Resistivity dependence of minority carrier lifetime and cell performance in p-type dendritic web silicon ribbon

This study shows that the bulk lifetime in 95 μm thick p-type dendritic web silicon solar cells is a strong function of bulk resistivity. The higher the resistivity, the greater the bulk lifetime. This behavior is explained on the basis of dopant–defect interaction, which increases the lifetime limiting trap concentration with the addition of dopant atoms. Model calculations show that in the absence of doping dependence of bulk lifetime (τ), 2 Ω cm web should give the best cell efficiency for bulk lifetimes below 30 μs. However, strong doping dependence of bulk lifetime in p-web cells shifts the optimum resistivity from 2 to 15 Ω cm. Bulk lifetime in the as-grown web material was found to be less than 1 μs for all the resistivities. After the cell processing which involves phosphorus gettering, aluminum gettering, and SiN induced hydrogen passivation of defects, the bulk lifetime increased to 6.68, 11, 31 and 68.9 μs in 0.62, 1.37, 6.45 and 15 Ω cm p-type web material, respectively. Therefore, cell process induced recovery of lifetime in web is doping dependent, which favors high resistivity. Solar cells fabricated on 95 μm thick web silicon by a manufacturable process involving screen-printing and belt-line processing gave 14.5% efficient 4 cm² cells on 15 Ω cm resistivity. This represents a record efficiency for such a thin manufacturable screen-printed cell on a low-cost PV grade Si ribbon that requires no wafering or etching.     

A framework for predictive modeling of anatomical deformations

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.     

Face Recognition Through Different Facial Expressions

Face recognition has become an accessible issue for experts as well as ordinary people as it is a focal non-interfering biometric modality. In this paper, we introduced a new approach to perform face recognition under varying facial expressions. The proposed approach consists of two main steps: facial expression recognition and face recognition. They are two complementary steps to improve face recognition across facial expression variation. In the first step, we selected the most expressive regions responsible for facial expression appearance using the Mutual Information technique. Such a process helps not only improve the facial expression classification accuracy but also reduce the features vector size. In the second step, we used the Principal Component Analysis (PCA) to build EigenFaces for each facial expression class. Then, a face recognition is performed by projecting the face onto the corresponding facial expression Eigenfaces. The PCA technique significantly reduces the dimensionality of the original space since the face recognition is carried out in the reduced Eigenfaces space. An experimental study was conducted to evaluate the performance of the proposed approach in terms of face recognition accuracy and spatial-temporal complexity.

     

啤酒酵母和乳酸菌发酵棕榈粕渣饲料制备工艺的响应面优化

通过单因素实验和响应面法对啤酒酵母和乳酸菌发酵棕榈粕渣饲料制备工艺进行研究。结果表明:基料制备最佳发酵条件为发酵时间5 d、棕榈仁渣目数70目、菌料比1∶103、发酵温度30℃、含水量36%,该条件下基料中总菌落数为7.20×10~(10)个/g;发酵产物最佳发酵条件为发酵温度30℃、棕榈果渣与空果串配比1∶2、棕榈果渣及空果粉碎长度4 cm、配料比1∶10(基料与棕榈果渣和空果串质量比)、含水量35%、发酵时间36 d,该条件下发酵产物营养成分OD值为0.966。对发酵饲料主要成分进行分析,其粗蛋白、粗脂肪、粗纤维、粗灰分、钙、磷、含水量分别为7.20%、7.61%、26.20%、5.20%、0.38%、0.26%、16.20%,总菌落数为9.0×10~7个/g。该发酵方法工艺简单,具有良好的工业化生产前景。     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

An energy-aware forecasting-based framework using a novel moving coordinate scheme for sensor real-time communications

Wireless Networks - In order to save on the energy expended by a sensor node in its communications with the sink, forecasting-based frameworks have recently been proposed. Those frameworks...     

Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

Using Stochastic Differential Equation for Verification of Noise in Analog/RF Circuits

Today’s analog/RF design and verification face significant challenges due to circuit complexity, process variations and short market windows. In particular, the influence of technology parameters on circuits, and the issues related to noise modeling and verification still remain a priority for many applications. Noise could be due to unwanted interaction between the circuit elements or it could be inherited from the circuit elements. In addition, manufacturing disparity influence the characteristic behavior of the manufactured circuits. In this paper, we propose a methodology for modeling and verification of analog/RF designs in the presence of noise and process variations. Our approach is based on modeling the designs using stochastic differential equations (SDE) that will allow us to incorporate the statistical nature of noise. We also integrate the device variation due to 0.18μ m fabrication process in an SDE based simulation framework for monitoring properties of interest in order to quickly detect errors. Our approach is illustrated on nonlinear Tunnel-Diode and a Colpitts oscillator circuits.