A micromachined tunable resonator fabricated by the CMOS post-process of etching silicon dioxide

This work investigates the fabrication of a micromechanical tunable resonator using the commercial 0.35 μm complementary metal oxide semiconductor (CMOS) process and the post-process of only one maskless wet etching. The post-process has advantages of easy execution and low cost. The post-process employs an etchant (silox vapox III) to etch the silicon dioxide layer to release the suspended structures of the resonator. The tunable resonator comprises a driving unit, a tuning unit and a sensing unit. The resonant frequency of the resonator can be tuned using a dc-biased electrostatic comb of linearly varied finger-length. Experimental results show that the resonant frequency of the resonator is about 4.8 kHz, and it has a frequency-tuning range of 6.8% at the tuning voltage of 0–25 V.     

Joint Downlink Beamforming and Time-Reversed Matched Filtering for Multiuser UWB Signals

This paper proposes a simple delay-line (DL) downlink beamformer for pulsed ultra-wideband (UWB) systems to compensate for propagation delay, which is detrimental to UWB signals. Moreover, we propose a prefiltering-based transmission scheme at the fixed access point (i.e., base station) by shifting the signal processing needs from the receiver at the mobile unit to the transmitter where power and computational resources are plentiful. The DL transmitter array can steer signals to the direction of the desired mobile user such that the desired signals stemming from each antenna are coherently combined and the multiuser interference (MUI) is averaged out. Furthermore, the prefiltering scheme is composed of a set of time-reversed matched filters (TR MFs) subject to multiple-input single-output (MISO) finite impulse response (FIR) channels. A simple correlation receiver is proposed at the mobile unit to extract the information of the time-hopping (TH) UWB signal. The performances under different scenarios are extensively evaluated.     

Experimental and simulated angular profiles of fluorescence and diffuse reflectance emission from turbid media

Given the wavelength dependence of sample optical properties and the selective sampling of surface emission angles by noncontact imaging systems, differences in angular profiles due to excitation angle and optical properties can distort relative emission intensities acquired at different wavelengths. To investigate this potentiality, angular profiles of diffuse reflectance and fluorescence emission from turbid media were evaluated experimentally and by Monte Carlo simulation for a range of incident excitation angles and sample optical properties. For emission collected within the limits of a semi-infinite excitation region, normalized angular emission profiles are symmetric, roughly Lambertian, and only weakly dependent on sample optical properties for fluorescence at all excitation angles and for diffuse reflectance at small excitation angles relative to the surface normal. Fluorescence and diffuse reflectance within the emission plane orthogonal to the oblique component of the excitation also possess this symmetric form. Diffuse reflectance within the incidence plane is biased away from the excitation source for large excitation angles. The degree of bias depends on the scattering anisotropy and albedo of the sample and results from the correlation between photon directions upon entrance and emission. Given the strong dependence of the diffuse reflectance angular emission profile shape on incident excitation angle and sample optical properties, excitation and collection geometry has the potential to induce distortions within diffuse reflectance spectra unrelated to tissue characteristics.     

1174: futuristic trends and innovations in multimedia systems using big data,IoT and cloud technologies (FTIMS)

Multimedia Tools and Applications -     

Hybrid evolutionary algorithms in a SVR-based electric load forecasting model

Accurately electric load forecasting has become the most important issue in energy management; however, electric load often presents nonlinear data patterns. Therefore, looking for a novel forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) reveals superior nonlinear modeling capabilities by applying the structural risk minimization principle to minimize an upper bound of the generalization errors, it is quite different with ANNs model that minimizing the training errors. The purpose of this paper is to present a SVR model with a hybrid evolutionary algorithm (chaotic genetic algorithm, CGA) to forecast the electric loads, CGA is applied to the parameter determine of SVR model. With the increase of the complexity and the larger problem scale of electric loads, genetic algorithms (GAs) are often faced with the problems of premature convergence, slowly reaching the global optimal solution or trapping into a local optimum. The proposed CGA based on the chaos optimization algorithm and GAs, which employs internal randomness of chaos iterations, is used to overcome premature local optimum in determining three parameters of a SVR model. The empirical results indicate that the SVR model with CGA (SVRCGA) results in better forecasting performance than the other methods, namely SVMG (SVM model with GAs), regression model, and ANN model.     

Intraoperative application of optical spectroscopy in the presenceof blood

A simple but effective method of spectral processing was developed to minimize or remove the effects of the presence of superficial blood on tissue optical spectra and, hence, enhance the performance of optical-spectroscopic-based in vivo tissue diagnosis and surgical guidance. This spectral-processing algorithm was developed using the principles of absorption-induced light attenuation wherein the ratio of fluorescence intensity (F) and the hth power of diffuse reflectance intensity (Rd) at a given emission wavelength λ_m is immune to spectral distortions induced by the presence of blood on the tissue surface. Here, the exponent h is determined by the absorption coefficients of whole blood at the excitation and emission wavelengths. The theoretical basis of this spectral processing was verified using simulations and was experimentally validated. Furthermore, the optical spectra of brain tissues collected in vivo was processed using this algorithm to evaluate its impact on brain tissue differentiation using combined fluorescence and diffuse reflectance spectroscopy. Based on the simulation, as well as experimental results, it was observed that using F/Rd^h h can effectively reduce or remove spectral distortions induced by superficial blood contamination on tissue optical spectra. Thus, optical spectroscopy can also be used intraoperatively for applications such as surgical guidance of tumor resection     

Energy-Based Collaborative Spectrum Sensing for Cognitive UWB Impulse Radio

This paper focuses on the issue of collaborative spectrum sensing in cognitive ultra wideband (CUWB) impulse radio. We employ energy-based signal detection method and apply the Neyman-Pearson (NP) deci...     

Transparent conductive indium-doped zinc oxide films prepared by atmospheric pressure plasma jet

Atmospheric-pressure plasma processing has attracted much interest for industrial applications due to its low cost, high processing speed and simple system. In this study, atmospheric-pressure plasma jet technique was developed to deposit indium-doped zinc oxide films. The inorganic metal salts of zinc nitrate and indium nitrate were used as precursors for Zn ions and In ions, respectively. The effect of different indium doping concentration on the morphological, structural, electrical and optical properties of the films was investigated. Grazing incidence X-ray diffraction results show that the deposited films with a preferred (002) orientation. The lowest resistivity of 1.8 × 10^− 3 Ω cm was achieved with the 8 at.% indium-doped solution at the substrate temperature of 200 °C in open air, and average transmittance in the visible region was more than 80%.     

A Low Complexity Algorithm for Subcarrier-and-Bit Allocation in OFDMA-Based LTE Systems

In this paper, we propose optimum and sub-optimum resource allocation and opportunistic scheduling solutions for orthogonal frequency division multiple access (OFDMA)-based multicellular systems. The applicability, complexity, and performance of the proposed algorithms are analyzed and numerically evaluated. In the initial setup, the fractional frequency reuse (FFR) technique for inter-cell interference cancellation is applied to classify the users into two groups, namely interior and exterior users. Adaptive modulation is then employed according to the channel state information (CSI) of each user to meet the symbol error rate (SER) requirement. There then, we develop subcarrier-and-bit allocation method, which maximizes the total system throughput subject to the constraints that each user has a minimum data rate requirement. The algorithm to achieve the optimum solution requires high computational complexity which hinders it from practicability. Toward this end, we propose a suboptimum method with the complexity extensively reduced to the order of O(NK), where N and K denote the total number of subcarriers and users, respectively. Numerical results show that the proposed algorithm approaches the optimum solution, yet it enjoys the features of simplicity, dynamic cell configuration, adaptive subcarrier-and-bit allocation, and spectral efficiency.