Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2017. / The concept of forward error correction (FEC) coding introduced the capability of achieving near Shannon limit digital transmission with bit error rates (BER) approaching 10-9 for signal to noise power (Eb/No) values as low as 0.7. This brought about the ability to transmit large amounts of data at fast rates on bad/noisy communication channels. In nano-satellites, however, the constraints on power that limit the energy that can be allocated for data transmission result in significantly reduced communication system performance. One of the effects of these constraints is the limitation on the type of channel coding technique that can be implemented in these communication systems. Another limiting factor on nano-satellite communication systems is the limited space available due to the compact nature of these satellites, where numerous complex systems are tightly packed into a space as small as 10x10x10cm. With the miniaturisation of Integrated-Circuit (IC) technology and the affordability of Field-Programmable-Gate-Arrays (FPGAs) with reduced power consumption, complex circuits can now be implemented within small form factors and at low cost. This thesis describes the design, implementation and cost evaluation of a ½-rate convolutional encoder and the corresponding Viterbi decoder on an FPGA for nano-satellites applications. The code for the FPGA implementation is described in VHDL and implemented on devices from the Artix7 (Xilinx), Cyclone V (Intel-fpga), and Igloo2 (Microsemi) families. The implemented channel code has a coding gain of ~3dB at a BER of 10-3. It can be noted that the implementation of the encoder is quite straightforward and that the main challenge is in the implementation of the decoder.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 43-44. / by Eric Stewart Brown. / B.S.

In this work, we consider the possibility and the effectiveness of implementing secure network coding and error-correcting network coding at the same time. Upon achieving this goal, information can be multicast securely to the sink nodes through a noisy network. Toward this end, we propose constructions of such codes and prove their optimality. After that, we extend the idea of generalized Hamming Weight [54] for the classical point-to-point communication channel to linear network coding. We also extend the idea of generalized Singleton bound to linear network coding. We further show that the generalized Hamming weight can completely characterize the security performance of linear code at the source node on a given linear network code. We then introduce the idea of Network Maximum Distance Separable code (NMDS code), which can be shown to play an important role in minimizing the information that an eavesdropper can obtain from the network. The problem of obtaining the optimal security performance is in fact equivalent to the problem of obtaining a Network Maximum Distance Separable code. / Network coding is one of the most important breakthroughs in information theory in recent years. The theory gives rise to a new concept regarding the role of nodes in a communication network. Unlike in existing networks where the nodes act as switches, in the paradigm of network coding, every node in the network can act as an encoder for the incoming information. With this new infrastructure, it is possible to utilize the full capacity of the network where it is impossible to do so without network coding. In the seminar paper by Ahlswede et al. [1] where network coding was introduced, the achievability of the maxflow bound for every single source multicast network by using network coding was also proved. By further exploring the possibility of linear network coding, Cai and Yeung introduced the idea of error-correcting network coding and secure network coding in [7] and [8] respectively. These papers launched another two important research areas in the field of network coding. / Ngai, Chi Kin. / Adviser: Yqung Wai Ho. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3696. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 122-128). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Tsang, Chi Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 66-69). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Statement of Originality --- p.v / Table of Contents --- p.vii / List of Figures --- p.x / Nomenclature --- p.xii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- A Ubiquitous Digital Writing System --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- MEMS Motion Sensing Technology --- p.6 / Chapter 2.2.1 --- Micro-Electro-Mechanical Systems (MEMS) --- p.6 / Chapter 2.2.2 --- Principle of a MEMS Accelerometer --- p.6 / Chapter 2.2.3 --- Principle of a MEMS Gyroscope --- p.7 / Chapter 2.3 --- Architecture of Ubiquitous Digital Writing System --- p.8 / Chapter 2.3.1 --- Micro Inertial Measurement Unit (μlMU) --- p.8 / Chapter 2.3.2 --- Data Transmission Module --- p.10 / Chapter 2.3.3 --- User Interface Software --- p.10 / Chapter 2.4 --- Summary --- p.12 / Chapter 3 --- Calibration of μ-Inertial Measurement Unit --- p.13 / Chapter 3.1 --- Introduction --- p.13 / Chapter 3.2 --- Sources of Error --- p.13 / Chapter 3.2.1 --- Deterministic Errors --- p.13 / Chapter 3.2.2 --- Stochastic Error --- p.14 / Chapter 3.3 --- Calibration of Accelerometers --- p.14 / Chapter 3.4 --- Coordinate Transformation with Gravity Compensation --- p.15 / Chapter 3.4.1 --- Coordinate Transformation --- p.16 / Chapter 3.4.2 --- Attitude Determination --- p.18 / Chapter 3.4.3 --- Gravity Compensation --- p.19 / Chapter 3.5 --- Summary --- p.20 / Chapter 4 --- Zero Velocity Compensation --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Algorithm Description --- p.21 / Chapter 4.2.1 --- Stroke Segmentation --- p.22 / Chapter 4.2.2 --- Zero Velocity Compensation (ZVC) --- p.22 / Chapter 4.3 --- Experimental Results and Discussion --- p.23 / Chapter 4.4 --- Summary --- p.24 / Chapter 5 --- Kalman Filtering --- p.28 / Chapter 5.1 --- Introduction --- p.28 / Chapter 5.2 --- Summary of Kalman filtering algorithm --- p.28 / Chapter 5.2.1 --- System Model --- p.28 / Chapter 5.2.2 --- Initialization --- p.29 / Chapter 5.2.3 --- Time Update --- p.32 / Chapter 5.2.4 --- Measurement Update --- p.33 / Chapter 5.2.5 --- Stroke Segmentation --- p.34 / Chapter 5.3 --- Summary --- p.34 / Chapter 6 --- Error Compensation from Position Feedback --- p.35 / Chapter 6.1 --- Introduction --- p.35 / Chapter 6.2 --- Global Positioning System (GPS) --- p.35 / Chapter 6.3 --- Zero z-axis Kalman Filtering --- p.36 / Chapter 6.3.1 --- Algorithm Implementation --- p.36 / Chapter 6.3.2 --- Experimental Results and Discussion --- p.40 / Chapter 6.4 --- Combined Electromagnetic Resonance (EMR) Position Detection Board and μlMU --- p.43 / Chapter 6.4.1 --- EMR Position Detection System --- p.43 / Chapter 6.4.2 --- A Combined Scheme --- p.44 / Chapter 6.4.3 --- Algorithm Implementation --- p.46 / Chapter 6.4.4 --- Synchronization --- p.50 / Chapter 6.4.5 --- Experimental Results and Discussion --- p.50 / Chapter 6.5 --- Summary --- p.54 / Chapter 7 --- Conclusion --- p.55 / Chapter 7.1 --- Future Work --- p.56 / Chapter 7.1.1 --- Improvement in the μlMU --- p.56 / Chapter 7.1.2 --- Combined Camera Optical Tracking and μlMU --- p.57 / Chapter 7.2 --- Concluding Remarks --- p.58 / Chapter A --- Derivation of Kalman Filtering Algorithm --- p.59 / Chapter A.1 --- Introduction --- p.59 / Chapter A.2 --- Derivation of a Priori State Estimation Equation --- p.60 / Chapter A.3 --- Derivation of a Posteriori State Estimation Equation --- p.60 / Chapter A.4 --- Derivation of a Priori Error Covariance Matrix --- p.61 / Chapter A.5 --- Derivation of the Optimal Kalman Gain --- p.62 / Chapter A.6 --- Derivation of a Posteriori Error Covariance Matrix --- p.63 / Chapter B --- Derivation of Process Noise Covariance Matrix --- p.64 / Bibliography --- p.66 / Publications --- p.69

"October 10th, 2001." / Errata included. / Bibliography: p. 179-185. / xii, 185 p. : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Looks at algorithms and architectures for implementing low-density parity-check codes to achieve reliable communication of digital data over an unreliable channel. Shows that published methods of finding LDPC codes do not result in good codes. Derives a cost metric for measuring short cycles in a graph due to an edge and proposes an algorithm for constructing codes through the minimisation of the cost metric. An encoding algorithm is derived by considering the parity check matrix as a set of linear simultaneous equations. A parallel architecture for implementing LDPC decoders is proposed and the advantages in terms of throughput and power reduction of this architecture are demonstrated through the implementation of 2 LSPC decoders in a 1.5V 0.16[mu]m CMOS process. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 2002

Spreadsheets are a widely used end-user programming tool. Field audits have found that 80-90% of spreadsheets created by end users contain textual and formula errors in spreadsheets. Such errors may have severe negative consequences for users in terms of productivity, credibility, or profits. To solve the problem of spreadsheet errors, researchers have presented manual and automatic error detection. Manual error detection is both tedious and time-consuming, while automatic error detection is limited to only finding some formula error categories such as formula reference errors. Both approaches do not provide the optimum result in error detection. We have tested a new error detection approach by detecting bad smells in spreadsheets, which is an indication that an error might be present. Originally developed for object-oriented programming, examples include the large class, and the lazy class. We have adapted the concept of bad smells to spreadsheets. Each bad smell detector might indicate an issue in the spreadsheet, but the indication is not definitive, since the user must examine the spreadsheet and make a final judgment about whether an error is actually present. We evaluated 11 bad smell detectors by analyzing the true positives against the false positives. The result shows that six detectors can highlight some error categories, such as categorical errors and typographical errors. / Graduation date: 2013

Concurrent error detection (CED) is the detection of errors or faults in a circuit or data path concurrent with normal operation of that circuit. The general approach for CED is to calculate a check symbol for the inputs to the circuit under operation, predict the check symbol that will result for the output of the circuit for those inputs, and compare the predicted check symbol to the one that is actually calculated for the output. If the predicted and actual check symbols are different, an error or fault has been detected. The alternative to this check symbol prediction is to use a second copy of the circuit under operation and compare the results of the two circuits. For some classes of circuits the prediction of the output check symbol can require less circuitry than a second copy of the circuit being tested. Four examples of these types of circuits are examined in this dissertation: Arithmetic Logic Units (ALUs), array multipliers, self-synchronous scrambler-descrambler pairs with their intervening data path, and switch fabrics. Faults in integrated circuits tend to produce unidirectional errors. Unidirectional errors are those in which all of the errors are in the same direction (e.g., 0 to 1 errors) within the block of data covered by a given check symbol. For this reason, codes that are optimized for unidirectional errors are the focus of investigation for most of the applications. In particular, the Bose-Lin codes are examined for those applications where unidirectional errors are expected to be typical. In order to examine the performance of the Bose-Lin codes in one of these applications, it was necessary to determine the theoretical performance for Bose- Lin codes for error rates beyond what had been previously studied. This analysis of Bose-Lin codes with large numbers of "burst" errors also included a further generalization of the codes. / Graduation date: 2002

Based on a Ph.D. thesis in the Dept. of Electrical Engineering, 1968. / Bibliography: p.73-74.

Bibliography: p.76. / Contract no. DA36-039-AMC-03200(E).

Bibliography: p. 87-88. / Contract no. DA36-039-AMC-03200(E).