The advancement of large scale integrated circuit technology has enabled the construction of complex interconnection networks. Graph theory provides a fundamental tool for designing and analyzing such networks. Graph Theory and Interconnection Networks provides a thorough understanding of these interrelated topics. After a brief introduction to graph terminology, the book presents well-known interconnection networks as examples of graphs, followed by in-depth coverage of Hamiltonian graphs. Different types of problems illustrate the wide range of available methods for solving such problems. The text also explores recent progress on the diagnosability of graphs under various models.

The conference proceeding of ICMMCS 2021 presents most recent scientific and technological advances in the fields of engineering mathematics and computational science to strengthen the links in the scientific community. It is a collection of high-quality, peer-reviewed research papers presented at the Second International Conference on Mathematical Modeling and Computational Science (ICMMCS 2021), held online during October 29-30, 2021. The topics covered in the book are mathematical logic and foundations, numerical analysis, neural networks, fuzzy set theory, coding theory, higher algebra, number theory, graph theory and combinatory, computation in complex networks, calculus, differential educations and integration, application of soft computing, knowledge engineering, machine learning, artificial intelligence, big data and data analytics, high-performance computing, network and device security, Internet of Things (IoT).

Exploring genomic functions requires accurate hysiological data acquisition to reveal the connection between genomic structure and medical symptoms. Most of these genetic experiments begin by changing certain genomic sequences of laboratory mice and then monitoring their physiological behavior. Physiological parameters typically include blood pressure, EKG, EEG, heart rate, body temperature, and body activity. In order to acquire accurate data with minimum distortion, a miniaturized wireless sensor without battery is highly desired. This work focuses on wireless activity sensor development. A lateral-axis differential capacitive MEMS accelerometer is designed and fabricated in MUMPs process. The system has been characterized with an untethered laboratory mouse. The received signal shows various acceleration waveforms corresponding to different types of mouse activities. The MEMS accelerometer and interface circuit can be potentially integrated with other biological sensors to achieve a miniature implantable biosensing system.

The advancement of large scale integrated circuit technology has enabled the construction of complex interconnection networks. Graph theory provides a fundamental tool for designing and analyzing such networks. Graph Theory and Interconnection Networks provides a thorough understanding of these interrelated topics. After a brief introduction to graph terminology, the book presents well-known interconnection networks as examples of graphs, followed by in-depth coverage of Hamiltonian graphs. Different types of problems illustrate the wide range of available methods for solving such problems. The text also explores recent progress on the diagnosability of graphs under various models.