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Autonomous Byte Stream Randomizer
Net-centric networking environments are often faced with limited resources and must utilize bandwidth as efficiently as possible. In networking environments that span wide areas, the data transmission has to be efficient without any redundant or exuberant metadata. The Autonomous Byte Stream Randomizer software provides an extra level of security on top of existing data encryption methods. Randomizing the data s byte stream adds an extra layer to existing data protection methods, thus making it harder for an attacker to decrypt protected data. Based on a generated crypto-graphically secure random seed, a random sequence of numbers is used to intelligently and efficiently swap the organization of bytes in data using the unbiased and memory-efficient in-place Fisher-Yates shuffle method. Swapping bytes and reorganizing the crucial structure of the byte data renders the data file unreadable and leaves the data in a deconstructed state. This deconstruction adds an extra level of security requiring the byte stream to be reconstructed with the random seed in order to be readable. Once the data byte stream has been randomized, the software enables the data to be distributed to N nodes in an environment. Each piece of the data in randomized and distributed form is a separate entity unreadable on its own right, but when combined with all N pieces, is able to be reconstructed back to one. Reconstruction requires possession of the key used for randomizing the bytes, leading to the generation of the same cryptographically secure random sequence of numbers used to randomize the data. This software is a cornerstone capability possessing the ability to generate the same cryptographically secure sequence on different machines and time intervals, thus allowing this software to be used more heavily in net-centric environments where data transfer bandwidth is limited.
New double-byte error-correcting codes for memory systems
Error-correcting or error-detecting codes have been used in the computer industry to increase reliability, reduce service costs, and maintain data integrity. The single-byte error-correcting and double-byte error-detecting (SbEC-DbED) codes have been successfully used in computer memory subsystems. There are many methods to construct double-byte error-correcting (DBEC) codes. In the present paper we construct a class of double-byte error-correcting codes, which are more efficient than those known to be optimum, and a decoding procedure for our codes is also considered.
Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm
Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively small coding complexity, it is proposed to concatenate a byte oriented unit memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real time minimal byte error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.
Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm
Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively modest coding complexity, it is proposed to concatenate a byte-oriented unit-memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real-time minimal-byte-error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.
Reed Solomon codes for error control in byte organized computer memory systems
A problem in designing semiconductor memories is to provide some measure of error control without requiring excessive coding overhead or decoding time. In LSI and VLSI technology, memories are often organized on a multiple bit (or byte) per chip basis. For example, some 256K-bit DRAM's are organized in 32Kx8 bit-bytes. Byte oriented codes such as Reed Solomon (RS) codes can provide efficient low overhead error control for such memories. However, the standard iterative algorithm for decoding RS codes is too slow for these applications. Some special decoding techniques for extended single-and-double-error-correcting RS codes which are capable of high speed operation are presented. These techniques are designed to find the error locations and the error values directly from the syndrome without having to use the iterative algorithm to find the error locator polynomial.
Persistent Octrees for Parallel Mesh Refinement Through Non-Volatile Byte-Addressable Memory
We report that octree-based mesh adaptation has enabled simulations of complex physical phenomena. Existing meshing algorithms were proposed with the assumption that computer memory is volatile. Consequently, for failure recovery, the in-core algorithms need to save memory states as snapshots with slow file I/Os. The out-of-core algorithms store octants on disks for persistence. However, neither of them was designed to leverage unique characteristics of non-volatile byte-addressable memory (NVBM). In this paper, we propose a novel data structure Distributed Persistent Merged octree (DPM-octree) for both meshing and in-memory storage of persistent octrees using NVBM. It is a multi-version data structure and can recover from failures using its earlier persistent version stored in NVBM. In addition, we design a feature-directed sampling approach to help dynamically transform the DPM-octree layout for reducing NVBM-induced memory write latency. DPM-octree uses parity trees which are created using erasure coding and stored in NVBM to support low-latency in-memory octant recovery after data loss. DPM-octree has been successfully integrated with Gerris software for simulation of fluid dynamics. Finally, our experimental results with real-world scientific workloads show that DPM-octree scales up to 1.1 billion mesh elements with 1000 processors on the Titan supercomputer.
Short, unit-memory, Byte-oriented, binary convolutional codes having maximal free distance
It is shown that (n sub 0, k sub 0) convolutional codes with unit memory always achieve the largest free distance among all codes of the same rate k sub 0/n sub 0 and same number 2MK sub 0 of encoder states, where M is the encoder memory. A unit-memory code with maximal free distance is given at each place where this free distance exceeds that of the best code with k sub 0 and n sub 0 relatively prime, for all Mk sub 0 less than or equal to 6 and for R = 1/2, 1/3, 1/4, 2/3. It is shown that the unit-memory codes are byte-oriented in such a way as to be attractive for use in concatenated coding systems.
Simulations for Full Unit-memory and Partial Unit-memory Convolutional Codes with Real-time Minimal-byte-error Probability Decoding Algorithm
A program which was written to simulate Real Time Minimal-Byte-Error Probability (RTMBEP) decoding of full unit-memory (FUM) convolutional codes on a 3-bit quantized AWGN channel is described. This program was used to compute the symbol-error probability of FUM codes and to determine the signal to noise (SNR) required to achieve a bit error rate (BER) of 10 to the minus 6th power for corresponding concatenated systems. A (6,6/30) FUM code, 6-bit Reed-Solomon code combination was found to achieve the required BER at a SNR of 1.886 dB. The RTMBEP algorithm was then modified for decoding partial unit-memory (PUM) convolutional codes. A simulation program was also written to simulate the symbol-error probability of these codes.
Performance Simulation for Unit-memory Convolutional Codes with Byte-oriented Viterbi Decoding Algorithm
A software package developed to simulate the performance of the byte-oriented Viterbi decoding algorithm for unit-memory (UM) codes on both 3-bit and 4-bit quantized AWGN channels is described. The simulation is shown to require negligible memory and less time than that for the RTMBEP algorith, although they both provide similar performance in terms of symbol-error probability. This makes it possible to compute the symbol-error probability of large codes and to determine the signal-to-noise ratio required to achieve a bit error rate (BER) of 0.000001 for corresponding concatenated systems. A (7, 10/48) UM code, 10-bit Reed-Solomon code combination achieves the required BER at 1.08 dB for a 3-bit quantized channel and at 0.91 dB for a 4-bit quantized channel.
Peripheral Equipment Interchanges Bytes Of Data
Method for conversion of data formats between incompatible computers reduces conversion time by 80 percent. Transposes high and low bytes of word so data from computer A match storage format of computer B. Two interface circuit boards convert data. Such boards ordinarily used for communication between computers. In format-conversion application, cable connecting boards modified so high-8-bit and low-8-bit data lines interchanged with result data words in proper format for computer B.
Byte select cache compression
Techniques are disclosed for designing cache compression algorithms that control how data in caches are compressed. The techniques generate a custom “byte select algorithm” by applying repeated transforms applied to an initial compression algorithm until a set of suitability criteria is met. The suitability criteria include that the “cost” is below a threshold and that a metadata constraint is met. The “cost” is the number of blocks that can be compressed by an algorithm as compared with the “ideal” algorithm. The metadata constraint is the number of bits required for metadata.
Powering America’s Non-Powered Dams, One Byte at a Time
Compared with other hydropower development opportunities, powering underutilized water infrastructure provides an attractive option for expanding clean energy in the 21st century. Research conducted by the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) targets hydropower development at non-powered dams (NPD) from several angles and is increasingly looking to new and improved data sources to inform decision-making. Sponsored by the DOE Water Power Technologies Office (WPTO), ORNL’s latest efforts build on a decade of work to advance understanding and data access for improved decision-making. Outcomes of the most recent research are geared toward better informing NPD stakeholders across a wide spectrum of interests. Furthermore, these outcomes are paving the way for additional federal investments to power NPDs in the U.S., including investments in innovative new technologies that incorporate ecological and social objectives while achieving cost reductions, commercialization and deployment. Here, this research centers around improving the breadth and accessibility of NPD data. These new digital information tools and associated tools can lead to a better decision-making process and support new hydropower development, helping to power America’s NPDs, one “byte” at a time.
FY 22 Project Name: Boots versus Bytes
IAEA has increasingly leveraged remote data transfer, amplifying the effectiveness of inspectors and analysts by allowing them to view data from Headquarters rather than requiring on-site activities. We propose that there may be even more opportunities to shift the international nuclear safeguards paradigm to remote activities through the implementation of enhanced data sharing and analysis.
Short unit-memory byte-oriented binary convolutional codes having maximal free distance
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On Nibbles and Bytes: The Conundrum of Memory for Space Systems - NASA Electronic Parts and Packaging (NEPP) and Efforts in Memories
Radiation requirements and trends. TID: 1) >90% of NASA applications are < 100 krads-Si in piecepart requirements. a) Many commercial devices (NVM and SDRAMs) meet or come close to this. b) Charge pump TID tolerance has improved an order magnitude over the last 10 years. 2) There are always a few programs with higher level needs and, of course, defense needs SEL: 1) Prefer none or rates that are considered low risk. a) Latent damage is a bear to deal with. 2) As we re packing cells tighter and even with lower Vdd, we re seeing SEL on commercial devices regularly (<90nm). a) Often in power conversion, I/O, or control areas. SEU: 1) It s not the bit errors, it s the SEFIs errors that are the biggest issues. a) Scrubbing concerns for risk, power, speed.
System Evaluation of the Intel Optane byte-addressable NVM
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Determining the locations of the various CIRC recording format information blocks (user data blocks, C2 and C1 words and EFM frames) on a recorded compact disc
Just prior to its being EFM modulated (i.e., converted to eight-to-fourteen channel data by the EFM encoder) and written to a Compact Disc (CD), information that passes through the CIRC Block Encoder is grouped into 33-byte blocks referred to as EFM frames. Twenty four of the bytes that make up a given EFM frame are user data that was input into the CIRC encoder at various (different) times, 4 of the bytes of this same EFM frame were created by the C2 ECC encoder (each at a different time), and another 4 were created by the C1 ECC encoder (again, each at a different time). The one remaining byte of the given EFM frame, which is known as the EFM frame C&D (for Control & Display) byte, carries information that identifies which portion of the current disc program track the given EFM frame belongs to and also specifies the location of the given EFM frame on the disc (in terms of a time stamp that has a resolution of l/75th second, or 98 EFM frames). (Note: since the program track and time information is stored as a 98-byte word, a logical group consisting of 98 consecutive EFM frames must be read, and their respective C&D bytes must be catenated and decoded, before the program track identification and time position information that pertains to the entire block of 98 EFM frames can be obtained.) The C&D byte is put at the start (0th byte) of an EFM frame in real time; its placement completes the construction of the EFM frame - it is assigned just before the EFM frame enters the EFM encoder. Four distinct blocks of data are referred to: 24-byte User Input Data Blocks; 28-byte C2 words; 32-byte C1 words; and 33-byte EFM frames.