A novel architecture for a reconfigurable micro machining cell

There is a growing demand for machine tools that are specifically designed for the manufacture of micro-scale components. Such machine tools are integrated into flexible micro-manufacturing systems. Design objectives for such tools include energy efficiency, small footprint and importantly flexibility, with the ability to easily reconfigure the manufacturing system in response to process requirements and product demands. Such systems find application in medical, photonics, automotive and electronic industries. In this paper, a new architecture for a reconfigurable micro manufacturing system is presented. The proposed architecture comprises a micro manufacturing cell with the key design feature being a hexagonal-base on which three tool heads can be attached to three of its sides. Each such machine-tool head, or processing module, is able to perform a different manufacturing process. These tool heads are interchangeable, enabling the cell to be configured to process a wide range of components with different materials, dimensions, tolerances and specification. Additional components of the cell include manipulation robots and an automated buffer unit. Such cells can be integrated into a manufacturing system via a modular conveyor belt to transfer parts from one cell to another and into assembly. A key consideration of the architecture is a control system that is also modular and reconfigurable; such that when new processing modules are introduced the control system is aware of the change and adjusts accordingly. Further to this coordination, issues between modules and machining cells are also considered. Other design considerations include work-piece holding and manipulation. This paper provides an overview of the architecture, the key design and implementation challenges as well as a high level operational performance assessment by means of a discrete event simulation model of the micro factory cell

Performance Analysis of Coherent and Noncoherent Modulation under I/Q Imbalance

In-phase/quadrature-phase Imbalance (IQI) is considered a major performance-limiting impairment in direct-conversion transceivers. Its effects become even more pronounced at higher carrier frequencies such as the millimeter-wave frequency bands being considered for 5G systems. In this paper, we quantify the effects of IQI on the performance of different modulation schemes under multipath fading channels. This is realized by developing a general framework for the symbol error rate (SER) analysis of coherent phase shift keying, noncoherent differential phase shift keying and noncoherent frequency shift keying under IQI effects. In this context, the moment generating function of the signal-to-interference-plus-noise-ratio is first derived for both single-carrier and multi-carrier systems suffering from transmitter (TX) IQI only, receiver (RX) IQI only and joint TX/RX IQI. Capitalizing on this, we derive analytic expressions for the SER of the different modulation schemes. These expressions are corroborated by comparisons with corresponding results from computer simulations and they provide insights into the dependence of IQI on the system parameters. We demonstrate that the effects of IQI differ considerably depending on the considered system as some cases of single-carrier transmission appear robust to IQI, whereas multi-carrier systems experiencing IQI at the RX require compensation in order to achieve a reliable communication link

Role of semicore states in the electronic structure of group-III nitrides: An exact exchange study

The bandstructure of the zinc-blende phase of AlN, GaN, InN is calculated employing the exact-exchange (EXX) Kohn-Sham density-functional theory and a pseudopotential plane-wave approach. The cation semicore d electrons are treated both as valence and as core states. The EXX bandgaps of AlN and GaN (obtained with the Ga 3d electrons included as core states) are in excellent agreement with previous EXX results, GW calculations and experiment. Inclusion of the semicore d electrons as valence states leads to a large reduction in the EXX bandgaps of GaN and InN. Contrary to common belief, the removal of the self-interaction, by the EXX approach, does not account for the large disagreement for the position of the semicore d electrons between the LDA results and experiment.Comment: 10 pages including 3 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

Assessment of temperature polarization in membrane distillation channels by liquid crystal thermography

The measurement of local temperature distributions within a Membrane Distillation (MD) channel is a crucial step for the optimization of the channel and spacer geometry. This information allows the estimation of temperature polarization phenomena, which can dramatically influence the thermal efficiency of the process and the optimal choice of the geometric configuration (net spacer features, channel size, etc.). In the present work a recently presented experimental technique, based on the use of Thermochromic Liquid Crystals (TLCs) and digital image analysis, has been employed in order to assess temperature polarization phenomena. The local heat transfer coefficient distribution on the membrane surface in a MD spacer-filled channel was thus assessed. Different diamond spacers geometries were investigated, in order to highlight how the geometrical features affect both pressure drop and heat transfer in spacer filled channels

Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

Predicting the Standard and Deviant Patterns In EEG Signals Based On Deep Learning Model

In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently,such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed