification of Secret Sharing called Partitioned Secret Sharing. We demonstrate the power of Partitioned Encryption: combining it with the partitioning of the user set gives a solution scheme for ‘Verifiable Secret Sharing’ and ‘Simultaneous Broadcast in the Presence of Faults’, which are important primitives of fault-tolerant distributed computing introduced by Chor, Goldwasser, Micali and Awerbuch (1985). The scheme is fully polynomial, simple, and efficient in terms of communication rounds. The basic partitioning methods are suggested as general tools for distributed computing, which are easy to implement and analyze.     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

Texture anisotropy technique in brain degenerative diseases

Structural alterations anisotropy-based measured for different areas for the most common types of dementia diseases could be a biomarker of brain impairment. The current work aims to assess whether texture anisotropy can discriminate both healthy versus Alzheimer’s and Pick’s patients based on regional evaluation while maintaining high predictive power. The investigated area is reduced from the whole-brain surface to three major lobes (i.e., frontal, temporal and parietal). A predictive model was proposed to associate a disease with a specific area in the brain based on the anisotropy values. Simultaneous analysis of 1680 measurements from 105 brain magnetic resonance images acquired as T2w and PD sequences was performed to establish the significance of the model. The cerebral calcinosis disease has been used as artificial ground truth. The association based on textural anisotropy between targeted diseases and control patients was performed by using Pearson’s correlation coefficients. A new proposed consistency index investigated the texture anisotropy relevance for all image’s types and all analyzed classes and regions. The validation study is based on area under the receiver-operating characteristic curve that depicted the overall diagnostic performance of the texture anisotropy in each region. The proposed model demonstrated that texture anisotropy is accurate solution in diagnosis of Alzheimer’s and Pick’s diseases when the investigated area is reduced to major lobes, with sensitivity >90% and specificity >80%.

     

彩色图像的稀疏分解

图像稀疏分解是基于过完备原子库的一种图像表示方法,主要在灰度图像中应用.本文在分析灰度图像稀疏分解的基础之上,将其应用到彩色图像处理之中,实现了在RGB和YUV两种颜色模型下的匹配追踪稀疏分解.实验表明,YUV颜色模型更适合低码率彩色图像稀疏分解.     

An online method to remove chattering and repeating alarms based on alarm durations and intervals

Chattering and repeating alarms, which repeatedly make transitions between alarm and non-alarm states without operators’ response, are the most common form of nuisance alarms encountered in industrial plants. The paper formulates two novel rules to detect chattering alarms caused by random noise and repeating alarms by regular patterns such as oscillation, and proposes an online method to effectively remove chattering and repeating alarms via the m-sample delay timer. Industrial examples are provided to support the formulated rules and to illustrate the effectiveness of the proposed method.     

Synthesis,lattice energy and microwave dielectric properties of BaCu2-xCoxSi2O7 ceramics

BaCu_2-xCo_xSi₂O₇ solid solutions with orthorhombic structure (Pnma) were prepared by solid-state reaction method. The phase synthesis process, structural evolution and microwave dielectric properties of BaCu_2-xCo_xSi₂O₇ ceramics were investigated. Single BaCu₂Si₂O₇ phase was obtained when calcined at 950 °C for 3 h and was decomposed into BaCuSi₂O₆ phase when calcined at 1075 °C for 3 h. The sintering process was effectively promoted when Cu²⁺ was replaced by Co²⁺ and the maximum solubility of BaCu_2-xCo_xSi₂O₇ was located between 0.15 and 0.20. P-V-L complex chemical bond theory and Raman spectra were used to explain the structure-property correlations of BaCu_2-xCo_xSi₂O₇ ceramics. The corrected dielectric constant (ε_r-corr) of BaCu_2-xCo_xSi₂O₇ ceramics decreased monotonously with the susceptibility (Σχ^μ) and ionic polarizability of primitive unit cell. The quality factor (Q × f) increased with bond strength and lattice energy (U_cal), especially the lattice energy of the Si-O bond. The temperature coefficient of resonant frequency (τ_f) was determined by the susceptibility and lattice energy of the Cu/Co-O bond. The following optimum microwave dielectric properties were obtained at x = 0.15 when sintered at 1000 °C for 3 h: ε_r = 8.45, Q×f =58958 GHz and τ_f = -34.4 ppm/°C.     

Room temperature NH3 gas sensor based on PMMA/RGO/ZnO nanocomposite films fabricated by in-situ solution polymerization

Effective detection of ammonia gas is of great importance due to its detrimental effects on human health, environment, and ecosystem. High-performance composite gas sensors are vital in accomplishing this goal. Herein, we investigate the performance of an ammonia (NH₃) gas sensor fabricated via dip-coating the silver interdigitated electrode for PMMA/RGO/ZnO (PRZ) nanocomposite solution with acetone as a solvent. The PRZ ternary nanocomposite was synthesized using the in-situ solution polymerization method and the resistive properties of the films assembled on the interdigitated electrode were analyzed, with respect to the fixed and varying ammonia gas concentrations, using LCR meter. When the sensor is operated in the controlled chamber containing ammonia gas at room temperature, the sensor responds rapidly to ammonia with a fast recovery of 13.02 s at a gas concentration of 350 ppm. The PRZ sensor exhibits high sensing percentage response (527%), excellent repeatability (four times), high sensitivity at low concentrations (less than 10 ppm), swift response and recovery times (1.94 s/13.02 s), and long-term stability (up to 90 days) with fluctuation of 3.2%, which signifies PRZ composite as a potential material for ammonia gas sensor. Aspects such as simplicity of the synthesis process and fabrication, excellent sensing performance, as well as fast response-recovery time at a particular gas concentration are noteworthy in this study. These features can be utilized for the detection of ammonia gas in chemical and biological fields.