Code Modulation Research Articles

LC-NeRF: Local Controllable Face Generation in Neural Radiance Field.

3D face generation has achieved high visual quality and 3D consistency thanks to the development of neural radiance fields (NeRF). However, these methods model the whole face as a neural radiance field, which limits the controllability of the local regions. In other words, previous methods struggle to independently control local regions, such as the mouth, nose, and hair. To improve local controllability in NeRF-based face generation, we propose LC-NeRF, which is composed of a Local Region Generators Module (LRGM) and a Spatial-Aware Fusion Module (SAFM), allowing for geometry and texture control of local facial regions. The LRGM models different facial regions as independent neural radiance fields and the SAFM is responsible for merging multiple independent neural radiance fields into a complete representation. Finally, LC-NeRF enables the modification of the latent code associated with each individual generator, thereby allowing precise control over the corresponding local region. Qualitative and quantitative evaluations show that our method provides better local controllability than state-of-the-art 3D-aware face generation methods. A perception study reveals that our method outperforms existing state-of-the-art methods in terms of image quality, face consistency, and editing effects. Furthermore, our method exhibits favorable performance in downstream tasks, including real image editing and text-driven facial image editing.

Protograph LDPC Code and Shaped Index Modulation Design for Multi-Mode OAM Systems

Protograph LDPC Code and Shaped Index Modulation Design for Multi-Mode OAM Systems

Resolving Range Ambiguity Using Transmit Coding Modulation and Multi-Pulse Processing in FPC-PA Radar

Resolving Range Ambiguity Using Transmit Coding Modulation and Multi-Pulse Processing in FPC-PA Radar

A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

A machine-learning augmented mixed method for simulating α particle transport in inertial confinement fusion

The plasma heating by the α particle transport is a main self-heating source in inertial confinement fusion (ICF) that determines the capsule implosion performance. Due to the high energy of α particle and the high temperature in the ICF capsule hot spot, significant non-equilibrium effect exists and the continuum mechanics breaks down. For the numerical simulation of implosion and charged particle transport, the Boltzmann equation needs to be solved to capture the kinetic effects. However, the 7-dimensional Boltzmann equation, the highly frequent Coulomb collision, and the multi-folded integral stopping power formulation greatly limits the computational efficiency and challenges the computational power. To overcome the high computational cost of high-frequent Coulomb collisions, we propose a mixed collision model according to which the collisions are categorized into low-frequent large-angle collisions and high frequent small-angle grazing collisions. The large-angle collision process is precisely solved based on the Coulomb cross-section. For the highly frequent small-angle grazing, a statistic model is constructed with second-order accuracy in time. The mixed collision model reduces the computational cost of scattering calculation by two orders of magnitude. For the multi-folded integral stopping power formulation, a neural network is used to improve computational efficiency. Based on the proposed algorithm, we develop one-dimensional to three-dimensional module code to directly solve the α particle transport Boltzmann equation. The α transport module code is integrated into the multi-physics LARED-S program. The MC version ICF software is verified by a simulation study of the N210207 and N191110 experiment.

A Developed Polar Coded Generalized Frequency Division Multiplexing Based on Bit-Interleaved Coded Modulation

Due to the tremendous development in the wireless communication field and the wide requirements of current generations, such as high data rates, extremely low power consumption, and high performance, it is necessary to think about techniques and methods that present improvement to meet the growing requirements. The introduction of the Generalized Frequency Division Multiplexing (GFDM) technique was one of the proposed methods; it is one of the candidate schemes for the current generations and beyond. GFDM was presented as an improved method over other multiplexing techniques, as it provided an improvement in the field of Out Of Band (OOB) and Peak Average Power Ratio (PAPR). At the same time, it provided a lower performance in BER with respect to SNR. In this paper, methods of enhancement were presented based on Matlab to improve the performance of the GFDM system, which was improved by adding a coding system based on Polar Coding (PC). The proposed method is presented to improve the performance of GFDM by using the Genetic Algorithm (GA) to improve the performance of the Pulse shaping Filter (PSF) in GFDM. The channel estimation is accomplished by means of the LS method, and the Artificial Neural Network (ANN) based on feed-forward backpropagation is used to enhance the estimation process. The proposed Genetic Algorithm Artificial Neural Network Polar Code Interleaver GFDM (GNPI-GFDM) presents an enhancement over the traditional GFDM of more than 3.5dB at BER=10-4over the Rayleigh fading channel

MTL-TRANSFER: Leveraging Multi-task Learning and Transferred Knowledge for Improving Fault Localization and Program Repair

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.

A Novel OFDM Approach Using Error Correcting Codes and Continuous Phase Modulation for Underwater Acoustic Communication

A Novel OFDM Approach Using Error Correcting Codes and Continuous Phase Modulation for Underwater Acoustic Communication

PWD’S Learning App

The goal of the proposed project is to create a mobile application specifically designed to meet the educational needs of people with impairments, with an emphasis on pupils who are blind or deaf. The program consists of two modules: a Basics of Coding module for deaf users and an English Learning module for blind users. The English module includes gamification features like levels and streak systems to increase student engagement, a voice-controlled interface to enhance interaction, and customized learning algorithms to modify the complexity of the lessons based on performance. In contrast, the Coding module makes use of a visual user interface that is accessible to students who are deaf. It does this by teaching coding principles through visually appealing material and by integrating game-like elements and individualized learning algorithms. Prioritizing inclusiveness and accessibility, both modules guarantee a flawless mobile user experience. To improve the learning process, additional features like progress monitoring and user profiles are included. Based on user input, the program will be updated and improved continuously to ensure that it continues to be relevant and successful in fostering diversity and skill development in education. Keywords— PWD, Gamified Education, Hearing Listening and Blind problem.

Engineering Ribosomal Machinery for Noncanonical Amino Acid Incorporation.

The introduction of noncanonical amino acids into proteins has enabled researchers to modify fundamental physicochemical and functional properties of proteins. While the alteration of the genetic code, via the introduction of orthogonal aminoacyl-tRNA synthetase:tRNA pairs, has driven many of these efforts, the various components involved in the process of translation are important for the development of new genetic codes. In this review, we will focus on recent advances in engineering ribosomal machinery for noncanonical amino acid incorporation and genetic code modification. The engineering of the ribosome itself will be considered, as well as the many factors that interact closely with the ribosome, including both tRNAs and accessory factors, such as the all-important EF-Tu. Given the success of genome re-engineering efforts, future paths for radical alterations of the genetic code will require more expansive alterations in the translation machinery.

High-Level Programming of FPGA-Accelerated Systems with Parallel Patterns

As a result of frequency and power limitations, multi-core processors and accelerators are becoming more and more prevalent in today’s systems. To fully utilize such systems, heterogeneous parallel programming is needed, but this introduces new complexities to the development. High-level frameworks such as SkePU have been introduced to help alleviate these complexities. SkePU is a skeleton programming framework based on a set of programming constructs implementing computational parallel patterns, while presenting a sequential interface to the programmer. Using the various skeleton backends, SkePU programs can execute, without source code modification, on multiple types of hardware such as CPUs, GPUs, and clusters. This paper presents the design and implementation of a new backend for SkePU, adding support for FPGAs. We also evaluate the effect of FPGA-specific optimizations in the new backend and compare it with the existing GPU backend, where the actual devices used are of similar vintage and price point. For simple examples, we find that the FPGA-backend’s performance is similar to that of the existing backend for GPUs, while it falls behind in more complex tasks. Finally, some shortcomings in the backend are highlighted and discussed, along with potential solutions.

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.

Code search engines for the next generation

Given the abundance of software in open source repositories, code search engines are increasingly turning to “big data” technologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax-based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.

Vision Transformer With Quadrangle Attention.

Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the flexibility of transformers to adapt to objects of varying sizes, shapes, and orientations. To address this issue, we propose a novel quadrangle attention (QA) method that extends the window-based attention to a general quadrangle formulation. Our method employs an end-to-end learnable quadrangle regression module that predicts a transformation matrix to transform default windows into target quadrangles for token sampling and attention calculation, enabling the network to model various targets with different shapes and orientations and capture rich context information. We integrate QA into plain and hierarchical vision transformers to create a new architecture named QFormer, which offers minor code modifications and negligible extra computational cost. Extensive experiments on public benchmarks demonstrate that QFormer outperforms existing representative vision transformers on various vision tasks, including classification, object detection, semantic segmentation, and pose estimation. The code will be made publicly available at QFormer.

대학(원)생의 대화형 인공지능 챗봇 서비스 활용 경험 사례 연구

Objectives The purpose of this study is to present how university (graduate) students use ChatGPT, an interactive artificial intelligence chatbot service, and what their perceptions are about it. Methods In order to achieve research objectives, We interviewed six college (graduate) students who had experience voluntarily using ChatGPT starting in early 2023, immediately after the launch of the ChatGPT service. Afterwards, we derived research results through the process of interview content analysis, member checking, and revision. Results First, university (graduate) students' satisfaction with their first experience of using ChatGPT influenced the frequency and reliability of subsequent ChatGPT use. Second, the purpose of using ChatGPT was information/ knowledge search, acquisition, confirmation, translation, coding and code modification, writing, foreign language learning, and brainstorming. Third, the method of use changed depending on the purpose of using ChatGPT. When brainstorming was needed, simple questions were asked about topics or keywords, and when in-depth information or knowledge was needed, the field, concept, and type of desired answer were specifically presented. When students checked their understanding or needed specific information, they first organized their thoughts and then asked questions. Additionally, they asked questions as if they were talking to a person, verified the information and answers provided by ChatGPT on their own rather than using them as is, and searched for related literature when necessary. Fourth, students recognized that the main characteristic of ChatGPT is that it provides easy and convenient access to various knowledge and information. Fifth, students mentioned that ChatGPT helps learning by saving time and acquiring ideas, but it has limitations such as acquiring incorrect knowledge and information, need for additional confirmation, and low learning effectiveness. Conclusions University (graduate) students are actively experiencing various areas of use of ChatGPT and are receiving meaningful help in learning using it. In the future, it will be necessary to clearly guide students on the strengths and limitations and discover and share various use cases so that they can use ChatGPT effectively.

Rate 5/6 TCM Code Having 64 States with 64 QAM for Fading Channel

Trellis Coded Modulation (TCM) is powerful technique employed in digital communication systems to improve the reliability and efficiency of data transmission. TCM combines error control coding and modulation schemes to achieve superior performance in challenging channel conditions. In TCM, information bits are encoded using a trellis encoder, which generates a sequence of encoded symbols. These symbols are then mapped onto a modulation scheme, such as Quadrature Amplitude Modulation (QAM) or Phase Shift Keying (PSK), to create the modulated signal. At the receiver, the received signal is demodulated and decoded using a trellis decoder, which employs maximum likelihood decoding to recover the original information bits. The trellis structure allows for efficient error correction and makes TCM particularly suitable for channels with fading, noise, and other impairments. TCM has been widely used in various communication standards and applications to enhance data transmission reliability and spectral efficiency. TCM is a coding technique that merges coding and modulation to attain substantial coding gain without compromising bandwidth efficiency. The emergence of TCM occurred in the late 1970s, and ever since, it has found extensive utilization in various contemporary information transmission systems. In this paper, a new methodology is introduced for the design of a 64-state rate 5/6 TCM code optimized for fading channels. The outcomes obtained from the study are remarkably encouraging, demonstrating coding gain of around 10 dB in comparison to uncoded 32 PSK. This substantial gain plays a pivotal role in boosting data rates, especially in situations where bandwidth-limited channels are involved.

Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation

Data transfer in a wireless channel environment is plagued by numerous security issues and power transmission loss. Channel state information is crucial to attaining the performance benefit of reconfigurable intelligent surfaces (RISs) in wireless systems. Because of the Doppler effect, mobility makes accurate channel estimate (CE) more difficult. The parameter estimation approach is employed as an alternative to direct estimation in the channel matrix to address the aforementioned problems. Also, to lower the Mean Square Error (MSE) between the estimated and original channel, training-based CE, such as Linear Minimum Mean Square Error (LMMSE), and Least Square Error (LSE), are widely used in some wireless standards. To optimize the channel, certain intelligent optimized strategies are presented. For the Optimal CE, a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE has been provided. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. Finally, the performance of the accepted model is determined in comparison to other existing techniques using a variety of metrics.