PAPR reduction technique for LTE OFDMA systems

Long term evolution (LTE) is the final step toward the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile networks. LTE uses orthogonal frequency division multiple access (OFDMA) for the downlink transmission and single carrier-frequency division multiple access (SC-FDMA) for uplink. OFDMA meets the 4G requirement for spectrum flexibility and enables cost-efficient solutions for very wide carriers with high peak rates. However, the potentially large peak-to-average power ratio (PAPR) of the transmitting signals has limited its application. This high PAPR causes interference when the OFDM signals are passed through an amplifier which does not have enough linear range. In this article, we investigate a clipping based PAPR reduction method for LTE OFDMA systems. Simulation results show that the clipping method is reduced PAPR significantly which decreases as the number of clip and filtering level is increased. As a results, increase the mean transmit power, and improve the power amplifier efficiency. This comes at the outlay of complexity, efficiency as well as cost.

Kinetically controlled homogenization and transformation of crystalline fiber networks in supramolecular materials

Supramolecular materials with three-dimensional fiber networks have applications in many fields. For these applications, a homogeneous fiber network is essential in order to get the desired performance of a material. However, such a fiber network is hard to obtain, particularly when the crystallization of fiber takes place nonisothermally. In this work, a copolymer is used to kinetically control the nucleation and fiber network formation of a small molecular gelling agent, N-lauroyl-L-glutamic acid di-nbutylamide (GP-1) in benzyl benzoate. The retarded nucleation and enhanced mismatch nucleation of the gelator by the additive leads to the conversion of a mixed fiber network into a homogeneous network consisting of spherulites only. The enhanced structural mismatch of the GP-1 during crystallization is quantitatively characterized using the rheological data. This effect also leads to the transformation of an interconnecting (single) fiber network of GP-1 into a multidomain fiber network in another solvent, isostearyl alcohol. The approach developed is significant to the production of supramolecular materials with homogeneous fiber networks and is convenient to switch a single fiber network to a multidomain network without adjusting the thermodynamic driving force.

Obtaining accurate frequencies of sequential patterns over a single sequence

In the mining and analysis of a single long sequence, one fundamental and important problem is obtaining accurate frequencies of sequential patterns over the sequence. However, we identify that five previous frequency measures suffer from inherent inaccuracies. To obtain more accurate frequencies, we introduce two basic principles called strict anti-monotonicity and maximum-count for frequency measures. Under the two principles, a new frequency measure is presented. An algorithm is also devised to compute it. Both theoretical analysis and empirical evaluation show that more accurate frequencies can be obtained under the new measure

Comonitoring of adenosine and dopamine using the wireless instantaneous neurotransmitter concentration system : proof of principle : laboratory investigation

Object

The authors of previous studies have demonstrated that local adenosine efflux may contribute to the therapeutic mechanism of action of thalamic deep brain stimulation (DBS) for essential tremor. Real-time monitoring of the neurochemical output of DBS-targeted regions may thus advance functional neurosurgical procedures by identifying candidate neurotransmitters and neuromodulators involved in the physiological effects of DBS. This would in turn permit the development of a method of chemically guided placement of DBS electrodes in vivo. Designed in compliance with FDA-recognized standards for medical electrical device safety, the authors report on the utility of the Wireless Instantaneous Neurotransmitter Concentration System (WINCS) for real-time comonitoring of electrical stimulation–evoked adenosine and dopamine efflux in vivo, utilizing fast-scan cyclic voltammetry (FSCV) at a polyacrylonitrile-based (T-650) carbon fiber microelectrode (CFM).
Methods

The WINCS was used for FSCV, which consisted of a triangle wave scanned between −0.4 and +1.5 V at a rate of 400 V/second and applied at 10 Hz. All voltages applied to the CFM were with respect to an Ag/AgCl reference electrode. The CFM was constructed by aspirating a single T-650 carbon fiber (r = 2.5 μm) into a glass capillary and pulling to a microscopic tip using a pipette puller. The exposed carbon fiber (the sensing region) extended beyond the glass insulation by ~ 50 μm. Proof of principle tests included in vitro measurements of adenosine and dopamine, as well as in vivo measurements in urethane-anesthetized rats by monitoring adenosine and dopamine efflux in the dorsomedial caudate putamen evoked by high-frequency electrical stimulation of the ventral tegmental area and substantia nigra.
Results

The WINCS provided reliable, high-fidelity measurements of adenosine efflux. Peak oxidative currents appeared at +1.5 V and at +1.0 V for adenosine, separate from the peak oxidative current at +0.6 V for dopamine. The WINCS detected subsecond adenosine and dopamine efflux in the caudate putamen at an implanted CFM during high-frequency stimulation of the ventral tegmental area and substantia nigra. Both in vitro and in vivo testing demonstrated that WINCS can detect adenosine in the presence of other easily oxidizable neurochemicals such as dopamine comparable to the detection abilities of a conventional hardwired electrochemical system for FSCV.
Conclusions

Altogether, these results demonstrate that WINCS is well suited for wireless monitoring of high-frequency stimulation-evoked changes in brain extracellular concentrations of adenosine. Clinical applications of selective adenosine measurements may prove important to the future development of DBS technology.

Wireless instantaneous neurotransmitter concentration system-based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring - laboratory investigation

Object  In a companion study, the authors describe the development of a new instrument named the Wireless Instantaneous Neurotransmitter Concentration System (WINCS), which couples digital telemetry with fast-scan cyclic voltammetry (FSCV) to measure extracellular concentrations of dopamine. In the present study, the authors describe the extended capability of the WINCS to use fixed potential amperometry (FPA) to measure extracellular concentrations of dopamine, as well as glutamate and adenosine. Compared with other electrochemical techniques such as FSCV or high-speed chronoamperometry, FPA offers superior temporal resolution and, in combination with enzyme-linked biosensors, the potential to monitor nonelectroactive analytes in real time.

Methods  The WINCS design incorporated a transimpedance amplifier with associated analog circuitry for FPA; a microprocessor; a Bluetooth transceiver; and a single, battery-powered, multilayer, printed circuit board. The WINCS was tested with 3 distinct recording electrodes: 1) a carbon-fiber microelectrode (CFM) to measure dopamine; 2) a glutamate oxidase enzyme–linked electrode to measure glutamate; and 3) a multiple enzyme–linked electrode (adenosine deaminase, nucleoside phosphorylase, and xanthine oxidase) to measure adenosine. Proof-of-principle analyses included noise assessments and in vitro and in vivo measurements that were compared with similar analyses by using a commercial hardwired electrochemical system (EA161 Picostat, eDAQ; Pty Ltd). In urethane-anesthetized rats, dopamine release was monitored in the striatum following deep brain stimulation (DBS) of ascending dopaminergic fibers in the medial forebrain bundle (MFB). In separate rat experiments, DBS-evoked adenosine release was monitored in the ventrolateral thalamus. To test the WINCS in an operating room setting resembling human neurosurgery, cortical glutamate release in response to motor cortex stimulation (MCS) was monitored using a large-mammal animal model, the pig.

Results   The WINCS, which is designed in compliance with FDA-recognized consensus standards for medical electrical device safety, successfully measured dopamine, glutamate, and adenosine, both in vitro and in vivo. The WINCS detected striatal dopamine release at the implanted CFM during DBS of the MFB. The DBS-evoked adenosine release in the rat thalamus and MCS-evoked glutamate release in the pig cortex were also successfully measured. Overall, in vitro and in vivo testing demonstrated signals comparable to a commercial hardwired electrochemical system for FPA.

Conclusions  By incorporating FPA, the chemical repertoire of WINCS-measurable neurotransmitters is expanded to include glutamate and other nonelectroactive species for which the evolving field of enzyme-linked biosensors exists. Because many neurotransmitters are not electrochemically active, FPA in combination with enzyme-linked microelectrodes represents a powerful intraoperative tool for rapid and selective neurochemical sampling in important anatomical targets during functional neurosurgery.

Improving fiber trapping with a contact surface during the ring twisting of two cotton yarns

In this study, a geometrical model was introduced to improve the hair trapping via a surface contacting the yarn-twisting triangle during ring twisting of two single yarns. The fiber-trapping improvement with the contact surface was analyzed theoretically. Then, single Ne 80 ring cotton yarns were used to produce two-ply yarns under different ring-twisting conditions, namely conventional twisting, dry twisting of yarns with a plane surface, wet twisting of yarns with a plane surface, dry twisting of yarns with a grooved surface, and wet twisting of yarns with a grooved surface. Plied yarn properties, including yarn hairiness, strength, and irregularity, were tested. The Student Newman Keuls (SNK) test and variation analysis were also carried out in the SPSS program to study the effect of different contact surfaces on related yarn properties; the significance level was 0.05 for the SNK test and variation analysis. The hairiness of plied yarns was significantly reduced when twisting with the plane or grooved surface, especially for the wet twisting cases. This corresponds well with our model on improving fiber trapping.

Robust single-hidden layer feedforward network-based pattern classifier

In this paper, a new robust single-hidden layer feedforward network (SLFN)-based pattern classifier is developed. It is shown that the frequency spectrums of the desired feature vectors can be specified in terms of the discrete Fourier transform (DFT) technique. The input weights of the SLFN are then optimized with the regularization theory such that the error between the frequency components of the desired feature vectors and the ones of the feature vectors extracted from the outputs of the hidden layer is minimized. For the linearly separable input patterns, the hidden layer of the SLFN plays the role of removing the effects of the disturbance from the noisy input data and providing the linearly separable feature vectors for the accurate classification. However, for the nonlinearly separable input patterns, the hidden layer is capable of assigning the DFTs of all feature vectors to the desired positions in the frequencydomain such that the separability of all nonlinearly separable patterns are maximized. In addition, the output weights of the SLFN are also optimally designed so that both the empirical and the structural risks are well balanced and minimized in a noisy environment. Two simulation examples are presented to show the excellent performance and effectiveness of the proposed classification scheme.

Size-frequency distributions along a latitudinal gradient in middle permian fusulinoideans

Geographic gradients in body size within and among living species are commonly used to identify controls on the long-term evolution of organism size. However, the persistence of these gradients over evolutionary time remains largely unknown because ancient biogeographic variation in organism size is poorly documented. Middle Permian fusulinoidean foraminifera are ideal for investigating the temporal persistence of geographic gradients in organism size because they were diverse and abundant along a broad range of paleo-latitudes during this interval (~275–260 million years ago). In this study, we determined the sizes of Middle Permian fusulinoidean fossils from three different paleo-latitudinal zones in order to examine the relationship between the size of foraminifers and regional environment. We recovered the following results: keriothecal fusulinoideans are substantially larger than nonkeriothecal fusulinoideans; fusulinoideans from the equatorial zone are typically larger than those from the north and south transitional zones; neoschwagerinid specimens within a single species are generally larger in the equatorial zone than those in both transitional zones; and the nonkeriothecal fusulinoideans Staffellidae and Schubertellidae have smaller size in the north transitional zone. Fusulinoidean foraminifers differ from most other marine taxa in exhibiting larger sizes closer to the equator, contrary to Bergmann's rule. Meridional variation in seasonality, water temperature, nutrient availability, and carbonate saturation level are all likely to have favored or enabled larger sizes in equatorial regions. Temporal variation in atmospheric oxygen concentrations have been shown to account for temporal variation in fusulinoidean size during Carboniferous and Permian time, but oxygen availability appears unlikely to explain biogeographic variation in fusulinoidean sizes, because dissolved oxygen concentrations in seawater typically increase away from the equator due to declining seawater temperatures. Consequently, our findings highlight the fact that spatial gradients in organism size are not always controlled by the same factors that govern temporal trends within the same clade.

Insulin sensitivity in response to a single resistance exercise session in apparently healthy individuals

BACKGROUND: Regular resistance exercise completed for a number of weeks has been shown to increase insulin sensitivity and reduce the risk of diabetes-related complications. However, the acute responses to resistance exercise have not been adequately investigated in relation to training frequency.
AIM: To investigate the changes to insulin sensitivity in apparently healthy individuals following a single session of unaccustomed resistance exercise.
SUBJECTS AND METHODS: Ten sedentary, apparently healthy individuals performed a baseline oral glucose tolerance test and maximal strength testing. Participants then performed a single session of moderate-high intensity resistance exercise which was followed by 4 consecutive days of oral glucose tolerance testing, for which participants replicated their initial diet. Mean estimated insulin sensitivity change scores from baseline values and their 95% confidence intervals were compared to the previously determined values for a clinically meaningful change.
RESULTS: Two participants were identified as having hyperinsulinemia and their data were therefore removed from the main analysis. There was a clinically meaningful increase in insulin response (mean >7237 pmol·l⁻¹·120 min⁻¹) on all days following the exercise session and a clinically meaningful increase in glucose response (mean >81 mmol·l⁻¹·120 min⁻¹) on only the 3rd day following exercise. These changes suggest a potentially adverse short-term effect. Additionally, the 2 individuals with hyperinsulinemia displayed more extreme results.
CONCLUSION: These results suggest that insulin sensitivity may be impaired following a single session of unaccustomed resistance exercise for approximately 4 days in healthy untrained, older individuals. Further research is required for individuals with hyperinsulinemia

A theoretical analysis for fiber contacts in multilayer nanofibrous assemblies

A theoretical analysis is presented for the estimation of the number of contacts between fibers in random multilayer nanofibrous assemblies with arbitrary fiber diameter and orientation. The statistics of fiber contacts for single-layer nanofiber mats were considered first, and the equations were developed for three-dimensional multilayer nanofibrous assemblies by considering the superposition of the single-layer assemblies. Based on the theoretical approach presented here for multilayer nanofibrous networks, the network porosity, mean fiber diameter and a function of fiber aspect ratio contribute to a model to determine the average number of fiber contacts per unit fiber length in multilayer nanofibrous mats. The theory is studied parametrically and results compared with the work of a model presented by Samson. It is shown that the presented model compared to the existing models is more sensitive with the fiber diameter in the nano-scale. It is also believed that the presented theory for fiber-to-fiber contacts is more realistic and useful for further studies of multilayer nanofibrous assemblies.

A controllable simple method for production of polymeric nanofiberous composite via single nozzle jet electrospinning

A special Micro-Nano fiberous composite structure composed of nano- and micro-scale fiber of Polycaprolactone (PCL) and Gelatin produced by using single nozzle electrospinning instrument. By controlling the solution (polymer concentration and polymer composition percent) and processing parameters of electrospinning (feed rate and electrostatic field), different portion of nano and micro fibers in the structure is achieved. This method can result a one-stage method of fabrication of Micro-Nano fiberous composite structure instead of previously used twostage process or using additional facility to produce structure near-similar to this composite structure. The resulting materials finely mingle nano- and micro fibers together, rather than simply juxtaposing them, as is commonly found in the literature. The results obtained from SEM, Flow Porosimetry, and DMA led the authors to confirm that the structure has very versatile and improved properties for many applications like cell culture scaffolds. These favourable mechanical and structural properties can provide easier opening of spaces for cell penetration to deeper levels of the scaffold and withstand to tensions during to clinical handling.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.