Abstract This article focuses on applications of cryptography to the problem of secure communication; in this context, secure refers to both secrecy and message integrity. The article describes appropriate techniques for achieving these goals in both the private‐key and public‐key settings.

Abstract This article focuses on computing education for students at the K–8 grade range, when students are not only learning the skills they will need in their careers, but forming opinions and attitudes that will influence what their career choices will be.

Abstract Although the Internet has penetrated virtually every aspect of our lives, many scenarios exist where an existing network infrastructure is not available. For example, a hurricane may destroy buildings where the critical networking equipment is housed or may cut the power supply to the equipment. In such scenarios, it is extremely important for people living in the disaster area, as well as relief workers, to communicate with each other using whatever networking devices they have, without relying on any existing infrastructure. Such an infrastructureless network is called anad hoc network, more specifically, “a network that is setup, literally, for a specific purpose, to meet a quickly appearing communication need” (1). In addition to supporting human communication, an ad hoc network may be used for other purposes, such as monitoring the physical environment and detecting events (as we will explain later).An ad hoc network can be wired or wireless, but more research effort has been focused onwireless ad hoc networksas they are easier to establish and do not restrict the mobility of users.Major research issues in wireless ad hoc networks include, but are not limited to, how to ensure connectivity among nodes in the face of unplanned deployment, controlled or uncontrolled mobility, and nodes leaving/joining the network (potentially as a result of failures/repairs); how to access efficiently the shared wireless medium in a distributed fashion; how to route packets to their destinations in the presence of topology dynamics (because of mobility and failures) and sometimes intermittent connectivity; and how to meet the quality of service (QoS) requirements with distributed resource management. These issues are made even more challenging by the limited resource availability that is typical of a wireless ad hoc network.

Abstract This article examines the development of the computing field. Our account considers the computing field in four stages: infancy (1935–1950), childhood (1950–1970), adolescence (1970–1990), and young adulthood (1990–2010). The computing profession is a product of the fourth stage. The relationships between computing and other fields are vitally important.

Abstract Cerebellar Model Arithmetic Computer (CMAC) is a special neural network architecture that was proposed as a functional model of a certain part of the mammalian brain, which is called the cerebellum.Cerebellum (from the Latin word “little brain”) is a fist‐sized structure located at the lower back of the brain. It is believed that its main function is to coordinate movements of muscles and joints, but recent evidence suggests that the cerebellum is partially responsible for speech, learning, emotions, and attention.CMAC as an artificial neural network architecture was introduced by Albus in 1975. This architecture is based on a theory developed independently by D. Marr and J. Albus around 1970. As it was developed as a simple model of the cerebellum, its main task was to control manipulators. Its original name Cerebellar Model Articulation Controller also comes from this first application.

Abstract Digital arithmetic includes all aspects of the specification, analysis, and implementation of arithmetic operations (±, ×, ÷,, etc.) in digital systems such as general‐purpose processors, digital signal processors, graphics, and various embedded systems. These aspects include number systems, arithmetic algorithms, hardware implementation of arithmetic operators (adders, multipliers, dividers), elementary function implementation, and floating‐point arithmetic. The implementors of arithmetic units and systems must compromise between various objectives: speed, area (cost), power consumption, accuracy, reusability… hence, no “best solutions” exist in general, but solutions that can be significantly different depending on whether you are designing a circuit for a cellular phone or for a supercomputer.

Abstract This article will compare eight different guidelines for designing computer‐aided instruction (CAI); each one is based on a different understanding of how humans learn from multimedia. Once a design guideline is adopted, the preferred learning definition becomes apparent. Similarly, once designers agree on what the multimedia learning should be, a corresponding guideline can be applied.

Abstract The central processing unit (CPU) of a computer executes instructions encoded as a sequence of binary numbers. This program representation is commonly referred to as machine code. Assembly languages provide mnemonic abbreviations for the instructions of a computer. Program assemblers translate assembly language programs into machine code and binary data. Program assemblers allow the use of symbolic addresses (labels) to save the assembly language programmer from the tedious task of computing memory addresses manually. Macros provide textual substitution facilities. Pseudoinstructions, code optimizations, and just‐in‐time assembly complicate the task of program assemblers even more.

Abstract Software development is a dynamic process in which engineers constantly modify and refine their systems. Fundamental sources of change are usually associated with error correction, adaptations required to accommodate changes to external environments, and changes caused by changing requirements, design, or extensions of system functionality. Confusion among software engineers will develop when changes are not analyzed before they are made, recorded before they are implemented, reported to those with a need to know or controlled in a manner that will improve quality and reduce error. The discipline of identifying, organizing, and controlling modifications to a software system is called software configuration management (SCM). The ultimate goal of SCM is to maximize productivity by minimizing mistakes and to maintain software integrity, traceability, and accountability. This article summarizes the state of the art in both research and practice, as well as the well‐established and advanced technologies in the area of SCM.

Abstract This article explains fuzzy controllers and general fuzzy systems. It emphasizes the basic idea of fuzzy control and the mathematics behind the concept. The article is divided into two sections. In the first part, the mathematics of fuzzy systems is discussed, which includes the definitions and basics of fuzzy expert systems, general fuzzy systems, and their properties. In the second part, the concept of fuzzy control are detailed which include the selection of inputs and outputs, enhancing control knowledge as a rule base, determining which rules to use, determining conclusions, converting decisions into actions, and graphical visual representation of fuzzy control operation. Then, various fuzzy control techniques and the essence of how fuzzy systems can be used for a wide range of control architectures are explained. The article concludes with the summary and future prospects of fuzzy logic systems.