This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.
Nowadays, multimedia communication has become very widespread and this requires it to be protected from attackers and transmitted securely for reliability. Encryption and decryption techniques are useful in providing effective security for speech signals to ensure that these signals are transmitted with secure data and prevent third parties or the public from reading private messages. Due to the rapid improvement in digital communications over the recent period up to the present, the security of voice data transmitted over various networks has been classified as a favored field of study in earlier years. The contributions to audio encryption are discussed in this review. This Comprehensive review mainly focuses on presenting several kinds of methods for audio encryption and decryption the analysis of these methods with their advantages and disadvantages have been investigated thoroughly. It will be classified into encryption based on traditional methods and encryption based on advanced chaotic systems. They are divided into two types, continuous-time system, and discrete-time system, and also classified based on the synchronization method and the implementation method. In the fields of information and communications security, system designers face many challenges in both cost, performance, and architecture design, Field Programmable gate arrays (FPGAs) provide an excellent balance between computational power and processing flexibility. In addition, encryption methods will be classified based on Chaos-based Pseudo Random Bit Generator, Fractional-order systems, and hybrid chaotic generator systems, which is an advantageous point for this review compared with previous ones. Audio algorithms are presented, discussed, and compared, highlighting important advantages and disadvantages. Audio signals have a large volume and a strong correlation between data samples. Therefore, if traditional cryptography systems are used to encrypt such huge data, they gain significant overhead. Standard symmetric encryption systems also have a small key-space, which makes them vulnerable to attacks. On the other hand, encryption by asymmetric algorithms is not ideal due to low processing speed and complexity. Therefore, great importance has been given to using chaotic theory to encode audio files. Therefore, when proposing an appropriate encryption method to ensure a high degree of security, the key space, which is the critical part of every encryption system, and the key sensitivity must be taken into account. The key sensitivity is related to the initial values and control variables of the chaotic system chosen as the audio encryption algorithm. In addition, the proposed algorithm should eliminate the problems of periodic windows, such as limited chaotic range and non-uniform distribution, and the quality of the recovered audio signal remains good, which confirms the convenience, reliability, and high security.
Gas or liquefied petroleum gas (LPG) is a chemical substance resultant from petroleum and could be dangerous in industrial places or those that deal with this substance. Gas leakage causes many health issues. So, to prevent such catastrophes and in order to maintain a clean air environment, the workspace atmosphere should be frequently monitored and controlled. The proposed monitoring gas leakage detector system is based on Internet of Things (IoT) technology. NodeMCU ESP8266 Wi-Fi is used to be the microcontroller for the whole system. The combustible gas sensor (MQ2) is used in order to detect the presence of methane (CH4) and carbon monoxide gas (CO). MQ2 sensor will detect the concentration of the gas according to the voltage output of the sensor and the ESP8266 will send the data reading from the gas sensor to Blynk IoT platform over an IOS phone; data visualization is done using Thingspeak IoT Platform. Besides, a fan will immediately work upon the leakage occurs along with an alarming buzzer.
A new algorithm for multi-object recognition and localization is introduced in this paper. This algorithm deals with objects which have different reflectivity factors and distinguish color with respect to the other objects. Two beacons scan multi-color objects using long distance IR sensors to estimate their absolute locations. These two beacon nodes are placed at two corners of the environment. The recognition of these objects is estimated by matching the locations of each object with respect to the two beacons. A look-up table contains the distances information about different color objects is used to convert the reading of the long distance IR sensor from voltage to distance units. The locations of invisible objects are computed by using absolute locations of invisible objects method. The performance of introduced algorithm is tested with several experimental scenarios that implemented on color objects.