This is a project with objectives to study and to apply the surface of the hardware and software part of the panel with LED diode lamps. Much more is focused on the programming of LED matrix diodes located on 16×96 electronic panels, respectively 16 rows of 96 columns. Also, there is elaborated the study of the mode of operation, composition, coding and use of software for panel diodes. In the focus of all this is the introduction of diode panels in use at "Kadri Zeka" University exactly at the Faculty of Computer Science to provide digital information to students, professors or anyone passing through the premises of the university what is happening inside the classrooms of the Faculty of Computer Science. This happens in real time in accordance with the schedule of lectures and exercises in the respective classrooms of the FCS. In fact, this project explains in detail the concept of LED diodes and microcontrollers, their features, operation, power supply and work with 0 [VDC] and 5 [VDC] voltages, respectively 0 and 1 logic bits. Meanwhile, the project also explains how coding, software building that locks, and unlocks LED diodes on a 96×16 LED diode panel, thereby producing text on the panel (display, screen) in static, mobile, animation or up-down, left and right movements according to the time allocated to the timers of the microcontrollers. Such action achieves the goal of the digitalization project of the classrooms of the Faculty of Computer Science.

<p>We are living in the era of internet and smartphones. Almost everybody in developing countries has at least one smartphone or connection to the internet through any other mobile device. So developing mobile software for the people or government is a big chance to make people life easier. The time has become a very important factor for which you can’t even pay for extra time, so making life easier for those people who don’t have time is big chance not losing it. All the data that are generated from the software or services is the best match to store those data on cloud, with which we don’t care about privacy and protection, availability to access them, manipulation of them and so on. In this paper, we describe how this newly emerged paradigm of cloud computing can be helpful for mobile e-Governance. Using cloud of course has a cost, but if you can’t give the same conditions that cloud gives, it is best choice to store the data on cloud. If we use cloud you don’t have to pay for all the IT staff who cares about the data, servers, databases, networks, with those money you can pay for cloud services. We start by an introduction about the cloud and e-Government, continuing with what the benefits and challenges of the e-Government and cloud are computing, the relationship of e-Government and cloud computing, mobile e-Governance in cloud and some examples of some countries that are using mobile e-Government in cloud. <br />Keywords: e-Government, e-Governance, Cloud Computing, Mobile, Data Storage</p>

This project has the main objective to study some main functions of hardware and software of panels with LED diodes using Wireless technology communication to communicate from computer to the LED diodes panels. It is also focused on the programming of LED matrix diodes located on 16X96 electronic panels, respectively 16 rows of 96 columns. Also, there is elaborated the study of the mode of operation, composition, coding and use of software for panel diodes using the wireless technology to change the panel program each time it is needed. In the focus of all this is the introduction of diode panels in use at "Kadri Zeka" University exactly at the Faculty of Computer Science to provide digital information to students, professors or anyone passing through the premises of the university that is happening inside the classrooms of the Faculty of Computer Science. This happens in real time in accordance with the schedule of lectures and exercises in the respective classrooms of the FCS. In fact, this project explains in detail the concept of LED diodes and microcontrollers, their features, operation, power supply and work with 0 [VDC] and 5 [VDC] voltages, respectively 0 and 1 logic bits. Meanwhile, the project also explains how coding, how the software is developed to switch on, and switch off LED diodes on a 96X16 LED diode panel, thereby producing text on the panel (display, screen) in static, mobile, animation or up-down, left and right movements according to the time allocated to the timers of the microcontrollers. Such actions achieve the goal of the digitalization project of the classrooms of the Faculty of Computer Science. The same technology and all the hardware devices are possible to be used in all the marketing to represent information about different scopes.

Nowadays we have so much images provided by different types of machines, while we need to store them or transfer to other devices or via internet, we need to compress them because the images usually have large amount of size. Compressing them reduces time for transferring files. The compression can be done with different methods and software in order to reduce their capacity expressed in megabytes as much as tens of hundreds of gigabytes for more files. It is well known that the speed of information transmission depends mainly on its quantity or the capacity of the information package. Image compression is a very important task for data transfer and data storage, especially nowadays because of the development of many image acquisition devices. If there is no compression technique used on these data, they may occupy immense space of memory, or render difficult data transmission. Artificial Neural Networks (ANN) have demonstrated good capacities for lossy image compression. The ANN algorithm we investigate is BEP-SOFM, which uses a Backward Error Propagation algorithm to quickly obtain the initial weights, and then these weights are used to speed up the training time required by the Self-Organizing Feature Map algorithm. In order to obtain these initial weights with the BEP algorithm, we analyze the hierarchical approach, which consists in preparing the image to compress using the quadtree data structure by segmenting the image into blocks of different sizes. Small blocks are used to represent image areas with large-scale details, while the larger ones represent the areas that have a small number of observed details. Tests demonstrate that the approach of quadtree segmentation quickly leads to the initial weights using the BEP algorithm.