Capacitive micromachined ultrasonic tranducers (cMUTs) have recently emerged as an alternative to conventional piezoelectric transducers. They offer many advantages in terms of bandwidth, fabrication of layer arrays, efficiency, and sensitivity. This research presents the principles of operation, fabrication process steps, and application of the capacitive micromachined ultrasound transducer. The study also demonstrates in detail the collapse voltage design parameter of a cMUT membrane. Several important applications are presented to show the feasibility of using cMUTs which are demonstrated by imaging examples in immersion and air due to the cMUT capability of producing large bandwidth (123% fractional bandwidth) and lower impedance mismatch. Finally, the advantages of three-dimensional echographic images based on moving ultrasound linear array its technique are discussed in detail and compared with those of two-dimensional optical hand geometry

In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

Mobile Ad Hoc Network (MANET) is a set of mobile nodes that communicate with each other to configure an immediate network without utilizing any of infrastructure, the centralized access point or centralized administration in multiple hop manner. There are a lot of routing protocols have been proposed in MANET which are different from each other in the performance and the mechanism. Therefore, the performance study of those protocols in different scenarios is needed. This paper presents the performance comparison between Dynamic Source Routing (DSR), Ad hoc on demand distance Vector Routing (AODV) as reactive routing protocols and Destination Sequenced Distance Vector (DSDV) as a proactive routing protocol to precisely determine which protocol is more effective. Network Simulator (NS) version 2.35 has used to simulate and evaluate the performance of these protocols in terms of the packet delivery ratio, average throughput, average end-to-end delay, and packet loss ratio with respect to the variable number of nodes.