A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes

Methodologies, modeling approaches, and the interactions between various system elements involved in inspection-allocation and sensor-distribution problems, that influence operational quality decisions, are discussed. The surveyed papers fall into two broad categories: inspection-oriented quality-assurance strategies and diagnosis-oriented sensor-distribution strategies. Within each subarea, individual papers are further classified according to the system characteristics of the physical processes being investigated and the modeling characteristics of the approaches being used. As evident from nearly 100 journal articles published in the past four decades, these two problems have received considerable attention from researchers in quality engineering, management science, operations research as well as robotic vision arenas. We find that the inspection-allocation problem has been studied rather comprehensively whereas the relatively new sensor-distribution problem has plenty of opportunities for researchers. Discussions are also presented to summarize our observations based on the classifications along with some thoughts on future research.     

Information theoretic learning with adaptive kernels

This paper presents an online algorithm for adapting the kernel width that is a free parameter in information theoretic cost functions using Renyi's entropy. This kernel computes the interactions between the error samples and essentially controls the nature of the performance surface over which the parameters of the system adapt. Since the error in an adaptive system is non-stationary during training, a fixed value of the kernel width may affect the adaptation dynamics and even compromise the location of the global optimum in parameter space. The proposed online algorithm for adapting the kernel width is derived from first principles and minimizes the Kullback-Leibler divergence between the estimated error density and the true density. We characterize the performance of this novel approach with simulations of linear and nonlinear systems training, using the minimum error entropy criterion with the proposed adaptive kernel algorithm. We conclude that adapting the kernel width improves the rate of convergence of the parameters, and decouples the convergence rate and misadjustment of the filter weights.     

RoBiN: Random Access using Border Routers in Cellular Networks

Introducing Machine-to-Machine (M2M) communications over traditional 4G cellular networks make the cellular random access channel more congested and collision-prone. In order to resolve this random access congestion, we propose RoBiN - Random access using Border router in M2M cellular Networks. RoBiN proposes an architectural modification of introducing small cells, called Border Routers (BR), in cellular networks, with complete frequency reuse capability. We formulate the aforementioned challenge in terms of collision probability and system capacity. Subsequently, we propose an efficient solution for M2M communications in cellular networks. Exhaustive mathematical analysis shows that RoBiN significantly improves the random access success probability, by 50 % over existing 4G cellular systems. Simulation results on typical 4G networks corroborate our mathematical analysis and demonstrate almost 15 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and 3 times throughput improvements over legacy 4G cellular systems. Furthermore, RoBiN also achieves 50 %?80 % improvement in collision probability over existing time alignment matching work.     

Steganalysis of Network Packet Length Based Data Hiding

Recently, data hiding by modifying network parameters like packet header, payload, and packet length has become popular among researchers. Different algorithms have been proposed during the last few years which have altered the network packets in different ways to embed the data bits. Some of these algorithms modify the network packet length for embedding. Although most of the packet length based embedding schemes try to imitate the normal network traffic distribution, they have altered the statistical distribution of network packet lengths during embedding. These statistical anomalies can be exploited to detect such schemes. In this paper, a second order detection scheme for packet length based steganography has been proposed. A comprehensive set of experiments have been carried out to show that the proposed detection scheme can detect network packet length based steganography with a considerably high accuracy.     

Selective Extraction of (−)Epigallocatechin Gallate from Green Tea Leaves Using Two-Stage Infusion Coupled with Membrane Separation

A novel method for the extraction of (−)epigallocatechin gallate of high purity from green tea leaves is proposed in this study. The method comprised a two-stage water-based extraction followed by successive use of microfiltration and ultrafiltration. Microfiltration was used as a pretreatment to ultrafiltration. The best process conditions of each unit operation were estimated by performing well-planned experimentations. The clarified liquor was dried to powder by freeze drying. Chemical analyses revealed that the tea powder contained about 90% of polyphenols. The purity of (−)epigallocatechin gallate was found to be about 80%, while its average yield was 1.22 g/l. The method outlined in this study may have remarkable importance for the bulk production of high-purity (−)epigallocatechin gallate with potential application in pharmaceutical, cosmetic, and food-processing industries. Besides being a green process, this method can be easily scaled up for the commercial production of (−)epigallocatechin gallate.     

Interface Trap Charges Analysis on DC and High Frequency Characteristics of UTBB-FDSOI FET

Silicon - In this paper, we have studied effect of localised charges on performance of UTBB FDSOI FET. Purpose behind this work is to understand the performance of UTBB FDSOI FET under the...