Arabic Bank Check Processing: State of the Art

In this paper, we present a general model for Arabic bank check processing indicating the major phases of a check processing system. We then survey the available databases for Arabic bank check processing research. The state of the art in the different phases of Arabic bank check processing is surveyed (i.e., pre-processing, check analysis and segmentation, features extraction, and legal and courtesy amounts recognition). The open issues for future research are stated and areas that need improvements are presented. To the best of our knowledge, it is the first survey of Arabic bank check processing.     

Thermodynamic behavior of bismuth in copper pyrometallurgy: Molten matte,white metal and blister copper phases

This study developed thermodynamic data relating to the behavior of bismuth in copper smelting, converting and refining processes. The activity of bismuth was set by establishing a known vapor pressure of bismuth over melts of various phases. The melts were equilibrated with the vapor, and the resulting bismuth content used to establish bismuth activity coefficients. Experiments were carried out, under controlled oxygen potentials, at 1200 and 1250 °C. Results show that bismuth deviates positively from Raoult's Law at both 1200 and 1250 °C in copper (γ_Bi = 2.17, 2.27), white metal (γ_Bi = 6.1, 6.1), Cu₂S with 2 wt pct FeS (γ_Bi = 8.0, 8.0), Cu₂S with 4 to 6 wt pct FeS (γ_Bii = 16.4, 16.4) and Cu2S with 8 to 70 wt pct FeS (γ_Bi = 13.6, 13.6), respectively. Sabri Arac formerly Research Assistant at University of Arizona Gordon H. Geiger formerly head of the Department of Metallurgical Engineering at the University of Arizona     

Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot

A moving target tracking algorithm is proposed here and implemented on the Anafocus Eye-RIS vision system, which is a compact and modular platform to develop real time image processing applications. The algorithm combines moving-object detection with feature extraction in order to identify the specific target in the environment. The algorithm was tested in a mobile robotics experiment in which a robot, with the Eye-RIS mounted on it, pursued another one representing the moving target, demonstrating its performance and capabilities.     

A review on thermal cycling and drop impact reliability of SAC solder joint in portable electronic products

Currently, the portable electronic products trend to high speed, light weight, miniaturization and multifunctionality. In that field, solder joint reliability in term of both drop impact and thermal cycling loading conditions is a great concern for portable electronic products. The transition to lead-free solder happened to coincide with a dramatic increase in portable electronic products. Sn–Ag–Cu (SAC) is now recognized as the standard lead free solder alloy for packaging interconnects in the electronics industry. The present study reviews the reliability of different Ag-content SAC solder joints in term of both thermal cycling and drop impact from the viewpoints of bulk alloy microstructure and tensile properties. The finding of the study indicates that the best SAC composition for drop impact performance is not necessarily the best composition for optimum thermal cycling reliability. The level of Ag-content in SAC solder alloy can be an advantage or a disadvantage depending on the application, package and reliability requirements. As a result, most component assemblers are using at least two (and in many cases even more) lead-free solder sphere alloys to meet various package requirements.     

Modelling Intelligent Driving Behaviour Using Machine Learning

In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations. Environmental changes prompt the parameter values to change, a process which continues unless and until all processes are completed. This paper model intelligent behaviour by using a ‘driver behaviour model’ to obtain accurate intelligent driving behaviour patterns. This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another. These patterns constitute the outcome of different modules that collaborate to generate appropriate values. In this case, accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation, time-series dynamic backpropagation artificial neural network, multilayer perceptron and random sub-space on real-world data were also applied.     

Bit Impact Factor: Towards making fair vulnerability comparison

Reliability is becoming a major design concern in contemporary microprocessors since soft error rate is increasing due to technology scaling. Therefore, design time system vulnerability estimation is of paramount importance. Architectural Vulnerability Factor (AVF) is an early vulnerability estimation methodology. However, AVF considers that the value of a bit in a clock cycle is either required for Architecturally Correct Execution (i.e. ACE-bit) or not (i.e. unACE-bit); therefore, AVF cannot distinguish the vulnerability impact level of an ACE-bit. In this study, we present a new dimension which takes into account the vulnerability impact level of a bit. We introduce Bit Impact Factor metric which, we believe, will be helpful for extending AVF evaluation to provide a more accurate vulnerability analysis.     

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Metadata hiding for UAV video based on digital watermarking in DWT transform

As the advent of the Unmanned Aerial Vehicles (UAVs) has been increased, the protection of the information within the transmitted or stored video has become a big challenge. Most known drone systems attach metadata of the recorded video in separate files or in the header of the video. Current techniques make the metadata unsecure and easy to get lost and removed as well as it occupies more storage and bandwidth. In this paper, an efficient method is proposed to hide the metadata of UAVs video using the technology of digital watermarking. Discrete Wavelet Transform (DWT) is used to implement the embedding of the information robustly. The middle frequencies coefficients reside on CH3 sub-band are utilized to hide the watermark bits. In addition, a new scrambling algorithm is proposed to secure the information before hiding. The adaption of the proposed video watermarking algorithm to hide the metadata of the UAV video is achieved. The experimental results prove the high performance of the proposed method. The method had unnoticeable impact on the video quality where the PSNR of 44 dB is attained. The experiments show that the method achieves high robustness under various attacks and provides enough capacity for metadata hiding of UAV video.