Shape Coding Research Articles

Characterizing directional traffic-induced wear of road pavements

Traffic-induced wear of road pavements is known to possess directional characteristics. Quantitative characterization of wear directionality in terms of surface texture properties has practical significance in the study of wear mechanisms and driving safety analysis. However, the pavement texture parameters and indices in use today are unable to reveal the directional characteristics of pavement wear. This study proposed a texture statistic, the Directional Wear Index (DWI), to measure pavement wear directionality based on a laser scanned image of a pavement surface. First, macrotexture peaks that were likely to be subjected to traffic abrasive actions were identified as primary peaks using the concept of topographic prominence. Next, a slope ratio was calculated to characterize the shape of a primary peak. DWI was determined as a function of the prominence-weighted sum of the shape codes of all the primary peaks of the surface analyzed. The calculated value of DWI defines both the direction and the magnitude of directional wear of the pavement wear. The proposed concept and DWI calculation procedure were validated using field measured data of actual in-service pavement segments with known directional wear characteristics.

Graph-Guided Deformation for Point Cloud Completion

For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is generated using the shape priorly learnt by the networks. However, such models are undesirably biased towards prior average objects and inherently limited to fit geometry details. In this letter, we propose a Graph-Guided Deformation Network, which respectively regards the input data and intermediate generation as controlling and supporting points, and models the optimization guided by a graph convolutional network(GCN) for the point cloud completion task. Our key insight is to simulate the least square Laplacian deformation process via mesh deformation methods, which brings adaptivity for modeling variation in geometry details. By this means, we also reduce the gap between the completion task and the mesh deformation algorithms. As far as we know, we are the first to refine the point cloud completion task by mimicing traditional graphics algorithms with GCN-guided deformation. We have conducted extensive experiments on both the simulated indoor dataset ShapeNet, outdoor dataset KITTI, and our self-collected autonomous driving dataset Pandar40. The results show that our method outperforms the existing state-of-the-art algorithms in the 3D point cloud completion task.

An Improved Chinese String Comparator for Bloom Filter Based Privacy-Preserving Record Linkage

With the development of information technology, it has become a popular topic to share data from multiple sources without privacy disclosure problems. Privacy-preserving record linkage (PPRL) can link the data that truly matches and does not disclose personal information. In the existing studies, the techniques of PPRL have mostly been studied based on the alphabetic language, which is much different from the Chinese language environment. In this paper, Chinese characters (identification fields in record pairs) are encoded into strings composed of letters and numbers by using the SoundShape code according to their shapes and pronunciations. Then, the SoundShape codes are encrypted by Bloom filter, and the similarity of encrypted fields is calculated by Dice similarity. In this method, the false positive rate of Bloom filter and different proportions of sound code and shape code are considered. Finally, we performed the above methods on the synthetic datasets, and compared the precision, recall, F1-score and computational time with different values of false positive rate and proportion. The results showed that our method for PPRL in Chinese language environment improved the quality of the classification results and outperformed others with a relatively low additional cost of computation.

Decarbonizing via disparities: Problematizing the relationship between social identity and solar energy transitions in the United States

As solar adoption across the United States continues to grow, so do the gaps between rural and urban communities in how they choose to embrace these technologies, leading to serious questions of social justice and equity by researchers and policymakers alike. While recent studies have examined the racial and social justice elements of solar adoption alongside institutions' role in shaping pro-solar policies, codes, and code enforcement, an opportunity exists to discuss how the place and composition of the body politic in terms of race/ethnicity and rurality exists. This paper establishes a methodology for examining location and body politic composition concerning adopting all types of solar (residential, non-residential, utility-scale), utilizing the State of Georgia as a case study. Results indicate that the approach yields useful and informative findings; namely, there is a significant difference in adopting non-residential and utility-scale solar between rural and urban counties. We conclude by discussing further opportunities to expand on this analysis and the impact of assessing solar adoption in terms of value alignment between a body politic and the policies that shape the adoption of sustainable energy technologies. Combining solar adoption information for the State of Georgia with Census data, this study compares solar adoption trends across counties–grouped by urban/rural classification and racial and ethnic majority.

MOLTR: Multiple Object Localization, Tracking and Reconstruction From Monocular RGB Videos

Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this letter, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localize, track and reconstruct multiple rigid objects in an online fashion when a RGB camera captures a video of the surrounding. Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localize objects of interest and extract their shape codes that represent the object shape in a learnt embedding space. Detections are then merged to existing objects in the map after data association. Motion state (i.e., kinematics and the motion status) of each object is tracked by a multiple model Bayesian filter and object shape is progressively refined by fusing multiple shape code. We evaluate localization, tracking and reconstruction on benchmarking datasets for indoor and outdoor scenes, and show superior performance over previous approaches.

Shaping duo binary turbo-coded BICM scheme for JPWL image transmission using a link adaptation strategy over wireless channels

In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.

An Unambiguous Synchronization Scheme for GNSS BOC Signals Based on Reconstructed Correlation Function.

Binary offset carrier (BOC) modulation is a new modulation method that has been gradually applied to the Global Satellite Navigation System (GNSS) in recent years. However, due to the multi-peaks in its auto-correlation function (ACF), it will incur a false lock and generate synchronization ambiguous potentially. In this paper, an unambiguous synchronization method based on a reconstructed correlation function is proposed to solve the ambiguity problem. First, through the shape code vector constructed in this paper, the general cross-correlation function (CCF) expression of the BOC modulated signal will be obtained. Based on the features of the signal correlation function, it is decomposed into a matrix form of trigonometric functions. Then, it generates two local signal waves using a specific method, then the proposed method is implemented to obtain a no-side-peak correlation function by reconstructing the cross-correlation between the received signal and the two local signals. Simulations showed that it fully eliminates the side-peak threat and significantly removes the ambiguity during the synchronization of the BOC signals. This paper also gives the improved structure of acquisition and tracking. The detailed theoretical deduction of detection probability and code tracking error is demonstrated, and the corresponding phase discrimination function is given. In terms of de-blurring ability and detection probability performance, the proposed method outperformed other conventional approaches. The tracking performance was superior to the comparison methods and the phase discrimination curve only had a zero-crossing, which successfully removed the false lock points. In addition, in multipath mitigation, it outperformed the ACF of the BOC signal, and performs as well as the autocorrelation side-peak cancellation technique (ASPeCT) for BOC(kn,n) signals.

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer-assisted liver surgery programs. Many liver segmentation algorithms are very sensitive to fuzzy boundaries and heterogeneous pathologies, especially when the data are scarce. To solve these problems, we propose an automatic liver segmentation framework based on three-dimensional (3D) convolutional neural networks with a hybrid loss function. Two networks are incorporated in our method with the first being a liver shape autoencoder that is trained to obtain compressed codes of liver shapes, and the second being a liver segmentation network that is trained with a hybrid loss function. The design of the hybrid loss function is comprised of three parts. The first part is an adaptively weighted cross-entropy loss, which pays more attention to misclassified pixels. The second part is an edge-preserving smoothness loss, which guarantees that the adjacent pixels with the same label have similar outputs, while dissimilar for pixels with different labels. The third part of the loss is a shape constraint to model high-level structural differences based on the learned shape codes. Both networks use 3D operations for data processing. In our experiments, data augmentation is performed at both the training and the test stage. We extensively evaluated our method on two datasets: the Segmentation of the Liver Competition 2007 (Sliver07), and the Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. Finally, with only 20 training scans, we achieved the best score of 82.55 on the Sliver07 challenge, and a score of 83.02 on the CHAOS challenge. In this study, we proposed a novel hybrid loss to overcome the difficulties in liver segmentation. The quantitative and qualitative results demonstrate that our method is highly suited for pathological liver segmentation, even when trained with a small dataset.

Shaping code in conjunction with DCT for PAPR reduction in multicarrier system

Nonlinear behavior and power efficiency of the Power Amplifier (PA) contradictorily depend on the input signal amplitude distribution. The transmitted signal in multi-carrier modulation exhibits high Peak-to-Average Power Ratio (PAPR) and large bandwidths, leading to the degradation of the radio link and additional generation out-of-band interferences, which degrade the quality of the transmission. Practical solutions exist, like a power back-off, but with unacceptable efficiency performances of the transmitter. This paper deals with efficiency and linearity improvement using a new PAPR reduction method based on the combination of Discrete Cosine Transform and shaping technique. The main principle is to determine an optimal coding scheme according to a trade-off between coding complexity and performance benefits in the presence of PA nonlinearities. Simulation and experimental results in the context of OFDM signal and using a 20 W–3.7 GHz Radio-Frequency Power Amplifier show an improvement on PAPR reduction of about 3.25 dB. Also, the communication criteria like Bit Error Rate and Error Vector Magnitude are improved by about one decade and a half and 8%, respectively.

The Efficacy of an Explicit Intervention Approach to Improve Past Tense Marking for Early School-Age Children With Developmental Language Disorder.

Purpose The aim of the study was to evaluate the efficacy of a theoretically motivated explicit intervention approach to improve regular past tense marking for early school-age children with developmental language disorder (DLD). Method Twenty-one children with DLD (ages 5;9-6;9 [years;months]) were included in a crossover randomized controlled trial (intervention, n = 10; waiting control, n = 11). Intervention included once-weekly sessions over 10 weeks using the SHAPE CODING system, in combination with a systematic cueing hierarchy to teach past tense marking. Once the first group completed intervention, the waiting control group crossed over to the intervention condition. The primary outcome was criterion-referenced measures of past tense marking with standardized measures of expressive and receptive grammar as the secondary outcome. Ancillary analyses on extension and behavioral control measures of morphosyntax were also conducted. Results There was a significant Time × Group interaction (p < .001) with a significant difference in pre-post intervention improvement in favor of the intervention group (p < .001, d = 3.03). Further analysis once both groups had received the intervention revealed no improvement for either group on past tense production during the 5-week pre-intervention period, significant improvement pre-post intervention (p < .001, d = 1.22), with gains maintained for 5 weeks postintervention. No significant differences were found on pre- to postintervention standardized measures of grammar, or on extension or control measures. Conclusions The efficacy of the theoretically motivated explicit grammar intervention was demonstrated. Results contribute to the evidence base supporting this intervention to improve past tense production in early school-age children with DLD, suggesting it is a viable option for clinicians to select when treating morphosyntactic difficulties for this population. Supplemental Material https://doi.org/10.23641/asha.13345202.

Temporal coding of echo spectral shape in the bat auditory cortex.

Echolocating bats rely upon spectral interference patterns in echoes to reconstruct fine details of a reflecting object’s shape. However, the acoustic modulations required to do this are extremely brief, raising questions about how their auditory cortex encodes and processes such rapid and fine spectrotemporal details. Here, we tested the hypothesis that biosonar target shape representation in the primary auditory cortex (A1) is more reliably encoded by changes in spike timing (latency) than spike rates and that latency is sufficiently precise to support a synchronization-based ensemble representation of this critical auditory object feature space. To test this, we measured how the spatiotemporal activation patterns of A1 changed when naturalistic spectral notches were inserted into echo mimic stimuli. Neurons tuned to notch frequencies were predicted to exhibit longer latencies and lower mean firing rates due to lower signal amplitudes at their preferred frequencies, and both were found to occur. Comparative analyses confirmed that significantly more information was recoverable from changes in spike times relative to concurrent changes in spike rates. With this data, we reconstructed spatiotemporal activation maps of A1 and estimated the level of emerging neuronal spike synchrony between cortical neurons tuned to different frequencies. The results support existing computational models, indicating that spectral interference patterns may be efficiently encoded by a cascading tonotopic sequence of neural synchronization patterns within an ensemble of network activity that relates to the physical features of the reflecting object surface.

Soft Compression: An Approach to Shape Coding for Images

In this letter, we propose soft compression, an lossless compression approach to shape coding for images using location index and codebook of designed shapes with various sizes. This method is different from traditional image compression methods, as it aims at finding the basic shape blocks of pictures to improve the compression ratio from the perspective of information theory and frequency. In particular, the triplets including location and codeword are stored when encoding an image instead of the entire array. It is shown that soft compression can greatly reduce the bandwidth and storage space needed in the process of transmitting and storing the same kind of images.

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.

Conjunctive coding of color and shape in convolutional neural networks

Conjunctive coding of color and shape in convolutional neural networks

Multi-Level Distribution Matching

A general distribution matching architecture based on a multi-level structure is presented. It allows to generate arbitrary symbol distributions using simple binary distribution matchers. This is particularly advantageous for large symbol alphabets, where non-binary distribution matching algorithms tend to have high complexity. Some examples for possible implementations are provided, and a specific realization based on polar codes, which enables joint shaping and channel coding for reliable data transmission with higher-order modulation, is discussed and evaluated in detail.

Rate-Constrained Shaping Codes for Structured Sources

Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor – the ratio of the expected codeword length to the length of the input source word – and the minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.

Instance-level 3D shape retrieval from a single image by hybrid-representation-assisted joint embedding

We present a novel and effective joint embedding approach for retrieving the most similar 3D shape for a single image query. Our approach builds upon hybrid 3D representations—the octree-based representation and the multi-view image representation, which characterize shape geometry in different ways. We first pre-train a 3D feature space via jointly embedding 3D shapes with hybrid representations and then introduce a transform layer and an image encoder to map both shape codes and real images into a common space via a second joint embedding. Our pre-training benefits from the hybrid representation of 3D shapes and builds a more discriminative 3D shape space than using any one of 3D representations only. The transform layer helps to mind the gap between the 3D shape space and the real image space. We validate the efficacy of our method on the instance-level single-image 3D retrieval task and achieve significant improvements over existing methods.

Achievable Information Rates for Probabilistic Amplitude Shaping: An Alternative Approach via Random Sign-Coding Arguments.

Probabilistic amplitude shaping (PAS) is a coded modulation strategy in which constellation shaping and channel coding are combined. PAS has attracted considerable attention in both wireless and optical communications. Achievable information rates (AIRs) of PAS have been investigated in the literature using Gallager’s error exponent approach. In particular, it has been shown that PAS achieves the capacity of the additive white Gaussian noise channel (Böcherer, 2018). In this work, we revisit the capacity-achieving property of PAS and derive AIRs using weak typicality. Our objective is to provide alternative proofs based on random sign-coding arguments that are as constructive as possible. Accordingly, in our proofs, only some signs of the channel inputs are drawn from a random code, while the remaining signs and amplitudes are produced constructively. We consider both symbol-metric and bit-metric decoding.

Integration and segmentation conflict during ensemble coding of shape.

During ensemble coding, the visual system extracts summary information from input that has been integrated, facilitating gist-level judgments about objects and features that belong together. In contrast, input can be segmented, allowing for quick categorical distinctions between objects. Integration and segmentation usually work in parallel but may sometimes conflict in the context of ensemble coding. To investigate this possibility, we examined summary perception of aspect ratio (i.e., "tallness/flatness"). Aspect ratio has a category boundary (e.g., a circle), and individual aspect ratios may be perceptually exaggerated-segmented-away from this boundary. We predicted that summary perception of multiple aspect ratios would be disrupted when, as a set, they spanned the category boundary, since integration and segmentation would then be at odds. We found that when observers reported the average aspect ratio of a set of ellipses, they were less sensitive to the mean of sets that included both tall and flat ellipses, compared to sets comprised of tall or flat ellipses. Follow-up experiments suggest this occurred because segmentation distorted the appearance of ellipses away from the category boundary, exaggerating set heterogeneity. These experiments advance understanding of how the visual system summarizes information by showing that integration and segmentation can conflict. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps

ABSTRACT The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.