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Audio formats and data interchange
Published in Francis Rumsey, Desktop Audio Technology, 2003
For example, there are two approaches to byte ordering: the so-called little-endian order in which the least significant byte comes first or at the lowest memory address, and the big-endian format in which the most significant byte comes first or at the highest memory address. These relate to the byte ordering used in data processing by the two most common microprocessor families and thereby to the two most common operating systems used in desktop audio workstations. Motorola processors, as used in the Apple Macintosh, deal in big-endian byte ordering, and Intel processors, as used in MS-DOS machines, deal in little-endian byte ordering. It is relatively easy to interpret files either way around but it is necessary to know that there is a need to do so if one is writing software.
The microprocessor system
Published in D.A. Bradley, N.C. Burd, D. Dawson, A.J. Loader, Mechatronics, 2018
D.A. Bradley, N.C. Burd, D. Dawson, A.J. Loader
In the example of Fig. 10.1 the data bus has eight lines, and hence the range of values which a single item of data can take is restricted to that which can be represented by 8 binary digits or bits. Eight bits are referred to as a byte, and can represent a decimal number from 0 to 255 (28 – 1). Likewise the address bus, consisting of sixteen lines, can represent an address number in the range 0 to 216 – l or 65 535. This number is usually abbreviated to the binary equivalent of the decimal number and expressed as 64K, where K is equal to 1024 in the binary number system. To the CPU, the system appears as a series of 64K consecutive memory locations, each capable of storing an 8 bit binary value.
® for Engineers
Published in William Bober, ® Essentials, 2017
The memory of a computer is an ordered sequence of storage locations called memory cells. Each memory cell has an address indicating its relative position in memory. The memory cell is a collection of smaller units called bytes. A byte is the amount of storage required to store a single character (letter, number, or symbol). A byte is a collection of smaller units called bits. A bit takes on the value of 0 or 1, and is therefore suited for the binary system of numbers. Generally, there are eight bits to a byte. Each character or set of characters, or value, is represented by a particular pattern of zeros and ones. The computer can retrieve or store a value.
Energy Efficient Lightweight Cryptography Algorithms for IoT Devices
Published in IETE Journal of Research, 2022
Tarun Kumar Goyal, Vineet Sahula, Deepak Kumawat
Advanced Encryption Standard (AES) is private key symmetric block cipher, which is faster and stronger than triple DES. In AES 128 Bit plain text converts into 128-bit cipher text with the help of 128 bit, 192 bit, and 256 bit keys. It is shown in Figure 2. To increase the complexity, its operation repeated for round of 10, 12, and 14 (). In every round requires four-word byte so total requirement for 128 bit size. First four word is the key and then they are expanded to calculate remaining 40 words for 10 rounds. This technique is called as key expansion. Each round includes Sub Bytes, Shift rows, Mix column, add round key, and last round is slightly different in which mix column is not present.
Smart logistics based on the internet of things technology: an overview
Published in International Journal of Logistics Research and Applications, 2021
Yangke Ding, Mingzhou Jin, Sen Li, Dingzhong Feng
IoT technology has been developing in performance and function. Many anti-collision algorithms for RFID tags have been constructed to avoid tag collisions (Chen 2015; Su et al. 2018; Chen and Zhao 2019), and several novel protocols have been proposed to identify the missing tags (Chen, Xue, and Wang 2017; Shahzad and Liu 2016). The sensor’s battery life can be extended by optimising communication protocols and by in-network preprocessing of the sensor data (Jedermann, Pötsch, and Lloyd 2014). In addition to the improvement of IoT, its supporting technologies are making great progress. Internet Protocol version 6 (IPv6) uses 128-bit (16-byte) addresses whose space supports approximately 340 undecillion addresses (Wollschlaeger, Sauter, and Jasperneite 2017). These almost inexhaustible addresses provide tremendous support for the addressing of any number of objects needed in IoT. The coming fifth-generation of cellular networks (5G) brings greater speed (to move more data), lower latency (to be more responsive), and lower energy and cost (to be economical) than ever before (Andrews et al. 2014). 5G architecture is expected to accommodate a wide range of use cases in IoT with higher requirements for latency, resilience, coverage, and bandwidth. However, there is no end to the development of technology and the actual demand. It is a research need to further solve the above tough issues of IoT in Section 4.
Microsimulation analysis for network traffic assignment (MANTA) at metropolitan-scale for agile transportation planning
Published in Transportmetrica A: Transport Science, 2022
Pavan Yedavalli, Krishna Kumar, Paul Waddell
At each timestep, the vehicle's travel time, position, and velocity are updated. MANTA employs a unique traffic atlas concept, akin to a texture atlas in the computer graphics community or a discretization step in signal processing. Each road segment of length l is discretized into different compartments, where is the length of each compartment, in meters, and is specified to be 1 meter in this implementation. Each 1 m compartment of the road is assigned to a specific byte in the computer memory. Hence, a road represented in the computer memory is a contiguous sequence of bytes. Each byte in memory can be occupied by at most one vehicle, and records the velocity of the vehicle and reflects their real position on the road lane (Garcia-Dorado, Aliaga, and Ukkusuri 2014). Each road segment is represented as a new row in the 2D traffic atlas, and ultimately has a different number of bytes depending on its length. The 2D array layout of the road network requires less memory, making it parallelizable on a GPU rather than requiring a graph-based approach. The traffic atlas significantly reduces the computational cost of finding nearby vehicles, as it only involves looking up the status of neighboring cells in the memory array. The lookup scheme on a 2D grid to identify the vehicle's speed and its neighbors' speeds is parallelized with each thread on a GPU querying a specific block of memory address, which significantly speeds up the computation on the GPU. This approach varies from the traditional method, which requires checking the entire edge for neighboring vehicles.