Codebook Construction Research Articles

Learning shared template representation with augmented feature for multi-object pose estimation

Template matching pose estimation methods based on deep learning have made significant advancements via metric learning or reconstruction learning. Existing approaches primarily build distinct template representation libraries (codebooks) from rendered images for each object, which complicate the training process and increase memory cost for multi-object tasks. Additionally, they struggle to effectively handle discrepancies between the distributions of training and test sets, particularly for occluded objects, resulting in suboptimal matching accuracy. In this study, we propose a shared template representation learning method with augmented semantic features to address these issues. Our method learns representations concurrently using metric and reconstruction learning as similarity constraints, and augments response of network to objects through semantic feature constraints for better generalization performance. Furthermore, rotation matrices serve as templates for codebook construction, leading to excellent matching accuracy compared to rendered images. Notably, it contributes to the effective decoupling of object categories and templates, necessitating the maintenance of only a shared codebook in multi-object pose estimation tasks. Extensive experiments on Linemod, Linemod-Occluded and TLESS datasets demonstrate that the proposed method employing shared templates achieves superior matching accuracy. Moreover, proposed method exhibits robustness on a collected aircraft dataset, further validating its efficacy.

Three constructions of asymptotically optimal codebooks via multiplicative characters of finite fields

In this paper, we give three constructions of codebooks with multiplicative characters of finite fields. Our constructions generalize the first construction in A. X. Zhang and K. Q. Feng (IEEE Trans. Inf. Theory 58(4), 2507-2511, 2012) from one dimension to two dimensions. We determine the maximum cross-correlation amplitude of these codebooks by the orthogonal relation of multiplicative characters and the properties of Jacobi sum. We prove that all the codebooks we constructed are asymptotically optimal with respect to the Welch bound. The parameters of these codebooks are new.

A class of nearly optimal codebooks and their applications in strongly regular Cayley graphs

<abstract><p>Codebooks with small inner-product correlations are desirable in many fields, including compressed sensing, direct spread code division multiple access (CDMA) systems, and space-time codes. The objective of this paper is to present a class of codebooks and explore their applications in strongly regular Cayley graphs. The obtained codebooks are nearly optimal in the sense that their maximum cross-correlation amplitude nearly meets the Welch bound. As far as we know, this construction of codebooks provides new parameters.</p></abstract>

15 An Investigation of Non-Evidence Based Autism Intervention Representations in the Media

Objective:This study seeks to describe the context and contents of YouTube videos featuring non-evidence-based practices (NEBPs) for autism spectrum disorder, including recent fad usage of transcranial magnetic stimulation.Participants and Methods:A sample search of autism intervention-related YouTube videos was conducted to reflect the experience of caregivers researching autism intervention information on the internet. Exclusion criteria was applied to create a preliminary dataset. The videos were categorized into general evidence-based practice (EBP), non-evidence-based practice (NEBP), and both EBP and NEBP. The NEBP-related videos were then qualitatively described using a priori codes based on the literature regarding signs of pseudoscience, as well as an iteratively developed codebook through a constant comparative method between two independent coders.Results:Total videos from the YouTube search using the query "Autism Treatment" were capped at 150 videos. Total videos in English were 138. Total discrete videos (non-repeated) were 134. Total NEBP-related discrete videos were 62 (46% of final dataset); Total EBP-related videos were 38 (28.3% of final dataset); and total videos containing both EBP and NEBP as subject matter were 25 (18.6% of final dataset). Of the NEBP-related in the final dataset, the most frequent NEBP which occurred was stem cell therapy (n=26), followed by the Son-Rise program (n=13), Cannabis/marijuana (n=5), transcranial magnetic stimulation/magnetic e-resonance therapy (n=5), neurofeedback (n=1), brain rehabilitation (n=1), suramin (n=2), fecal transplants (n=2), Hyperbaric Oxygen therapy (n=1), Ayurvedic medicine (n=1), virtual reality (n=1), and others. The constant comparative method of coding yielded results specific to videos about NEBPs in autism, including: statements declaring a treatment will be effective, a banner on the YouTube page indicating if the channel is from a reputable source, a parent testimonial, a parent or caregiver display of emotion with respect to treatment efficacy, statements regarding pre-intervention repetitive or challenging behaviors, statements regarding demonstrated effects of the treatment, statements of a definitive cause of autism, statements regarding the severity of the autism in the treatment subject, specific words in reference to autism, including "disease", "toxin", or "inflammation", discussion of the financial aspect of the treatment, and videos with both high and low production values.Conclusions:The likelihood of a caregiver encountering and watching videos containing NEBP-related material when using a general query to search the internet for information on autism intervention is high. Additionally, videos regarding NEBPs have more engagements (e.g., likes, comments, shares) than videos regarding EBPs, oftentimes by multitudes. The information contained within videos in which an NEBP is the subject aligns with pre-established warning signs for pseudoscience for autism interventions, however this study also contributes new warning signs through the construction of the iterative codebook. Specifically, statements about treatment efficacy in the absence of cited research, an emotional parent testimony about the individual with autism's experience with the NEBP therapy, statements regarding behavioral improvements linked to participation in the therapy oftentimes in the context of inflated claims, and clear and confident statements regarding the cause of autism with no cited research.

Standalone-Intelligent Reflecting Surface Control Method Using Hierarchical Exploration by Beamwidth Expansion and Environment-Adaptive Codebook

Intelligent reflecting surfaces (IRS) have garnered attention for the high-capacity and reliable communication required by Beyond 5G. However, IRS control in existing studies assumes information sharing and control instructions from base stations (BSs), thus requiring a backhaul link to the core network and BSs. This requires the construction of a network system, ruining the advantages of IRS, such as its low cost and installation flexibility. Therefore, a standalone-IRS, which is completely independent of the core network and BSs, is expected to maximize the performance of the IRS. However, a standalone-IRS cannot obtain sufficient information for control decisions, such as channel state information, which is frequently used in existing studies; therefore, inefficient control methods, such as exploration, must be used. To this end, this study proposes a significant reduction in the overhead required for exploration by hierarchical exploration and a reduction in the exploration itself using codebooks to store the results of exploration. Furthermore, to prevent the degradation of communication quality due to the use of hierarchical exploration and codebooks, we propose an effective beamwidth expansion method for hierarchical exploration and an efficient codebook construction method based on user distribution as elemental methods. In this study, we evaluated the overhead reduction and communication quality of the standalone-IRS control by combining these methods through simulations.

Hybrid character sums and near-optimal partial Hadamard codebooks

In this paper, we first study exponential sums of the form∑x∈Fqψ(f(x))χ(g(x)) called hybrid character sums, where f,g are functions over finite field Fq, ψ is a nontrivial multiplicative character and χ is a nontrivial additive character of Fq. The absolute values or explicit values of them are determined under certain conditions. We then present several constructions of complex codebooks which are also called frames. The proposed codebooks are constructed from Hadamard matrices with binary row selection sequences. The hybrid character sums play an important role in determining the maximum magnitude of the inner products between vectors in these codebooks. It is proved that the magnitude of the inner products between distinct code vectors of each codebook is two-valued, and the maximum magnitude of each codebook asymptotically achieves the Welch bound equality. The constructed codebooks are equivalent to nearly equiangular tight frames and have nice application in compressed sensing matrices.

Evidence-Based Quality Improvement (EBQI) in the pre-implementation phase: key steps and activities.

Evidence-Based Quality Improvement (EBQI) involves researchers and local partners working collaboratively to support the uptake of an evidence-based intervention (EBI). To date, EBQI has not been consistently included in community-engaged dissemination and implementation literature. The purpose of this paper is to illustrate the steps, activities, and outputs of EBQI in the pre-implementation phase. The research team applied comparative case study methods to describe key steps, activities, and outputs of EBQI across seven projects. Our approach included: (1) specification of research questions, (2) selection of cases, (3) construction of a case codebook, (4) coding of cases using the codebook, and (5) comparison of cases. The cases selected included five distinct settings (e.g., correction facilities, community pharmacies), seven EBIs (e.g., nutrition promotion curriculum, cognitive processing therapy) and five unique lead authors. Case examples include both community-embedded and clinically-oriented projects. Key steps in the EBQI process included: (1) forming a local team of partners and experts, (2) prioritizing implementation determinants based on existing literature/data, (3) selecting strategies and/or adaptations in the context of key determinants, (4) specifying selected strategies/adaptations, and (5) refining strategies/adaptations. Examples of activities are included to illustrate how each step was achieved. Outputs included prioritized determinants, EBI adaptations, and implementation strategies. A primary contribution of our comparative case study is the delineation of various steps and activities of EBQI, which may contribute to the replicability of the EBQI process across other implementation research projects.

Optimization-Based Phase-Shift Codebook Design for Large IRSs

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of pre-designed phase-shift configurations for the IRS unit cells. Since the complexity of online optimization and the overhead for channel estimation scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We consider both continuous and discrete phase-shift designs and formulate the codebook construction as optimization problems. To solve the optimization problems, we propose an optimal algorithm for the discrete phase-shift design and a locally optimal solution for the continuous design. Simulation results show that the proposed algorithms facilitate the construction of codebooks of different sizes and with different beamwidths. Moreover, the performance of the discrete phase-shift design with 2-bit quantization is shown to approach that of the continuous phase-shift design. Finally, our simulation results show that the proposed designs enable large transmit power savings compared to the existing linear and quadratic codebook designs.

A Construction of Codebooks Asymptotically Meeting the Levenshtein Bound

A Construction of Codebooks Asymptotically Meeting the Levenshtein Bound

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique.

DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde–Buzo–Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.

Ball-Tree-Based Signal Detection for LMA MIMO Systems

In this letter, we consider the uplink multiuser load-modulated array (LMA) multiple-input multiple-output (MIMO) communication system, where multiple users transmit the phase modulation on hypersphere (PMH) signals to a base station (BS). To reduce the detection complexity, we propose a low-complexity detection scheme based on a ball-tree (BT) structure. The received codewords are stored in a space BT data structure and the detection process can be regarded as a nearest neighbor point search operation. Two partition rules are proposed in the BT codebook construction. Simulation results show that the proposed BT based signal detection achieves about the same bit error rate (BER) performance as the ML detection while the detection complexity is significantly reduced.

Canonical Conditions for K/2 Degrees of Freedom

We present a condition for 1=2 degree of freedom for each user in constant K-user single-antenna interference channels. This condition is sufficient for all and necessary for almost all channel matrices. Moreover, it applies to all channel topologies, i.e., to fully-connected channels as well as channels that have individual links absent, reflected by corresponding zeros in the channel matrix. Moreover, it captures the essence of interference alignment by virtue of being expressed in terms of a generic injectivity condition that guarantees separability of signal and interference. Finally, we provide codebook constructions achieving 1=2 degree of freedom for each user for all channel matrices satisfying the condition we identified.

Stbc-Based Hybrid Precoding with Dft Codebook Construction in Massive Mimo Systems

Stbc-Based Hybrid Precoding with Dft Codebook Construction in Massive Mimo Systems

HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde–Buzo–Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.

An Efficient BoF Representation for Object Classification

The Bag-of-features (BoF) approach has proved to yield better performance in a patch-based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as scale-invariant feature transform and speeded up robust features, extracted from images to create a BoF vector) leads to huge computational cost and an increased storage requirement. This paper demonstrates a two-staged approach to creating a discriminative and compact BoF representation for object classification. As a preprocessing stage to the codebook construction, ambiguous patch-based descriptors are eliminated using an entropy-based and one-pass feature selection approach, to retain high-quality descriptors. As a post-processing stage to the codebook construction, a subset of codewords which is not activated enough in images are eliminated from the initially constructed codebook based on statistical measures. Finally, each patch-based descriptor of an image is assigned to the closest codeword to create a histogram representation. One-versus-all support vector machine is applied to classify the histogram representation. The proposed methods are evaluated on benchmark image datasets. Testing results show that the proposed methods enables the codebook to be more discriminative and compact in moderate sized visual object classification tasks.

Parametric codebook design for efficient signal transmission using uniform circular arrays

In order to improve the quality of the received signal and system spectral efficiency, accurate beamforming using a given antenna array is essential for multiple-input multiple-output (MIMO) systems. To obtain desired MIMO transmission performance, construction of codebooks which are composed of matching beamforming vectors to the array structure is important. To effectively cover different types of mobile traffic, the base station for 5G new radio employs antenna arrays in various sizes and shapes. Nevertheless, the codebooks adopted by the 3GPP standard so far are based on the uniform linear array and the uniform planar array, necessitating design techniques for a wider class of antenna arrays. In this paper, we propose codebook construction methods for the uniform circular array with parameters to flexibly set the initial phase and step size based on the channel characteristics of the user equipment (UE). When tested over the 3GPP spatial channel model, the proposed codebooks show a substantial amount of gain over the conventional codebooks in all UE locations within the cell.

TCD-CF: Triple cross-domain collaborative filtering recommendation

• A triple cross-domain collaborative filtering recommendation method is proposed. • An extended codebook construction algorithm is designed. • A transfer learning method based on extended codebook is presented. • Empirical results show the proposed method is superior to state-of-the-art methods. Recently, data sparsity is still one of the critical problems faced by recommendation systems. Although many existing methods based on cross-domain can alleviate it to a certain extent, these methods only use the information of single-domain (e.g., user-side, item-side and rating-side) or dual-domain (e.g., user-rating-side, user-item-side and item-rating-side) to make recommendations, which results in performance degradation . In this paper, we propose a triple cross-domain collaborative filtering method to alleviate data sparsity, named TCD-CF. In TCD-CF method, the triple-side intrinsic characteristics are first obtained by using the joint nonnegative matrix factorization to integrate the user-side, item-side and rating-side domain knowledge. Then the extended codebook (as knowledge to transfer) based on these intrinsic characteristics is constructed by using the orthogonal nonnegative matrix tri-factorization. Finally, the codebook-based transfer method for cross-system CF is applied into the source domain and target domain to predict the missing ratings and perform recommendation in the target domain. Extensive experiments on two real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the cross-domain recommendation task.

Power Efficiency, Overhead, and Complexity Tradeoff of IRS Codebook Design—Quadratic Phase-Shift Profile

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of predesigned phase-shift configurations for the IRS unit cells. Since the overhead for channel estimation and the complexity of online optimization for IRS-assisted communications scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We show that there exists a fundamental tradeoff between power efficiency and the size of the codebook. We first analyze this tradeoff for baseline designs that employ a linear phase-shift across the IRS. Subsequently, we show that an efficient design for small codebooks mandates higher-order phase-shift variations across the IRS. Consequently, we propose a quadratic phase-shift design, derive its coefficients as a function of the codebook size, and analyze its performance. Our simulation results show that the proposed design yields a higher power efficiency for small codebooks than the linear baseline designs.

Improved bag-of-features using grey relational analysis for classification of histology images

An efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods.

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images. It remains a major challenge to process, store and transmit such huge volumes of microarray images. So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily. Various techniques have been proposed in the past with applications in different domains. The current research paper presents a novel image compression technique i.e., optimized Linde–Buzo–Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for compressing microarray images without any loss of quality. LBG model is generally used in designing a local optimal codebook for image compression. Codebook construction is treated as an optimization issue and can be resolved with the help of Grey Wolf Optimization (GWO) algorithm. Once the codebook is constructed by LBG-GWO algorithm, LZMA is employed for the compression of index table and raise its compression efficiency additionally. Experiments were performed on high resolution Tissue Microarray (TMA) image dataset of 50 prostate tissue samples collected from prostate cancer patients. The compression performance of the proposed coding esd compared with recently proposed techniques. The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.