Large Block Sizes Research Articles

Everyday urbanism in Beijing’s edge cities: on spatial and experience patterns

ABSTRACT The concentration of people in mega agglomerations in China has been challenging urban governance to reduce daily commuting by planning and investing in new edge cities. The main objective of this paper is to investigate everyday urbanism in two recently developed edge cities in Beijing by considering spatial conditions as well as resulting activity and experience patterns. The applied mapping surveys include land use distributions and density calculations via evaluating GIS data as well as field observations to investigate the typical experiences of 32 representative neighborhood walks. One of the main findings is the overall dependency of neighborhoods on rigid grid planning defining urbanism with large block sizes, resulting in walking experiences with a majority being dominated by compound walls along main roads and only 15% of walks being experienced along active frontages despite rather high urban densities with up to 300 residents per hectare.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Automated Differential-Linear Cryptanalysis for AND-RX Ciphers

Differential and linear cryptanalysis are two important methods to evaluate the security of block ciphers. Building on these two methods, differential-linear (DL) cryptanalysis was introduced by Langford and Hellman in 1994. This cryptanalytic method has been not only extensively researched but also proven to be effective. In this paper, a security evaluation framework for AND-RX ciphers against DL cryptanalysis is proposed, which is denoted as K6. In addition to modeling the structure of all the possible differential trails and linear trails at the bit level, we introduce a method to calculate this structure round by round. Based on this approach, an automatic algorithm is proposed to construct the DL distinguisher. Unlike previous methods, K6 uses a truncated differential and a linear hull instead of a differential characteristic and a linear approximation, which brings the bias of the DL distinguisher close to the experimental value. To validate the effectiveness of the framework, K6 is applied to Simon and Simeck, which are two typical AND-RX ciphers. With the automatic algorithm, we discover an 11-round DL distinguisher of Simon32 with bias 2−14.89 and a 12-round DL distinguisher of Simeck32 with bias 2−14.89. Moreover, the 14-round DL distinguisher of Simon48 with bias 2−22.30 is longer than the longest DL distinguisher currently known. In addition, the framework K6 shows advantages when analyzing ciphers with large block sizes. As far as we know, for Simon64/96/128 and Simeck48/64, the first DL distinguishers are obtained with our framework. The DL distinguishers are 16, 23, 32, 17, and 22 rounds of Simon64/96/128 and Simeck48/64 with bias 2−24.31, 2−47.57, 2−60.75, 2−22.54, and 2−31.41, respectively. To prove the correctness of distinguishers, experiments on Simon32 and Simeck32 have been performed. The experimental bias are 2−13.76 and 2−14.82, respectively. Comparisons of the theoretical and experimental results show good agreement.

A Computationally Efficient Technique Using Characteristic Basis Functions and Large Block Sizes

A Computationally Efficient Technique Using Characteristic Basis Functions and Large Block Sizes

Interpolation of scheduled simulation results for real-time auralization of moving sources

A central part of auralization is the consideration of realistic sound propagation effects. This can be achieved using computationally efficient physics-based simulations based on the principle geometrical acoustics. When considering complex effects, e.g. curved propagation due to atmospheric refraction, those simulations can be computationally demanding. This can become the bottleneck for real-time auralizations, as the run-time exceeds the duration of one audio block even for large block sizes. A solution is to schedule the simulations into a separate thread. However, this leads to an irregular update rate which is lower than the rate of the audio blocks. Consequently, the output signal can contain audible artifacts. This especially holds when considering the Doppler effect for dynamic scenarios with fast moving sources, like aircraft. This paper introduces a method for interpolating, and thereby upsampling, the results of scheduled simulations in an auralization context in order to avoid such artifacts. The method is applied to an aircraft flyover auralization considering curved sound propagation in an inhomogeneous, moving atmosphere. Using this method, it is possible to auralize such scenarios in real time.

Secure Seaport Management System using Blockchain Technology

Worldwide, shipping documents are still primarily created and handled in the traditional paper manner. Processes taking place in shipping ports as a result are time-consuming and heavily dependent on paper. Shipping documents are particularly susceptible to paperwork fraud because they involve numerous parties with competing interests. With the aid of smart contracts, a distributed, shared, and append-only ledger provided by blockchain technology allows for the addition of new records. In order to increase maritime transport and port efficiency and promote economic development, this paper examines current maritime sector developments in Iraq and offers a paradigm to secure the management system based on a hyper-ledger fabric blockchain platform. The performance evaluation of the proposed system implies two scenarios: one organization and two organizations by examining throughput and latency. High transaction transmission rates on large block sizes produced positive results. Similarly, employing a small block size and higher send rates results in poor performance. Additionally, it was noted that throughput will decrease and latency will rise as the number of organizations increases. Block size and block timeouts should be high in applications with many concurrent transactions in order to maintain good throughput.

Lithium-Ion Diffusivity Increases with Block Size in Niobium Tungsten Oxide Shear Structures

Nb16W5O55 emerged as a high-rate anode material for Li-ion batteries in 2018 (Griffith et al.,Nature 2018, 559 (7715), 556–563). This exciting discovery ignited research in Wadsley-Roth (W-R) compounds, but systematic experimental studies have not focused on how to tune material chemistry and structure to achieve desirable properties for energy storage applications. In this work, we systematically investigate how structure and composition influences capacity, Li-ion diffusivity, charge-discharge profiles, and capacity loss in a series of niobium tungsten oxide W-R compounds: (3×4)-Nb12WO33, (4×4)-Nb14W3O44, and (4×5)-Nb16W5O55. Potentiostatic intermittent titration (PITT) data confirmed that Li-ion diffusivity increases with block size, which can be attributed to an increasing number of tunnels for Li-ion diffusion. The small (3×4)-Nb12WO33 block size compound with preferential W ordering on tetrahedral sites exhibits single electron redox and, therefore, the smallest measured capacity despite having the largest theoretical capacity. This observation signals that introducing cation disorder (W occupancy at the octahedral sites in the block center) is a viable strategy to access multi-electron redox behavior in (3×4) Nb12WO33. The asymmetric block size compounds (i.e., (3×4) and (4×5) blocks) exhibit the greatest capacity loss after the first cycle, possibly due to Li-ion trapping at a unique low energy pocket site along the shear plane. Finally, the slope of the charge-discharge profile increases with increasing block size, likely because the total number of energy-equivalent Li-ion binding sites also increases. This unfavorable characteristic prohibits the large block sizes from delivering constant power at a fixed C-rate more so than the smaller block sizes. Based on these findings, we discuss design principles for Li-ion insertion hosts made from W-R materials.

A Second-Order Finite Volume Method for Field-Scale Reservoir Simulation

Subsurface reservoirs are large complex systems. Reservoir flow models are defined on complex grids that follow geology with relatively large block sizes to make consistent simulations feasible. Reservoir engineers rely on established reservoir simulation software to model fluid flow. Nevertheless, fluid front position inaccuracies and front smearing on large grids may cause significant errors and make it hard to predict hydrocarbon production efficiency. We investigate higher-order methods that reduce these undesired effects without refining the grid, thus making reservoir simulation more accurate and robust. For this paper, we implemented a second-order finite volume method with linear programming (LP) reconstruction in the open-source industry-grade reservoir simulator OPM Flow (part of the open porous media initiative, OPM). We benchmark it against the first-order method on full-scale cases with standard coarse and refined grids. We prepared open refined-grid models of a synthetic reservoir with an unstructured grid and refined Norne field example. Our results confirm that the LP method predicts front positions as accurately as the first-order method on the refined grid for problems dominated by transport. These include the water alternating gas scenario on the synthetic reservoir and piston-type injection on the Norne field. Moreover, we study the gains from the LP method for CO_{2} injection problems on the Norne field with full multi-phase complexity beyond transport. We observe the relevant difference between the first- and the second-order methods in these cases. However, in some configurations, the reservoir complexity overshadows the gains from the second-order methods.

Performance Analysis of a Hybrid Security Algorithm for Secure Cloud Environment

Encryption algorithms are critical in ensuring the security of information in today's rapidly evolving internet and network applications. These algorithms are especially important in ensuring information security for internet-based dynamic applications and storage methods like cloud computing, which require high reliability, scalability, and autonomy to enable ubiquitous access. Given the prevalence of cloud computing in our everyday routines, data security has emerged as a major concern, particularly for non-technical users. To address this concern, the effectiveness of two widely used symmetric key encryption algorithms, AES and Blowfish, in terms of throughput, power consumption, and encryption speed, has been analyzed and compared. Based on the simulation results, it is clear that AES outperforms Blowfish when security flaws are put into consideration. It should be noted, however, that AES requires more processing power. As a result, a proposed efficient hybrid security algorithm seeks to combine the benefits of both approaches in an encryption system, thereby improving data security in a cloud environment while optimizing resource utilization. According to the performance analysis, the proposed hybrid approach is an excellent solution for encrypting and decrypting files with large block sizes and longer keys, as well as defending against Man in the Middle (MitM) attacks.

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

Structure–Property Relationships in High-Rate Anode Materials Based on Niobium Tungsten Oxide Shear Structures

Nb16W5O55 emerged as a high-rate anode material for Li-ion batteries in 2018 [Griffith et al., Nature2018, 559 (7715), 556–563]. This exciting discovery ignited research in Wadsley–Roth (W–R) compounds, but systematic experimental studies have not focused on how to tune material chemistry and structure to achieve desirable properties for energy storage applications. In this work, we systematically investigate how structure and composition influences capacity, Li-ion diffusivity, charge–discharge profiles, and capacity loss in a series of niobium tungsten oxide W–R compounds: (3 × 4)-Nb12WO33, (4 × 4)-Nb14W3O44, and (4 × 5)-Nb16W5O55. Potentiostatic intermittent titration (PITT) data confirmed that Li-ion diffusivity increases with block size, which can be attributed to an increasing number of tunnels for Li-ion diffusion. The small (3 × 4)-Nb12WO33 block size compound with preferential W ordering on tetrahedral sites exhibits single electron redox and, therefore, the smallest measured capacity despite having the largest theoretical capacity. This observation signals that introducing cation disorder (W occupancy at the octahedral sites in the block center) is a viable strategy to assess multi-electron redox behavior in (3 × 4) Nb12WO33. The asymmetric block size compounds [i.e., (3 × 4) and (4 × 5) blocks] exhibit the greatest capacity loss after the first cycle, possibly due to Li-ion trapping at a unique low energy pocket site along the shear plane. Finally, the slope of the charge–discharge profile increases with increasing block size, likely because the total number of energy-equivalent Li-ion binding sites also increases. This unfavorable characteristic prohibits the large block sizes from delivering constant power at a fixed C-rate more so than the smaller block sizes. Based on these findings, we discuss design principles for Li-ion insertion hosts made from W–R materials.

Beyond 100 Gbit/s Pipeline Decoders for Spatially Coupled LDPC Codes

Low-density parity-check (LDPC) codes are a well-established class of forward error correction codes that provide excellent error correction performance for large code block sizes. However, for throughputs toward 1 Tbit/s, as expected for B5G systems, state-of-the-art LDPC soft decoders are restricted to short code block sizes of several hundred to thousands of bits due to routing congestion challenges, limiting the overall communications performance of the transmission system. Spatially coupled LDPC (SC-LDPC) codes and respective sliding window decoding methods show the potential to overcome these block size restrictions. However, in contrast to conventional LDPC codes little literature exists on the efficient hardware implementation of respective high-throughput decoders. In this work, we present the first in-depth investigation on the implementation of SC-LDPC decoders for throughputs beyond 100 Gbit/s. For an N=51328, R=0.8 terminated SC-LDPC code with sub-block size c=640 and coupling width m_s=1, we explore various design trade-offs, including row- and column-wise decoding, non-overlapping and overlapping window scheduling, and processor pipelining. To the best of our knowledge, this is the first description of a column-wise SC-LDPC decoding architecture in the literature. We complement the algorithmic investigation with the virtual silicon implementation of all presented decoders in a 22nm FD-SOI technology.

A high-performance two-dimensional transform architecture of variable block sizes for the VVC standard.

The versatile video coding standard H.266/VVC release has been accompanied with various new contributions to improve the coding efficiency beyond the high-efficiency video coding (HEVC), particularly in the transformation process. The adaptive multiple transform (AMT) is one of the new tools that was introduced in the transform module. It involves five transform types from the discrete cosine transform/discrete sine transform families with larger block sizes. The DCT-II has a fast computing algorithm, while the DST-VII relies on a complex matrix multiplication. This has led to an additional computational complexity. The approximation of the DST-VII can be used for the transform optimization. At the hardware level, this method can provide a gain in power consumption, logic resources use and speed. In this paper, a unifed two-dimensional transform architecture that enables exact and approximate DST-VII computation of sizes 8times 8, 8times 16, 8times 32, 16times 8, 16times 16, 16times 32, 32times 8, 32times 16 and 32times 32 is proposed. The exact transform computation can be processed using either multipliers or the MCM algorithm, while the approximate transform computation is based on additions and bit-shifting operations. All the designs are implemented under the Arria 10 FPGA device. The synthesis results show that the proposed design implementing the approximate transform matrices is the most efficient method with only 4% of area consumption. It reduces the logic utilization by more than 65% compared to the multipliers-based exact transform design, while about 53% of hardware cost saving is obtained when compared to the MCM-based computation. Furthermore, the approximate-based 2D transform architecture can operate at 78 MHz allowing a real-time coding for 2K and 4K videos at 100 and 25 frames/s, respectively.

Performance Evaluation of E-Voting Based on Hyperledger Fabric Blockchain Platform

Permissioned blockchain platforms have become more prevalent in a wide range of applications. These, such as hyperledger fabric platforms, are sensitive to latency and throughput. In this work, the E-voting case study adopts a hyperledger fabric platform where performance evaluation has been studied in terms of scalability, latency, throughput, CPU usage, and memory allocation. Three scenarios were performed with varying transaction rates, block size, and organizations. Another two scenarios were performed, first with varying block timeout and second, measuring the impact of CPUs and memory allocation on the proposed fabric’s entities (peers, orderer, couchDB, chaincode, etc.). The result shows that an increase in block size will significantly affect metrics such as latency and throughput. Good results were obtained with high transaction send rates on large block size. Similarly, low performance is obtained using a small block size with increased send rates. Also, it was noticed that increasing the number of organizations will increase latency and decrease the throughput. Therefore, in applications with a large number of concurrent transactions, to maintain high throughput, block timeouts and block size should be large. On The other hand, the number of CPUs and amount of memory allocation would impact hyperledger fabric performance.

Genome-wide association mapping for component traits of drought and heat tolerance in wheat.

Identification of marker trait association is a prerequisite for marker-assisted breeding. To find markers linked with traits under heat and drought stress in bread wheat (Triticum aestivum L.), we performed a genome-wide association study (GWAS). GWAS mapping panel used in this study consists of advanced breeding lines from the IARI stress breeding programme produced by pairwise and complex crosses. Phenotyping was done at multi locations namely New Delhi, Karnal, Indore, Jharkhand and Pune with augmented-RCBD design under different moisture and heat stress regimes, namely timely sown irrigated (IR), timely sown restricted irrigated (RI) and late sown (LS) conditions. Yield and its component traits, viz., Days to Heading (DH), Days to Maturity (DM), Normalized Difference Vegetation Index (NDVI), Chlorophyll Content (SPAD), Canopy temperature (CT), Plant Height (PH), Thousand grain weight (TGW), Grain weight per spike (GWPS), Plot Yield (PLTY) and Biomass (BMS) were phenotyped. Analysis of variance and descriptive statistics revealed significant differences among the studied traits. Genotyping was done using the 35k SNP Wheat Breeder's Genotyping Array. Population structure and diversity analysis using filtered 10,546 markers revealed two subpopulations with sufficient diversity. A large whole genome LD block size of 7.15 MB was obtained at half LD decay value. Genome-wide association search identified 57 unique markers associated with various traits across the locations. Twenty-three markers were identified to be stable, among them nine pleiotropic markers were also identified. In silico search of the identified markers against the IWGSC ref genome revealed the presence of a majority of the SNPs at or near the gene coding region. These SNPs can be used for marker-assisted transfer of genes/QTLs after validation to develop climate-resilient cultivars.

The Plat of Zion and Urban Development in Salt Lake City

The Plat of Zion and Urban Development in Salt Lake City

An efficient hybrid hash based privacy amplification algorithm for quantum key distribution

Privacy amplification (PA) is an essential part in a quantum key distribution (QKD) system, distilling a highly secure key from a partially secure string by public negotiation between two parties. The optimization objectives of privacy amplification for QKD are large block size, high throughput and low cost. For the global optimization of these objectives, a novel privacy amplification algorithm is proposed in this paper by combining multilinear-modular-hashing and modular arithmetic hashing. This paper proves the security of this hybrid hashing PA algorithm within the framework of both information theory and composition security theory. A scheme based on this algorithm is implemented and evaluated on a CPU platform. The results on a typical CV-QKD system indicate that the throughput of this scheme ( $$261\,\mathrm{Mbps}@2.6\times 10^8$$ input block size) is twice higher than the best existing scheme ( $$140\,\mathrm{Mbps}@1\times 10^8$$ input block size). Moreover, this scheme is implemented on a mobile CPU platform instead of a desktop CPU or a server CPU, which means that this algorithm has a better performance with a much lower cost and power consumption.

Glaciotectonic disintegration of roches moutonnées during glacial ripping in east Sweden

ABSTRACT Roches moutonnées are typical landforms of glacial erosion developed in hard rocks, with an asymmetric profile caused by abrasion and lee-side plucking. In eastern Sweden, some roches moutonnées show extensive damage, including open fractures, disintegration into blocks, fracture caves and short boulder trains. Disintegration increases along ice-flow directions during deglaciation of the last Weichselian Fennoscandian Ice Sheet, indicating a subglacial origin: limited edge rounding can be explained by a combination of hard rock, slow abrasion rates and disintegration just prior to deglaciation. The roches moutonnées initially developed in kernels of gneissic rocks with a wide fracture spacing (large block size) and interlocking fracture pattern, and hence high overall rock mass strength. Dilated fractures and ‘fracture caves’ occur up to 15 m below the ice-bed interface. It is proposed that hydraulic jacking by overpressured water opened up the rock mass along pre-existing fractures. Jacking reduced rock mass strength, allowing glaciotectonic deformation of the roches moutonnées. Uneven hydraulic jacking led to uplift of individual fracture-bound blocks above the pre-existing smooth, abraded surface of the roches moutonnées, creating blunt, step-like edges. These edges allowed high ice pushing forces to act on large blocks: where blocks extend into the deeper rock mass, they further aided the disintegration of the rock mass. The disintegrated roches moutonnées can be regarded as transient features between intact bedrock and complete disintegration into boulders. The jacking-disintegration-transport sequence is characteristic of glacial ripping and very different from classic lee-side plucking.

An LDPC Encoder Architecture With Up to 47.5 Gbps Throughput for DVB-S2/S2X Standards

Low-Density Parity-Check (LDPC) code is a type of forward error-correction code with excellent performance, and has been widely used in many modern communication standards. The second-generation satellite broadcasting standard (DVB-S2) and its extension (DVB-S2X) adopt a special Irregular Repeated Accumulate (IRA) LDPC code as inner coding scheme. However, due to the large block size, most of the architectures proposed so far use Random Access Memory (RAM) to store and update the encoding results, and the delay caused by address-controlled read and write operations and barrel shift during computation inevitably limits the upper bound of encoder throughput. In this paper, by extracting the periodicity of the parity-check matrix, we introduce a fast encoding algorithm that can efficiently process the multiplication of the information sequence and a large-dimensional sparse matrix, and propose an encoder architecture with low encoding delay and high throughput. The proposed architecture has been implemented and tested on a Xilinx Kintex-7 FPGA, and the result show that the encoder architecture can achieve the highest throughput of 47.5 Gbps at a clock frequency of 280 MHz.

Radix-$N$ Algorithm for Computing $N^{2^{n}}$-Point DFT Approximations

The ever increasing technological demand for the DFT computation poses several challenges both to theory and hardware realization. The design of usual fast Fourier transform (FFT) algorithms seems to have reached a stage of diminishing returns in terms of performance. Alternatively, approximate transform methods have been demonstrated to provide substantial gains in terms of energy-efficiency and performance by tolerating small inaccuracies in the results. In this paper, we present a transform scaling method variant of the Cooley-Tukey algorithm to obtain DFT approximations of large blocksize. The proposed method scales up a given <i>N</i>-point transformation to an <i>N</i>²-point transformation. Such scaling can be successively applied leading to <inline-formula><tex-math notation="LaTeX">$\mathop {{N}^2}\nolimits^n $</tex-math></inline-formula>-point transformations. We have fully presented the 32⁴-point DFT approximation which stems from a multiplierless 32-point DFT approximation. The proposed approximation is equipped with a fast algorithm; we also supply the arithmetic complexity assessment and an error analysis.