This paper aims to measure the footprint for construction materials and construction and demolition waste (CDW) environmental impacts for a case study building in Egypt through the complete Life Cycle Assessment (LCA) of the building ‘from cradle to grave’. The LCA measures eight impact categories, including carbon emissions and energy demand. Our analysis demonstrates the relative importance of life cycle stages; construction processes and materials manufacturing that make the largest contributions to the buildings’ environmental impacts. The results show that the material manufacturing stage is the most critical stage because of its high contribution (about 70%) of the total environmental impacts. On the other hand the disposal stage contributes (about -10%). The results can help engineers and construction industry stakeholders in Egypt to use more sustainable construction materials and change their CDW management practice.

There is a rapid increasing in the deployment of photovoltaic but most of its installations are on building facades and roofs. Therefore, to achieve the needed large-scale integration of photovoltaic into our energy system will require large-scale deployment beyond the building scale, which will become a part of our landscape. This paper seeks to improve the functional performance of outdoor spaces through the potential of power generation by photovoltaic in outdoor spaces, in hot regions, and improving the thermal comfort. Site measurements, survey, interviews, a 3D model of a shading device using DesignBuilder software, and Computational fluid dynamics (CFD) simulations were developed as part of the study. A comparison was made between the obtained measurements and simulation outputs for validation. The methodology is demonstrated for a case-study in Borg El Arab New City, Alexandria, Egypt. The simulation shows that the installation of PV, on the rooftop and the South-West elevation, enhanced the thermal comfort, and generated three and half times the annual consumed power for lighting, laptop charging units, and Wi-Fi hotspot. The results show that the installations of photovoltaic on shading device will promote the function of outdoor spaces, concerning the technological devices, and will suffice the need for energy for different outdoor needs.

The study focuses on comparing between three of the high-performance glazing systems (HPGS) available in the market through a simulation of a case study of an office building which is located in New Cairo City in Egypt as a hot desert climate zone. The paper aims to enable the decision makers to select the most suitable HPGS through an integrated assessment which combines between the energy, environmental and economic performances assessment. A clear double-glazing system is used as a basic scenario for benchmarking and compared to the three HPGS scenarios (Passive, Active and Building Integrated Photovoltaic BIPV). An office building model has been implemented by DesignBuilder software to illustrate how this assessment can be applied on a real architectural project. The results show that considering the three performances (environmental, energy and economic) in the integrated assessment can outweigh a scenario at the expense of another one. Although the BIPV has advantages in the energy performance, it has the highest embodied carbon. The Electro-chromic glass has the worst economic performance, however the building lowest CO 2 production. Low-E is the most suitable glazing system for the office buildings in the hot desert climate as it has achieved the highest accumulative points.

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Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Active magnetic bearing (AMB) systems have attracted much attention in the high speed rotating machinery industry. This paper presents an application of discrete-time model predictive control (MPC) subject to input/states constraints to control an AMB system based on linear timeinvariant (LTI) model. The main control objectives are to levitate the rotor shaft of the AMB system while tracking a reference trajectory and to reject possible disturbances without violating the input and state constraints. A nonlinear (NL) model of the AMB system is considered; at each sampling instant, a finite horizon MPC problem is solved to compute the optimal control input. The performance and the efficiency of the proposed MPC is validated via simulation and comparison with another classical PID controller.