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The purpose of this study was to examine the content of workout videos on TikTok. A sample of 297 TikTok workout videos was coded for demographic factors and body shape of content creators, as well as the presence of body positivity messaging, appearance-related messaging, and other relevant themes. The results suggest that TikTok workout content is often presented in a time-lapse format with little verbal instruction, which may make it difficult for viewers to perform the exercises. The majority of the videos depicted young women with athletic bodies and other characteristics associated with culturally based beauty ideals, and approximately half of the videos included non-White content creators. Approximately a quarter of the videos in the sample included objectification and a smaller proportion of the sample included messages about inner positivity. Overall, the TikTok platform may provide underrepresented individuals opportunities to seek out workout content from a more diverse group of instructors; however, the fact that many of these videos including objectifying depictions of the content creators is concerning. Further experimental work is needed in order to more fully elucidate the effects of TikTok workout videos on viewers. [ABSTRACT FROM AUTHOR]

In speech coding, denoising of the speech signal is essential as well as crucial. The filters for minimizing errors through denoising employ the autoregressive moving average (ARMA) approach, introducing higher computational complexity in speech coder design. This research work presents the design and implementation of an effective perceptual weighting filter (PWF) for speech coding. The high-level synthesis of the fixed-point PWF filter is optimized by multiple optimization techniques along with detailed design space exploration using the weighted sum (WS) method. To enhance the performance, an FPGA-based hardware accelerator is proposed using hardware/software (HW/SW) co-design in an embedded environment. Simulative analysis in Vivado HLS and final accelerator design in the Vitis IDE tool validate the proposed architecture by using real-time speech samples, demonstrating a 50% reduction in area and a 99% execution improvement. This makes it well-suited for use in modern speech codecs, enhancing the efficiency. [ABSTRACT FROM AUTHOR]

Reading comprehension tests involve not only reading passages and answering questions but also making choices such as the test-taking strategy to use, whether to search passages for answers, and where to begin searching. We examined the associations between student characteristics, passage type, test-taking strategies, and students' time searching texts for answers. We also examined differences in the characteristics of students with high precision in finding text that supported correct answer choices compared to students who lacked precision. Students in grades three (n = 76), five (n = 86), and eight (n = 86) read narrative and expository passages and responded to questions. We used eye-tracking videos to code test-taking strategies and question-reading and responding behaviors (QRRBs). Student characteristics of reading achievement and working memory were measured. Results indicated that test-taking strategies and QRRB impacted the effort and time required by students to complete the tests and the processes measured. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient design of Hamming (11, 7) encoder utilising Full Swing-Gate Diffusion Input (FS-GDI) approach in 65 nm technology nano-size node. The proposed design of Hamming codes aims to improve the power and area efficiency through reducing of transistors count by employing power-efficient logic style. Encoding circuits of Hamming code (11, 7) and (7, 4) are designed using the various traditional and proposed approaches. The amount of consumed power, delay time, Power Delay Product (PDP) and hardware simplicity are employed as a metrics for evaluating the efficiency of the proposed designs of encoding circuits. The simulation experiments are executed utilising Cadence Virtuoso simulator package. These experiments revealed that the proposed designs of Hamming encoding circuits achieve delay time reduction by 50.91% and 20% for Hamming codes (7, 4) and (11, 7), respectively. Also, hardware (H/W) simplicity and area efficiency of the circuits are improved by 50% compared to CMOS-based circuits. From the results analysis, the proposed FS-GDI based Hamming encoding circuits achieve efficient power and delay optimising. Hence, the power consumption, delay and area in communications systems and DSP circuits due to encoding process are reduced. The whole performance of DSP circuits can be more power/area efficient. [ABSTRACT FROM AUTHOR]

We describe the development of an observational video coding tool, the Rehabilitation Observation Measure of Engagement (ROME), to quantify engagement in rehabilitative settings at the person (internal state of an individual) and between-system (interaction between individuals) level. Forty-nine children with unilateral spastic cerebral palsy (29 males; Age: M = 9.28 yrs, SD = 3.08 yrs) and their interventionists were videotaped during different activities. Construct validity was examined by correlating the ROME with the Engagement vs. Disaffection with Learning Survey and the Pediatric Rehabilitation Intervention Measure of Engagement – Observation questionnaire. Inter- and intra-rater reliability were examined using two independent raters. The ROME's responsiveness to change was examined by comparing scores across activities. For construct validity, results showed a positive correlation for person-level engagement (r = 0.444, p = 0.003). No relationship was found between-system-level engagement. High intrarater (91.8%) and interrater (96.1%) reliability was found. The ROME's responsiveness to change was supported by children exhibiting lower engagement scores during repetitive shaping activities. These findings provide evidence that the ROME is a reliable tool to objectively examine the construct of engagement within rehabilitation and is valid for quantifying person-level engagement. It provides information that cannot be extracted from questionnaires and can help guide intervention decisions. Behavioral characteristics, including engagement, of the agents involved in rehabilitation are largely unstudied, although engagement is expected to benefit motor learning. The Rehabilitation Observation Measure of Engagement (ROME) is an observation measure that uses predefined codes and can be used universally, as it is not limited to specific language or cognitive levels. The ROME is a reliable tool for objectively measuring the role of the construct of behavioral engagement during rehabilitation and valid for examining person-level engagement. The ROME may be used as a measure of client and service provider process, of intervention quality, or as a decision guide. [ABSTRACT FROM AUTHOR]

Digital sandbox games such as Minecraft can be used to assess and support creativity. Doing so, however, requires an understanding of what is deemed creative in this game context. One approach is to understand how Minecrafters describe creativity in their communities, and how much those descriptions overlap with the established definitions of creativity in the literature. Based on prior work, we used a modified rubric for assessing a creative product in this study. We investigated the evaluation of a total of 52 Minecraft builds (i.e., structures that are created by the Minecrafters) from five YouTube videos. In addition to qualitatively coding the video transcripts, we also used natural language processing (NLP) for automatic coding of our data at scale. Our qualitative analysis of the YouTube transcripts revealed evidence for all but one dimension (i.e., humor) of the creativity rubric we used in our dataset. In addition, two new dimensions of creativity emerged in our qualitative coding process (i.e., realism and effort). Finally, the NLP model that we developed achieved about 80% accuracy in distinguishing the sentences that showed evidence of creativity. This study provides insights for research and educational practices of creativity in Minecraft or similar game or simulation environments. [ABSTRACT FROM AUTHOR]

Recent years have seen the development of a variety of mammalian library approaches for display and secretion mode. Advantages include library approaches for engineering, preservation of precious immune repertoires and their repeated interrogation, as well as screening in final therapeutic format and host. Mammalian display approaches for antibody optimization exploit these advantages, necessitating the generation of large libraries but in turn enabling early screening for both manufacturability and target specificity. For suitable libraries, high antibody integration rates and resulting monoclonality need to be balanced - we present a solution for sufficient transmutability and acceptable monoclonality by applying an optimized ratio of coding to non-coding lentivirus. The recent advent of microfluidic- assisted hit discovery represents a perfect match to mammalian libraries in secretion mode, as the lower throughput fits well with the facile generation of libraries comprising a few million functional clones. In the presented work, Chinese Hamster Ovary cells were engineered to both express the target of interest and secrete antibodies in relevant formats, and specific clones were strongly enriched by high throughput screening for autocrine cellular binding. The powerful combination of mammalian secretion libraries and microfluidics-assisted hit discovery could reduce attrition rates and increase the probability to identify the best possible therapeutic antibody hits faster. [ABSTRACT FROM AUTHOR]

3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the "Dynamic Low Power Management Method in 3DWiNoC" (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design. [ABSTRACT FROM AUTHOR]

Although the relationship between prejudice and predictive coding has attracted more attention recently, many important issues remain to be investigated, such as why prejudice is so persistent and how to accommodate seemingly conflicting studies. In this paper, we offer an integrated framework to explain the functional-computational mechanism of prejudice. We argue that this framework better explains (i) why prejudice is somewhat immune to revision, (ii) how inconsistent processing (e.g. one's moral belief and biased emotional reaction) may occur, (iii) the dispute over the content of 'alief' (unconscious belief), and (iv) conflicting data reported in disinformation studies. Finally, we briefly review the main intervention strategies and offer recommendations. [ABSTRACT FROM AUTHOR]

Enhancing video quality is a vital area of research that has piqued the interest of academia and industry alike. The High-Efficiency Video Coding (HEVC) compressed videos suffer from significant quality loss, especially at low bit rates. As a result, the decoder must improve the image quality of HEVC videos. Deep Learning with Super Interpolation based Super Resolution (DLSI-SR) and Laplacian Filter (LF) method is proposed to eliminate artifacts. A Multi-Layered Deep Convolutional Neural Network (ML-DCNN) with LF has been introduced to reduce the artifacts quickly in compressed video and increase the subjective video quality. The quality of Low-Resolution (LR) frames can be effectively enhanced using Super Interpolation and Laplacian filter. The Edge orientation analysis is performed on various external training video frames during the training stage. The LR video frame is up-sampled and interpolated using the Super Interpolation algorithm during the upscaling phase. The interpolated frame is then submitted to edge detection using a Canny Edge detection algorithm for frame smoothing. Using local Laplacian filter, the reconstructed high quality video frame is sharpened with an edge preservation approach. The simulation results of the proposed DLSI-SR method shows higher performance in terms of PSNR and SSIM with less processing time in comparison with the existing approaches. The experimental results show that the proposed approach achieves a maximum PSNR of 42.5 dB (enhanced by 2.56 dB) and produced an SSIM improvement of 0.948 (enhanced by +0.022) as compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Coding metasurfaces, characterized by processing the electromagnetic waves for specified functions through predesigned coding sequences either in space or time, can manipulate the electromagnetic waves in both space frequencies and time frequencies. They have been emerging as the revolutionary platforms for metasurface technologies in recent years. In this paper, we first introduce the general concept and operating principle of the coding metasurfaces. Then, two design strategies for realizing the coding metasurface particles, altering resonant states and switching current phase, are provided, respectively. Next, two recent reported applications that are based on space-coding metasurface for RCS reduction and time-coding metasurface for spread-spectrum wireless communication are presented, respectively. Finally, existing challenges and potential future research directions in coding metasurfaces are discussed. [ABSTRACT FROM AUTHOR]

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models. [ABSTRACT FROM AUTHOR]

The internet has become an integral part of our modern life, and the protection of secret data over the internet of multimedia things is the fundamental concern for Smart City applications. Nowadays, the widely used internet of things (IoT) uses audio, image, video, and various mixed modes as well, where privacy, as well as security, are equally essential in real-time transmission. Substantial research has been reported on various encryption algorithms for text and image files, but a similar need for video encryption has been felt a little late. It may be noted that the absence of any standard encryption technique designed especially for any video file has compelled us to design a new but quite robust encryption algorithm for video input as secret. This article has primarily addressed the security needs for video content in IoT. Any video file can be considered as a collection of continuous images. In this research article, we have dealt with video encryption, fundamentally a multi-round key-based shuffling of a collection of images, components of the original video file to be encrypted. The proposed novel scheme follows the fundamental concept of the symmetric encryption technique, where the encryption key is common for both encryption and decryption in real-time video applications. [ABSTRACT FROM AUTHOR]

The periodic misalignment of the wrapping phase and fringe order based on the phase-shift + coding method is known to occur easily, leading to jump errors in absolute phase recovery. This paper proposes a two-phase unwrapping method based on shifting-phase coding. This approach encodes the fringe order information and the wrapping phase into the four-step phase shift mode. It generates four composite fringe patterns to directly resolve the wrapping phase and fringe order, thus improving the measurement speed by reducing the number of fringe projections. The interleaved wrapping phases are then constructed by a sequence that changes the initial wrapping phase. The two-phase unwrapping becomes the absolute phase, so the phase error is avoided and the measurement accuracy is improved. The proposed strategy's effectiveness is proved by the comparison experiments of different methods. Similarly, the suggested method's reliability is verified by the measurement experiments of several discontinuous objects. [ABSTRACT FROM AUTHOR]

Surgical workflow analysis has gained greater attention in enhancing patient safety and obtaining optimal surgery results. However, automated recognition and classification of surgical phases are highly challenging due to several problems like higher computational complexities, increased time consumption, reduced learning ability, etc. Thus, the proposed study intends to develop an effective surgical workflow recognition and classification mechanism. Initially, the video annotation software is used convert the laparoscopic input videos into several frames for developing a proper recognition system. Then, an Exaggerated Series Wiener Bilateral Filtering (E-SWBF) approach is used in the pre-processing stage for preserving the edges removing the noises. After pre-processing, the spatial and temporal features are extracted through the attention-based convolutional Inception Bi-directional Long Short Term Memory (Attn_conv Inc_2DLSTM) model. Next, the feature dimensionality issue is reduced by selecting significant features through an Enhanced binary Aquila optimization (EBiAO) algorithm. Finally, the surgical phases are recognized and classified by the proposed Gated capsule tweaked autoencoder model (Gcaps_TAE). The obtained values of accuracy 98.95%, precision 96.55%, sensitivity 96.90%, specificity 99.23%, F1-score 96.72%, MSE 0.30%, and MAE 0.101% shows that the proposed model is superior to other existing methods. [ABSTRACT FROM AUTHOR]

A new motion estimation method and its hardware implementation are presented in this paper. In this method, all the frames are firstly gray coded. After that, the two most significant bits from the current frame and reference frame are used to compute the motion vector. Besides, a diamond search algorithm is used in place of the full-search algorithm. The motion estimation scheme proposed in the present paper is also verified to work on various standard video sequences. The experimental results thus obtained show that the proposed algorithm has an excellent balance of performance and computational complexity. External memory access has been reduced drastically by incorporating search pixel reuse. The resulting architecture is simple in terms of the hardware cost and power requirements while being faster than a recently reported similar architecture. The proposed architecture requires less hardware than comparable systems without compromising performance. The proposed architecture is therefore suited to be used in portable consumer electronic devices with real-time video applications in which power consumption is a concern. [ABSTRACT FROM AUTHOR]

The High Efficiency Video Coding (HEVC) with scalable extension known as SHVC allows encoding the same video with different resolutions in a single bitstream. However, this process increases the encoding complexity. The complexity is increased mainly due to the motion estimation process and the Rate-Distortion Optimization (RDO) search process. The Test Zonal (TZ) algorithm with fast search patterns helps to accelerate the motion estimation process. However, the search patterns may get trapped to local minima, leading to inaccurate motion vectors. Moreover, the RDO search process used to determine the Coding Unit (CU) size increases the complexity. We proposed the Horizontal Subsampling Motion Estimation (HSME) method to find the accurate motion vectors with reduced complexity. The experimental results prove that the HSME method saves the encoding time by 53.03% with a 6.45% increase in Bjøntegaard delta bit rate and 0.28 dB loss in Bjøntegaard Delta Peak Signal-to-Noise Ratio (BD-PSNR) compared to standard SHM-12.1. In addition, we designed the Early Terminated Long- and Short-Term Memory (ET-LSTM) network that predicts the CU partition by taking the output features of the Early Terminated Convolutional Neural Network (ET-CNN). The ET-CNN learns the CU partitions from the residual Coding Tree Unit (CTU) using the deep learning approach. Our proposed method (HSME + ET-CNN + ET-LSTM) achieves 53% savings in encoding time, which is significantly higher than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The tradeoff between bandwidth efficiency and power efficiency with nonlinear distortion introduced by high power amplifier (HPA) in a modern communication system, at a high bit rate has become a prominent problem. Digital predistortion (DPD), the most cost-effective and yet lesser complex method is a widely used linearization method. In this method, the dynamic nonlinear characteristics of the PA are used to obtain an inverse transfer function to abolish the nonlinearity. The development of a good DPD technique depends on its ability to model an accurate PA behavior and its inverse behavior. Recently, several neural network-based predistortion linearizers had been proposed, and these linearizers are effectively proving their worth, but these methods report inadequate generalization performance in the presence of system imperfections like DC offset, IQ imbalance, and both. The present paper proposes a Mixture of Experts (MOE) based committee machine neural network for modeling the PA/transmitter system transfer function and its inverse transfer function under different system conditions. MOE models encompass a family of modular neural network architectures having several experts network and a single gating network, and targeting to divide a complex problem into simple subtasks. An algorithm based on a maximum likelihood criterion is considered for the MOE network training. The results obtained with the proposed model show good generalization performance with nonlinear HPA and with memory effect in the presence of system imperfections. [ABSTRACT FROM AUTHOR]

This paper explores a technique for visually/diagnostically lossless coding in the wavelet domain to effectively compress the three-dimensional medical image data. The quantisation module based on Just Noticeable Distortion (JND) for wavelets guarantees the visual quality in the reconstructed data. This method has been further extended to present the Volume of Interest (VOI) based technique that enables to preserve the quality of the diagnostically significant VOI region. The proposed method tested on several datasets outperforms the state-of-the-art methods. Apart from the conventional quality metric, Human Visual System (HVS) based quality metrics are also used to evaluate the visual quality of the reconstructed image. A subjective and objective evaluation carried out for VOI based coder shows that the quality-compression needs of the medical community are well addressed. [ABSTRACT FROM AUTHOR]

Research Findings: We examined the amount of preschool lead/assistant teachers' English/Spanish language use and relations between quality of teacher-child interactions, and Dual Language Learners' (DLLs) English/Spanish bilingual vocabulary in 31 English-medium Head Start classrooms. Measures in this study included (a) children's conceptual English/Spanish bilingual receptive and expressive vocabulary, (b) classroom quality via systematic observation, (c) video coding of the amount of utterances of teacher Spanish use in classrooms, and (d) a set of child- and teacher-level covariates. Results showed that Spanish was used much less than English by both teachers and DLLs. DLLs spoke more Spanish than teachers and directed most Spanish to peers. For high levels of Emotional Support, teachers' Spanish use was significantly negatively associated with English/Spanish bilingual expressive and receptive vocabulary. For low levels of Emotional Support and high levels of Instructional Support, higher amounts of teachers' Spanish use were significantly positively associated with higher levels of children's English/Spanish bilingual expressive and receptive vocabulary. Practice or Policy: Emotionally-supportive practices may enable monolingual teachers to facilitate their DLLs' learning through creating relationships that may counteract strain from language barriers. Spanish instruction provides a supportive effect for DLLs and may contribute to closing English language and literacy disparities between DLLs and English-speaking peers. [ABSTRACT FROM AUTHOR]

Digital Transformation has generated much research and curiosity recently, with the COVID-19 global pandemic accelerating its pace across all industry sectors. Current literature has not adequately provided a comprehensive understanding of Digital Transformation Leadership (DTL). The objective of this research is to explore the characteristics of DTL by undertaking a comprehensive review of Information Systems literature using a systematic procedure of identifying and coding 87 research papers, resulting in 600 coded excerpts, capturing the 'who' and 'what' of DTL. Our analysis identifies eight DTL characteristics, namely: digital strategist, digital culturalist, digital architect, customer centrist, organisational agilist, data advocate, business process optimiser and digital workplace landscaper. We discuss mapping the DTL characteristics to c-suite roles, presenting a taxonomy from the literature, of interest to both academics and practitioners. This research raises the awareness of the concept of DTL characteristics, especially amongst those in positions of leadership and decision making authority. [ABSTRACT FROM AUTHOR]

The emerging trend of ultra-high definition (4 K, UHD) video services necessitates coding efficiency, which is much higher than H.264. High-Efficiency Video Coding (HEVC) has been designed with the intention of drastically reducing the bit rate without compromising the video quality. The key feature is the introduction of a larger block structure with flexible block sizes from 64 × 64 to 8 × 8 samples. The enhanced feature has imposed higher complexity, specifically at the intra-prediction mode level of HEVC. The proposed research work has presented a novel hybrid approach to reducing the number of candidate Coding Units (CU) passing through irrelevant partition patterns and time-consuming iterative Rate-Distortion (RD) cost estimation process. The hybrid approach is implemented in two steps. In the first step gradient evaluation is performed and the CU is categorized as homogeneous or non-homogeneous. In the second step Early Termination is adopted by using machine learning for partitioning non-homogeneous CU. As the number of candidate Coding Units undergoing the time-consuming RD process is reduced by using the two-step hybrid approach, the proposed approach proves to be the best of its kind. The reduction in the number of candidate units undergoing the RD process reduces the complexity of the intra-prediction process. Encoding time is the measure of complexity and the proposed hybrid approach reduces the encoding time by an average of 36% compared with the HEVC reference model and also by an average of 26% as compared with the previous work. The Peak Signal to Noise Ratio (PSNR) reduced by an average of 0.1% indicates a slight reduction in the encoded image quality. The proposed approach significantly reduced the encoding time without affecting the RD performance of the encoder. The image quality is not compromised substantially when considering the improvement in the encoding time and the reduction in the complexity at an intra-prediction level. [ABSTRACT FROM AUTHOR]

In this paper, we propose to redefine the classical studies of urban trails and wanderings by giving greater consideration to the affective and intra-active aspects of the performance of gait. Hence, the idea is to deepen our knowledge of landscape as a sphere of movement. The need to conduct research on urban walking emerges from the mechanics of so-called "sensory motor amnesia", an unaware process of memory loss of how certain groups of muscles feel and how to control them. It seems that, apart from such casual matters as small injuries and bad postural habits, the process can be also deepened due to cultural factors, as a result of adapting bodies to modern urban life patterns. The phenomenon of re-fixing specific, human, physiological affordances is manifested by the development and preference of certain techniques and strategies of embodied acting in space, among which some may result in enhancing the sphere of discomfort and others lead to somatic improvement. This article presents walking, as one of the body technologies constituting the quality of spatial experience. Walking is presented as an exploratory, and adaptive form of the art of living, which can improve the quality of human urban life. The proposed treatment of the topic within the framework of an affective, intra-active, urban landscape theory may facilitate a better understanding of the mechanics of unperceived (hidden) affordances, pertaining to the processes involved in the developing performatics of different kinds of walking in different kinds of urban landscapes. [ABSTRACT FROM AUTHOR]

The purpose of this review paper is to understand why organisations choose to implement data governance (DG) programmes. An understanding of these motivations will facilitate assessing the effectiveness of DG programmes. A search of the literature returned 628 publications for examination; of these 50 were deemed to be relevant to the research, and were selected for analysis and coding using a grounded theory approach. Our analysis found 115 organisational motivations for implementing DG, grouped into 23 categories. We use the Khatri and Brown framework to organise these categories across their five decision domains. The motivations are predominantly associated with operations and technology. This presents a challenge for organisations, where an over-focus on technology could lessen the business imperative. DG needs to be much more than an operational plan for managing the data asset. DG requires a holistic approach to succeed which suggests that all decision domains are considered adequately. [ABSTRACT FROM AUTHOR]

Geospatial studies must address spatial data quality, especially in data-driven research. An essential concern is how to fill spatial data gaps (missing data), such as for cartographic polylines. Recent advances in deep learning have shown promise in filling holes in images with semantically plausible and context-aware details. In this paper, we propose an effective framework for vector-structured polyline completion using a generative model. The model is trained to generate the contents of missing polylines of different sizes and shapes conditioned on the contexts. Specifically, the generator can compute the content of the entire polyline sample globally and produce a plausible prediction for local gaps. The proposed model was applied to contour data for validation. The experiments generated gaps of random sizes at random locations along with the polyline samples. Qualitative and quantitative evaluations show that our model can fill missing points with high perceptual quality and adaptively handle a range of gaps. In addition to the simulation experiment, two case studies with map vectorization and trajectory filling illustrate the application prospects of our model. [ABSTRACT FROM AUTHOR]

Objectives: Ergonomic design of child restraint systems (CRS) may facilitate optimal travel behavior and crash protection of child passengers during motor vehicle trips. However there have been few studies examining the relationship between CRS design and child passenger travel behavior. The aim of this study was to examine whether associations between CRS design features and child passenger behavior exist during real-world, everyday vehicle trips.Methods: Video from a naturalistic driving study (NDS) was analyzed in this study. Families drove an instrumented study vehicle for approximately two weeks with at least one child aged between one and eight years traveling in their own forward-facing (FF) CRS or belt positioning booster (BPB). Video for one child passenger was randomly selected from each trip for analysis. Video was coded for five-second epochs at nine time points (5%, 17%, 25%, 30%, 50%, 53%, 75%, 89% and 95% of trip length). Two types of child passenger travel behaviors were identified by manual review of the video and audio recordings: (i) optimal/suboptimal head position and (ii) correct/incorrect use of the internal harness/shoulder belt. Video screenshots were used to characterize CRS design features. Random effects logistic regression models were used to examine the associations between specific CRS design features and the travel behaviors of interest, whilst accounting for clustering of data by child and trip.Results: Suboptimal head position was associated with the absence of a height adjustable headrest and a narrow headrest wing width in FFCRS. Incorrect harness use in a FFCRS was associated with the absence of an adjustable headrest, in addition to headrest features such as wing width and depth. In BPBs, a reduction in suboptimal head position was associated with the absence of a sash belt guide, however no restraint design features were associated with incorrect shoulder belt use.Conclusions: Some CRS design features may influence undesirable child passenger travel behavior. These early findings support enhanced and user-centric CRS design as a likely important mechanism to improve child passenger safety. [ABSTRACT FROM AUTHOR]

This study investigated the effect of traditional interactive reading and interactive reading using mindmaps on children's language competence and their story comprehension and causal reasoning. Furthermore, we explored teachers' dialogic scaffolding in the two approaches and investigated the relation between teachers' dialogic scaffolding and children's causal reasoning. In total, 7 early childhood teachers and their 176 pupils participated. Classrooms were randomly assigned to the traditional interactive reading group or the interactive reading using mindmaps group. Before and after a six-week intervention, children's language competence was measured using tests for thematic vocabulary, narrative competence, and critical listening. In addition, story comprehension was measured with a short questionnaire and children's causal reasoning and teachers' dialogic scaffolding were assessed by coding videos of shared book reading activities. Outcomes indicated that children significantly improved their language competence over the course of the study, independent of the group to which they were assigned. In addition, findings showed that children's causal reasoning was partly related to teachers' dialogic scaffolding. [ABSTRACT FROM AUTHOR]

The objective was to identify the main characteristics of the way that people with disabilities are represented in secondary physical education textbooks. The study was empirical and descriptive. The sample comprised 6.773 photographs. The methodology employed was a content analysis using an ad hoc observational instrument which was scientifically validated through a pilot study, a consultation with experts and an inter-coding test. Univariate and bivariate analysis were carried out; statistical calculations utilised SPSS 20.0. Results showed that the representation of people with disabilities in physical education textbooks is very limited, both in terms of frequency and diversity. Some positive changes were noted with regards to similar, previously published research. There are signs of a move away from an elite sports model towards a more varied representation that includes artistic motor expression, physical fitness training and physical activities in natural environments which do not necessarily take place in the traditional educational environment. [ABSTRACT FROM AUTHOR]

Recognising workflow phases from endoscopic surgical videos is crucial to deriving indicators that convey the quality, efficiency, outcome of the surgery, and offering insights into surgical team skills. Additionally, workflow information is used to organise large surgical video libraries for training purposes. In this paper, we explore different deep networks that capture spatial and temporal information from surgical videos for surgical workflow recognition. The approach is based on a combination of two networks: The first network is used for feature extraction from video snippets. The second network is performing action segmentation to identify the different parts of the surgical workflow by analysing the extracted features. This work focuses on proposing, comparing, and analysing different design choices. This includes fully convolutional, fully transformer, and hybrid models, which consist of transformers used in conjunction with convolutions. We evaluate the methods against a large dataset of endoscopic surgical videos acquired during Gastric Bypass surgery. Both our proposed fully transformer method and fully convolutional approach achieve state-of-the-art results. By integrating transformers and convolutions, our hybrid model achieves 93% frame-level accuracy and 85 segmental edit distance score. This demonstrates the potential of hybrid models that employ both transformers and convolutions for accurate surgical workflow recognition. [ABSTRACT FROM AUTHOR]

Multilingual films do not always travel well across borders. When translated, multilingualism may become neutralised and washed out. In subtitling, multiple languages used in the original are usually not marked in any way in the target subtitles. This results in neutralisation and linguistic homogenisation, whereby target viewers may not even realise that different languages are spoken in the film. In this study, we test a new solution aimed to help preserve the multilingual nature of films in interlingual subtitling: the strategy of colour coding. To gauge the impact of colour coding on viewers, we conducted a survey-based reception study on a group of 52 Polish hearing speakers who watched a multilingual film with interlingual subtitles in two versions: one where subtitles were coloured, i.e. each language was marked with a different colour, and the other, control condition, where the subtitles were traditionally white. We measured participants' immersive tendency, immersion, comprehension, and cognitive load. While colour coding did not have a significant impact on immersion, we found that individuals with a lower tendency for immersion reported higher immersion levels when watching coloured subtitles. Our results may contribute to the discussion how to render the multilingual nature of films in interlingual subtitling. [ABSTRACT FROM AUTHOR]

Mobility-as-a-Service (MaaS) can be considered the latest innovative technological solution to sustainable transportation. It offers travelers access to a pre-paid bundle of transportation modes on a single app with the additional convenience of the integration of planning, booking, and payment. Capturing the impact of this new technology on travel behavior remains a challenge, given the scarce number of comprehensive MaaS pilots. In this study, a stated adaptation experiment was designed to determine travelers' propensity toward using this new service. Such knowledge sheds light on the potential of MaaS to alter daily travel patterns. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods. [ABSTRACT FROM AUTHOR]

Obtaining accurate cloudage information through ground-based cloud observation is of great significance to astronomical telescope observatory site selection. This paper proposes a residual attention-based encoder–decoder network (CloudRAEDNet) for ground-based cloud image segmentation in nychthemeron. CloudRAEDNet uses ImageNet pre-trained ResNet50 as the encoder backbone network, which reduces the number of network training. The network decoder introduces residual modules to solve the problem of network degradation caused by the increase in the number of network layers. CloudRAEDNet connects encoder and decoder through attention gates to suppress the features of irrelevant areas and automatically focus on areas with prominent features. In addition, the segmentation performance of the network is further improved by the ranger optimizer. The comparative experimental results show that CloudRAEDNet can segment the local details of the ground-based cloud images more finely without increasing the time complexity. Compared with CloudSegNet, EFCN, CloudU-Net and CloudU-Netv2, CloudRAEDNet has the best segmentation performance. The results of ablation experiments show that the attention module contributes the most to CloudRAEDNet and the residual module contributes the least to CloudRAEDNet. In addition, the pre-training and Ranger optimizer also contribute to improving the segmentation performance of CloudRAEDNet. [ABSTRACT FROM AUTHOR]

In this paper, a polarization conversion metasurface is proposed, which can realized ultra-wideband circular polarization (CP) maintaining reflection in the frequency range 7.3–28.7 GHz; in addition, when its unit cell structure is rotated by an angle ψ, almost ±2ψ Pancharatnam-Berry (PB) phase will be generated in its co-polarized reflection coefficient under CP incidence. Thus, base on the polarization conversion metasurface, an ultra-wideband PB 2-dit coding metasurface is further proposed for Radar Cross Section (RCS) reduction. The simulation and experiment results show that the coding metasurface can achieve more than 10 dB RCS reduction in the ultra-wide frequency band 7.2–28.6 GHz under normal incidence with arbitrary polarizations; in addition, when the incident angle is increased to 45°, the ultra-wideband RCS reduction can still be realized, which shows that the coding metasurface has good stealth performance in X, Ku and K bands, and it is very practical for most airborne radar, fire control radar and imaging radar working in these bands. [ABSTRACT FROM AUTHOR]

The complexity of intra prediction in High-Efficiency Video Coding (HEVC) is increased significantly due to the incorporation of inherent features like variable-sized quadtree partitioned coding units and 35 angular modes that help in achieving better compression. This paper presents an efficient hardware architecture for the intra prediction that supports and comprises the above aspects and achieves a higher throughput to support high definition (HD) videos. A compact reusable reference buffer structure is implemented to limit the buffer size to 1 KB. A dedicated arithmetic unit to take advantage of the parallelism present in the prediction algorithm is incorporated, which allows the reuse of multipliers to reduce hardware resources. The loading of reference samples to buffers for prediction causes significant delays which are eliminated in our design. The entire architecture functions as a pipelined unit with no data dependency and generates eight samples/clock cycle in parallel. The design is implemented on a Field Programmable Gate Array (FPGA) platform operating at a frequency of 110 MHz. This makes it possible to support 4 K videos at 30 frames per second, with the resource cost of 16 K logic gates and 122 registers. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, the emergency policy for online learning at home has been launched in China. Live video, prerecorded video, and their combination are the three main video learning modes. Parental involvement emerges as a unique factor that may influence online learning effects. Students' behaviors such as interaction, engagement may vary from traditional online learning context due to multiple reasons. Hence, the previous acceptance models are not applicable. In this paper, by modifying and extending the Expectation Confirmation Model, a unified acceptance and continuance intention model (UACIM) is proposed to explore the factors affecting students online learning continuance intention. Confirmation is omitted for the lack of a priori knowledge. Perceived ease of use is included as the factor assessing technology performance. Parental involvement, interaction, and students' engagement are considered as external variables. The hypotheses are validated by the data collected from 306,139 students from Grade 4 to Grade 9 in Hubei Province, China. The results of structural equation modeling reveal that all variables are significant in explaining continuance intention, which indicates the vital roles of the external factors. Some negative correlations are found with suggestions that practitioners should be prudent when implementing interactions as well as offering individualized instructional designs. Furthermore, three video learning modes are found to be successful. The most preferable way is the hybrid mode with the most interactions, engagement, strongest satisfaction, perceived usefulness, and continuance intention. There are significant differences among three groups for most paths. Therefore, appropriate interventions can be devised to enhance learning effects and continuance intention when utilizing different video learning. [ABSTRACT FROM AUTHOR]

Most deep learning methods in hyperspectral image (HSI) classification use local learning methods, where overlapping areas between pixels can lead to spatial redundancy and higher computational cost. This paper proposes an efficient global learning (EGL) framework for HSI classification. The EGL framework was composed of universal global random stratification (UGSS) sampling strategy and a classification model BrsNet. The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples. To fully extract and explore the most distinguishing feature representation, we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information. As a type of spectral attention, the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model. Meanwhile, we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework. Experiments were conducted on three famous datasets, IP, PU, and SA. Compared with other classification methods, our proposed method produced competitive results in training time, while having a greater advantage in test time. [ABSTRACT FROM AUTHOR]

Braced frames are frequently used as lateral load resisting systems in steel buildings due to their cost-effectiveness and efficiency in resisting earthquake loads. Evaluating the seismic loads on those buildings requires estimating the fundamental period of vibration of the buildings at first. Most design codes use empirical formulas that depend only on the height of the buildings without considering the effect of bracing configurations or building irregularities. This paper aims to develop new equations to estimate the fundamental period of vibration of irregular steel buildings occupied with different bracing systems. For this purpose, 176 prototype buildings with different bracing configurations and irregularities have been selected and modeled using ETABS finite element program. Three types of irregularity have been considered: vertical, horizontal, and combined irregularity. Then, the fundamental periods of vibration have been estimated through analysis and optimal building design using nonlinear regression analysis. According to the findings, the configuration of the bracing system and the building irregularity influence the fundamental period of vibration of buildings of the same height, where vertical and combined irregularities decrease the period values by about 20% for CBFs. This indicates the importance of incorporating new equations in calculating the fundamental period of these types of steel structures. [ABSTRACT FROM AUTHOR]

Augmented virtual environments (AVE) combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously. AVE can provide solutions for continuous tracking of moving objects, camera scheduling, and path planning in the real world. This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment. The study includes 3D motion analysis of moving objects, multi-path prediction, hierarchical visualization, and path-based multi-camera scheduling. The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE. The path analysis algorithms proved accurate and time-efficient, costing less than 1.3 ms to get the optimal path. The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory, which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene. [ABSTRACT FROM AUTHOR]

This study quantified rugby referees' decision-making accuracy at the breakdown in an elite rugby sevens tournament, examining whether decision-making accuracy differed over time, and if it was associated with match demands, score difference, field location, and referee positioning. During the National Sevens Tournament (six referees, 22 matches), decision-making accuracy was analysed using video coding from 602 breakdowns and match demands were examined using GNSS technology and heart rate recordings. Overall, the referees had a decision-making accuracy of 88.2% with no changes over time (all p > 0.05). There were no significant associations between match demands, score difference, or field location and decision-making accuracy (all p > 0.05). However, significantly more incorrect decisions occurred when the referees were in a suboptimal position (29.0%) compared to an optimal position (10.9%) at the breakdown (p < 0.05). The findings suggest that referees' decision-making accuracy during the breakdown remains relatively consistent across an elite rugby sevens tournament. While unaffected by match demands, score difference or field location, referees' decision-making accuracy may be improved by optimising their positioning at the breakdown. [ABSTRACT FROM AUTHOR]

Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman Coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel–Ziv–Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment. [ABSTRACT FROM AUTHOR]

The main challenge of Cognitive Radio (CR) is to identify the occurrence of primary users (PU) effectively to mitigate interference with licenced communications. The occurrence of PU is identified by spectrum sensing (SS). Due to shadowing and deep fading, SS may produce false sensing results. Cooperative spectrum sensing (CSS) can mitigate these problems but at the cost of extra energy consumption especially when the CR users are battery functioned. In CSS, the fusion rule directly affects the energy efficiency (EE) of CR users, it can be optimised to increase EE. Moreover, the energy consumption of the CR users can be minimised if they are grouped into clusters. In this paper intra and inter fusion schemes have been proposed in a cluster-based CSS (CB-CSS). The overall EE is maximised by optimising the inter and intra fusion rule threshold of CB-CSS. An iterative algorithm has been presented that determines the optimal value of inter and intra fusion rule. Results show that for 6X6cluster size, optimum inter and intra fusion rule threshold is found to be 4 and 5, respectively. [ABSTRACT FROM AUTHOR]

The use of digital representations of physical objects allows to simulate phenomena and predict behaviours. The representativeness of a model is based on the congruency between the code, the survey and the modelling strategies. In terms of physical space, two important approaches are the Geographical Information Systems (GIS) and Building Information Modelling (BIM). GIS approach is intended to code environmental information based on geographical references. BIM approach is intended to code buildings in terms of their components, generating parametric descriptions. For historic buildings, BIM extends to the so-called Historical BIM models (HBIM). Together, these strategies allow coding territorial-scale entities, such as historical centres. An application for these models would be the simulation of risk situations, leading to vulnerability analysis. Despite the existence of GIS-BIM-HBIM integration examples, most of their procedures are designed to fit with a specific case study, which questions the suitability of a general coding framework. This paper presents a vision of state-of-the-art technologies and strategies for coding, surveying and model historical centres, with emphases on the analysis of urban vulnerability and risk. Finally, we propose a general comprehensive framework on the convergences of GIS-BIM-HBIM technologies and successful practices. [ABSTRACT FROM AUTHOR]

Aphasia research uses the length of time within rehabilitation sessions as the main measure of dosage. Few papers detail therapeutic ingredients or outline the number of times these were delivered over the treatment period. The present observational study identified therapeutic ingredients in the Very Early Rehabiltiation in SpEech (VERSE) trial and explored the dosage provided using a model of cumulative intervention intensity (CII). Therapists video recorded one therapy session per week and 53 (12%) randomly selected therapy videos were analysed. The videos were coded for number of error productions, self-corrections and type and frequency of therapist cueing. The Western Aphasia Battery Revised-Aphasia Quotient (WABR-AQ) was used for measuring patient outcome with total verbal utterances (p < 0.001) and cues used with success (p < 0.001) being independent positive predictors of WABR-AQ score at six months post stroke and hypothesized as key therapeutic ingredients. The CII was calculated by counting identified therapeutic ingredients and multiplying this by the number of sessions completed. Collectively, the key ingredients occurred on average 504 times per session and over 10,000 times per participant during the treatment period. This paper reports a novel approach for identifying key treatment ingredients and detailing the dosage delivered within an early aphasia rehabilitation trial. [ABSTRACT FROM AUTHOR]

A modified algorithm combining equivalence theory with MPI technology to improve Global Navigation Satellite System data processing efficiency is presented. The reservation of covariance matrix, elimination of unknowns and parallelism of the equivalence algorithm are derived. Experimental results show that the algorithm can improve the efficiency of processing 100 IGS stations by 56% with negligible precision loss. Moreover, the proposed approach has higher efficiency than the leastsquares and Gauss-Jordan parallel method and this advantage is more obvious as computational scale increases. Further experiments on a clustershow the more nodes used, the higher the efficiency it can achieve. [ABSTRACT FROM AUTHOR]

Over the past few years, Convolutional Neural Networks (CNN) have grown in popularity with the remote sensing community due to their relatively easy training process, excellent generalization capacity and state-of-the-art performance. CNN models following an encoder-decoder architecture to perform end-to-end semantic segmentation have been applied to Change Detection (CD) applications with remarkable results. In this paper, we aim to further improve the performance of such models. First, we experiment with the introduction of additional boundary information into an encoder-decoder architecture that performs semantic segmentation for CD. We use the Dense Extreme Inception Network (DexiNeD) to produce the semantically informed edges. Second, we propose a training process that implicitly teaches the model to become more robust to misregistration errors. We evaluate our proposed approaches on a CD dataset, which consists of very high resolution RGB satellite image pairs, using two encoder-decoder models, UNet and UNet++, as our backbone architecture. The evaluation results suggest that both enhancements improve the performance of the CD network, with the average improvement on precision, recall, F1score and IoU ranging between 1% and 2% when incorporating boundary features into our architectures, and up to 2.5% when modeling the misregistration errors in the training process. [ABSTRACT FROM AUTHOR]

In this paper, a different approach based on convolutional neural networks (CNNs) is proposed to generate digital surface model (DSM) from a single high-resolution satellite image. In this regard, an approach based on a deep convolutional neural network was designed. The proposed CNN has an encoder-decoder structure to extract multi-scale features in the encoding part and estimate the height values by up-sampling the extracted abstract features. Then, a filtering approach based on morphological operators is proposed to extract the non-ground pixels from each estimated height image. The final digital surface mode Shuttle Radar Topography Mission (SRTM) is obtained by integrating the SRTM elevation model and extracted non-ground objects. Evaluating the estimated height images indicated 0.219, 0.865, and 2.912 m on average log10 error, relative error, and root mean square error (RMSE), respectively. In addition, investigating the final integrated DSM indicated 4.625 m on average for RMSE, demonstrating a promising performance of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective image retrieval approach using block truncation coding compressed data stream based on edge-based quantization (EQBTC). First, an image is compressed into corresponding quantisers and a bitmap image by EQBTC. Then, the quantisers are used for colour feature extraction, whereby the bitmap image and grey image are used for luminance and edge feature extraction. Subsequently, two image features, the colour histogram feature (CHF) and the overall structure feature (OSF), are computed to measure the similarity between two images using a specific distance metric computation. The results presented in this paper demonstrate that the proposed model is superior to the block truncation coding image retrieval scheme and some earlier proposed methods. [ABSTRACT FROM AUTHOR]

Pedestrian traffic is an important subject of surveillance to ensure public safety and traffic management, which may benefit from intelligent and continuous analysis of pedestrian videos. State-of-the-art methods for intelligent pedestrian surveillance have a number of limitations in automating and deriving useful information of high-density pedestrian traffic (HDPT) using closed circuit television (CCTV) images. This work introduces an automatic and improved HDPT surveillance system by integrating and optimizing multiple computational steps to predict pedestrian distribution from input video frames. A fast and efficient particle image velocimetry (PIV) technique is proposed to yield pedestrian velocities. A machine learning regressor model, boosted Ferns, is used to improve pedestrian count and density estimation: an essential metric for HDPT analysis. A camera perspective model is proposed to improve the speed and position estimates of HDPT by projecting 2D image pixels to 3D world-coordinate dat. All these functional improvements in HDPT velocity and displacement estimations are used as inputs to a sophisticated pedestrian flow evolution model, PEDFLOW to predict HDPT distribution at a future time point, which is a crucial information for pedestrian traffic management. The predicted and simulated HDPT properties (density, velocity) obtained using the proposed framework show low errors when compared to the ground truth data. The proposed framework is computationally efficient, suitable for multiple camera feeds with HDPT videos, and capable of rapidly analyzing and predicting flows of thousands of pedestrians. The paper shows one of the first steps towards fully integrated CCTV-based automated HDPT management system. [ABSTRACT FROM AUTHOR]

There is a conflict between increasing the image swath and improving the azimuth resolution in a conventional single-channel synthetic aperture radar (SAR) system. This paper introduces a method of realizing high-resolution and wide-swath (HRWS) SAR imaging with a multiple-input multiple-output (MIMO) system based on a space-pulse phase coding (SPPC) technique, which makes it possible to discriminate and separate the echoes from multiple ambiguous range regions through coding the phases of the transmitted pulses in each spatial channel. Two coding schemes, referred to as the uniform-cyclic coding and the uniform-acyclic coding, respectively, are devised for discriminating the range-ambiguous echoes. By choosing a proper phase gradient factor, both coding schemes can be used to resolve the range ambiguities. The uniform-cyclic coding scheme presents a little better performance of the range ambiguity suppression, and the uniform-acyclic coding scheme can preserve the coherency of the range-ambiguous clutters. Simulation results have demonstrated the effectiveness of the proposed coding schemes. [ABSTRACT FROM AUTHOR]