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

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

Eagle-Eye: Open-Source Intelligence Tool for IoT Devices Detection

The use of Internet of Things (IoT) devices has been growing over the last few years making these appliances available in every household and organization. This significant rise of usability led to misuse, especially by non-technical people, making it an easy target for attackers to intrude on these networks. Therefore, the conventional thinking of protecting the information technology devices needs to embrace these frequent changes. Open-Source Intelligence (OSINT) is one of the modern techniques that can be used to keep track of these new systems by harvesting publicly available information. Collecting the needed information can be challenging for the IoT devices manufacturing companies and clients. This paper proposes an Open-Source Intelligence tool for IoT devices detection called Eagle-Eye which is integrated with Shodan search engine to perform OSINT queries and display it in user-friendly format. With the use of this tool companies, clients and researchers can automate their task of identifying and searching for different IoT devices statics that can be utilized and analyzed to harden these devices

An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

In recent years machine learning techniques have been able to perform tasks previously thought impossible or impractical such as image classification and natural language translation, as such this allows for the automation of tasks previously thought only possible by humans. This research work aims to test a naïve post processing grammar correction method using a Long Short Term Memory neural network to rearrange translated sentences from Subject Object Verb to Subject Verb Object. Here machine learning based techniques are used to successfully translate works in an automated fashion rather than manually and post processing translations to increase sentiment and grammar accuracy. The implementation of the proposed methodology uses a bounding box object detection model, optical character recognition model and a natural language processing model to fully translate manga without human intervention. The grammar correction experimentation tries to fix a common problem when machines translate between two natural languages that use different ordering, in this case from Japanese Subject Object Verb to English Subject Verb Object. For this experimentation 2 sequence to sequence Long Short Term Memory neural networks were developed, a character level and a word level model using word embedding to reorder English sentences from Subject Object Verb to Subject Verb Object. The results showed that the methodology works in practice and can automate the translation process successfully

Magec: An image searching tool for detecting forged images in forensic investigation

© 2016 IEEE. Manipulation of digital images for the purpose of forgery is a rapidly growing phenomenon that poses a challenge for cyber-crime investigators. Distinguishing original images from duplicates and the number of original copies within the same media are some examples of challenges presented by duplicate digital images. In this paper, we present a new image-searching tool called, Magec, to detect duplicate image(s) on digital media, using the original image modification attributes as a signature. First, we describe the tool and the methods used to detect duplicate images, then we evaluate the tool\u27s performance based on the number of folders it searches and the number of files it searches for. Later, we present the analysis of the tool using different operating system attributes. The goal is to find copies of the same object that is hidden; compressed images, or images saved with different attributes and demonstrates which one is the original image and thereby deduce which ones are copies. This research helps in better utilization of small/limited capacity devices, where limited storage capacity may be a problem. The experimental results prove that the presented search tool provides faster and accurate results. Finally, the conducted tests on the Magec tool analyzed, and verified, and the results are presented alongside with challenges identified

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

Context-Aware Driver Distraction Severity Classification using LSTM Network

Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems. In this paper, we present a deep learning technique that classifies drivers’ distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type. Using a video coding taxonomy, we study drivers’ distractions based on events information from Regions of Interest (RoI) such as hand gestures, facial orientation and eye gaze estimation. Furthermore, a novel probabilistic (Bayesian) model based on the Long shortterm memory (LSTM) network is developed for classifying driver’s distraction severity. This paper also proposes the use of frame-based contextual data from the multi-view TeleFOT naturalistic driving study (NDS) data monitoring to classify the severity of driver distractions. Our proposed methodology entails recurrent deep neural network layers trained to predict driver distraction severity from time series data

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets