Decoding Speed Research Articles

An introduction to using QR codes in web portals for synchronizing calendar events over phones

An optical label with machine-readable information about the object it is attached to is called a quick response (QR) code. QR codes frequently hold information for a tracker, locator, or identifier that directs users to a website or application. To efficiently store data, a QR code has four standardized encoding modes: kanji, byte or binary, alphanumeric, and numeric. As a means of identifying a wide range of commercial goods, including transactions, ads, and other public notices, the QR code gained popularity. In our web portal, the proposed QR code model synchronizes all the event details synchronously in the mobile calendar. QR code is used for web-to-mobile data transfer, saving events or meeting details in the mobile calendar. Anyone with a smartphone can view the data encoded in a QR code by scanning it. Although it makes it easier for end users to decode QR codes, verifying access to the encoded data is a cause for worry. Our proposed model validates access to data through the QR code, allowing only authorized personnel to access data. To ensure accessibility control, the proposed model has the functionality of a one-time password (OTP) that enhances application security. The model achieved an average decoding speed of 157 milliseconds with an error rate of 0.38%.

Implementation of turbo decoding for VDES signal data segments

Abstract The International Telecommunication Union (ITU) mandates the use of turbo coding for VDES signal data segments. Previous studies primarily focused on turbo decoding for a specific link in VDES, often neglecting its applicability to all links. This paper proposes an FPGA-based implementation method for turbo code decoding, aiming to achieve fast and accurate turbo decoding of VDES signal data segments, while also ensuring compatibility with different links. The resulting turbo decoder can adapt to the different code rates and data segment lengths specified in the VDES. A comparison of the decoding speeds between DSP and FPGA confirms that the parallel computing capability of FPGA can significantly improve the decoding speed. Finally, the main onboard resources required for FPGA implementation of turbo decoding were provided.

Eukaryotic AlaX provides multiple checkpoints for quality and quantity of aminoacyl-tRNAs in translation.

Translational fidelity relies critically on correct aminoacyl-tRNA supply. The trans-editing factor AlaX predominantly hydrolyzes Ser-tRNAAla, functioning as a third sieve of alanyl-tRNA synthetase (AlaRS). Despite extensive studies in bacteria and archaea, the mechanism of trans-editing in mammals remains largely unknown. Here, we show that human AlaX (hAlaX), which is exclusively distributed in the cytoplasm, is an active trans-editing factor with strict Ser-specificity. In vitro, both hAlaX and yeast AlaX (ScAlaX) were capable of hydrolyzing nearly all Ser-mischarged cytoplasmic and mitochondrial tRNAs; and robustly edited cognate Ser-charged cytoplasmic and mitochondrial tRNASers. In vivo or cell-based studies revealed that loss of ScAlaX or hAlaX readily induced Ala- and Thr-to-Ser misincorporation. Overexpression of hAlaX impeded the decoding efficiency of consecutive Ser codons, implying its regulatory role in Ser codon decoding. Remarkably, yeast cells with ScAlaX deletion responded differently to translation inhibitor treatment, with a gain in geneticin resistance, but sensitivity to cycloheximide, both of which were rescued by editing-capable ScAlaX, alanyl- or threonyl-tRNA synthetase. Altogether, our results demonstrated the previously undescribed editing peculiarities of eukaryotic AlaXs, which provide multiple checkpoints to maintain the speed and fidelity of genetic decoding.

Building of Barcode and QR Code Reader Device

This study focuses on building a QR and barcode reader device to scan the barcode or QR code (1D/2D) and display the read information on the Liquid Crystal Display (LCD). With this device we can scan and read all the barcode and QR codes through an OLED display. The proposed device can work in the dark, have a stable performance as well as fast decoding speed. It can easily capture barcodes 1D, 2D on labels, paper, and mobile phone or computer displays sensitively and accurately.

Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency

This work introduces a silent speech interface (SSI), proposing a few-layer graphene (FLG) strain sensing mechanism based on thorough cracks and AI-based self-adaptation capabilities that overcome the limitations of state-of-the-art technologies by simultaneously achieving high accuracy, high computational efficiency, and fast decoding speed while maintaining excellent user comfort. We demonstrate its application in a biocompatible textile-integrated ultrasensitive strain sensor embedded into a smart choker, which conforms to the user’s throat. Thanks to the structure of ordered through cracks in the graphene-coated textile, the proposed strain gauge achieves a gauge factor of 317 with <5% strain, corresponding to a 420% improvement over existing textile strain sensors fabricated by printing and coating technologies reported to date. Its high sensitivity allows it to capture subtle throat movements, simplifying signal processing and enabling the use of a computationally efficient neural network. The resulting neural network, based on a one-dimensional convolutional model, reduces computational load by 90% while maintaining a remarkable 95.25% accuracy in speech decoding. The synergy in sensor design and neural network optimization offers a promising solution for practical, wearable SSI systems, paving the way for seamless, natural silent communication in diverse settings.

Decoding of compressive data pages for optical data storage utilizing FFDNet.

Coded aperture-based compression has proven to be an effective approach for high-density cold data storage. Nevertheless, its limited decoding speed represents a significant challenge for its broader application. We introduce a novel, to the best of our knowledge, decoding method leveraging the fast and flexible denoising network (FFDNet), capable of decoding a coded aperture-based compressive data page within 30.64 s. The practicality of the method has been confirmed in the decoding of monochromatic photo arrays, full-color photos, and dynamic videos. In experimental trials, the variance between decoded results obtained via the FFDNet-based method and the FFDNet-absent method in terms of average PSNR is less than 1 dB, while realizing a decoding speed enhancement of over 100-fold when employing the FFDNet-based method.

PatchDPCC: A Patchwise Deep Compression Framework for Dynamic Point Clouds

When compressing point clouds, point-based deep learning models operate points in a continuous space, which has a chance to minimize the geometric fidelity loss introduced by voxelization in preprocessing. But these methods could hardly scale to inputs with arbitrary points. Furthermore, the point cloud frames are individually compressed, failing the conventional wisdom of leveraging inter-frame similarity. In this work, we propose a patchwise compression framework called patchDPCC, which consists of a patch group generation module and a point-based compression model. Algorithms are developed to generate patches from different frames representing the same object, and more importantly, these patches are regulated to have the same number of points. We also incorporate a feature transfer module in the compression model, which refines the feature quality by exploiting the inter-frame similarity. Our model generates point-wise features for entropy coding, which guarantees the reconstruction speed. The evaluation on the MPEG 8i dataset shows that our method improves the compression ratio by 47.01% and 85.22% when compared to PCGCv2 and V-PCC with the same reconstruction quality, which is 9% and 16% better than that D-DPCC does. Our method also achieves the fastest decoding speed among the learning-based compression models.

PMRC: Prompt-Based Machine Reading Comprehension for Few-Shot Named Entity Recognition

The prompt-based method has been proven effective in improving the performance of pre-trained language models (PLMs) on sentence-level few-shot tasks. However, when applying prompting to token-level tasks such as Named Entity Recognition (NER), specific templates need to be designed, and all possible segments of the input text need to be enumerated. These methods have high computational complexity in both training and inference processes, making them difficult to apply in real-world scenarios. To address these issues, we redefine the NER task as a Machine Reading Comprehension (MRC) task and incorporate prompting into the MRC framework. Specifically, we sequentially insert boundary markers for various entity types into the templates and use these markers as anchors during the inference process to differentiate entity types. In contrast to the traditional multi-turn question-answering extraction in the MRC framework, our method can extract all spans of entity types in one round. Furthermore, we propose word-based template and example-based template that enhance the MRC framework's perception of entity start and end positions while significantly reducing the manual effort required for template design. It is worth noting that in cross-domain scenarios, PMRC does not require redesigning the model architecture and can continue training by simply replacing the templates to recognize entity types in the target domain. Experimental results demonstrate that our approach outperforms state-of-the-art models in low-resource settings, achieving an average performance improvement of +5.2% in settings where access to source domain data is limited. Particularly, on the ATIS dataset with a large number of entity types and 10-shot setting, PMRC achieves a performance improvement of +15.7%. Moreover, our method achieves a decoding speed 40.56 times faster than the template-based cloze-style approach.

Several Efficient Alternative Solutions for RS Codes in RAID 6

The RAID-6 specification requires a storage system with multiple storage devices to be able to endure the failure of any two parities. RAID-6 can be implemented using a variety of erasure coding methods, but each has drawbacks, like low fault tolerance, bad writing performance low decoding speed, and so on. In this paper, we research a variety of materials, explore RAID 6 disks to comprehend their purpose and prerequisites, and understand how to decode RS codes. In order to help people find a decoding method that is more appropriate to their own systems and actual needs, we research and compare numerous high-quality alternating decoding techniques, and the RAID 6 Liberation codes and Row-Diagonal Parity emerge as the winners. We introduce their decoding methods briefly and list their advantages and disadvantages in this paper for people to request based on their needs. Finally, we compare different decoding methods, propose practical application scenarios and future development directions for these more efficient decoding methods, and also point out their existing shortcomings.

A Comparative Study of BFV and CKKs Schemes to Secure IoT Data Using TenSeal and Pyfhel Homomorphic Encryption Libraries

Internet of things (IoT) devices and applications are on the rise, generating large amounts of sensitive and confidential data that need to be processed securely. Due to resource constraints, the data generated is often stored and processed in the cloud. The drawback of data cloud storage and processing is the fact that it can be hacked, leaked, or sold by cloud companies. Fully homomorphic encryption (FHE) allows computation on encrypted data using basic mathematical operations and has recently been successfully implemented using schemes and libraries with better performance. In this paper, the authors propose a mixture of edge-cloud-based security schemes using FHE to secure IoT data. The authors evaluate the performance of two FHE schemes (BFV and CKKS) based on data: encoding speed, encryption speed, arithmetic operations (addition and multiplication) speed, and decryption decoding speed using two Python libraries (TenSEAL and PyFHEl). The encryption and decryption are done at the edge node using a Raspberry Pi 4, while the processing is done at the cloud node using a laptop.

An Empirical Evaluation of Columnar Storage Formats

Columnar storage is a core component of a modern data analytics system. Although many database management systems (DBMSs) have proprietary storage formats, most provide extensive support to open-source storage formats such as Parquet and ORC to facilitate cross-platform data sharing. But these formats were developed over a decade ago, in the early 2010s, for the Hadoop ecosystem. Since then, both the hardware and workload landscapes have changed. In this paper, we revisit the most widely adopted open-source columnar storage formats (Parquet and ORC) with a deep dive into their internals. We designed a benchmark to stress-test the formats' performance and space efficiency under different workload configurations. From our comprehensive evaluation of Parquet and ORC, we identify design decisions advantageous with modern hardware and real-world data distributions. These include using dictionary encoding by default, favoring decoding speed over compression ratio for integer encoding algorithms, making block compression optional, and embedding finer-grained auxiliary data structures. We also point out the inefficiencies in the format designs when handling common machine learning workloads and using GPUs for decoding. Our analysis identified important considerations that may guide future formats to better fit modern technology trends.

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding

Objective. Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models. Approach. On top of GRS, JGRS implements a ‘jump mechanism’, which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding. Main results. Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS. Significance. In our experiments, we demonstrate that JGRS achieves on average 9%–20% more compressed models compared to GRS with 2–8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.

P‐19: A Design and application of MIP special driving system for low power Glass‐based products

In this paper, a design and application of low power handwritten tablet is proposed. Based on glass‐based MIP low power LCD technology for a 8‐inch product , the multi‐lane SPI series parallel decoding circuit is used to improve the decoding speed of image data transmission, At the same time, it can be refreshed in units of 1 line for partial refresh by means of address decoding. Because the communication and decoding interface of MIP LCD technology is a non‐general interface, an adaptive dedicated driving system is proposed simultaneously, including SOC, MCU, MIP LCD screen, stylus and touch sensor processing. In this driving system, SOC is transmitted to MCU through USB HS, and MCU drives MIP display by customized SPI interface. It can realize the global refresh, partial refresh and stylus touch through data processing which matching the unique data requirements of MIP and adopting multi‐buffer and thread separation. The low‐ power handwritten tablet solution proposed in this paper can significantly reduce power consumption with static power consumption of 25uW and full‐screen refresh rate reach 25Hz while partial refresh rate (100 lines) can be up to 300Hz. Combined with 8color display and stylus partial refresh application, the display content is richer and the response speed is increased by 42.8%, user experience is greatly improved.

Fast list decoders for polarization‐adjusted convolutional (PAC) codes

AbstractA latest coding scheme named polarization‐adjusted convolutional (PAC) codes is shown to approach the dispersion bound for the code (128,64) under list decoding. However, to achieve the near‐bound performance, the list size of list decoding needs to be excessively large, which leads to insufferable time complexity. In this paper, to improve the speed of list decoding, fast list decoders for PAC codes are proposed. Four types of constituent nodes are defined and fast list decoding algorithms are provided for each of them. Simulation results present that fast list decoding with three types of constituent nodes can yield exactly the same error‐correction performance as list decoding, and reduce more than 50% time steps for the code (128,64). Moreover, fast list decoding with four types of constituent nodes can further reduce decoding complexity with negligible performance degradation.

A Quality Improvement Technique in KERBEROS with Secure Authentication

Kerberos is an confirmation convention in which client and server can commonly verify to each other over an unreliable arrange association, to guarantee information judgment of the message and security of channel communications. In this paper, a modern novel is proposed to move forward Kerberos confirmation convention by utilizing Mystery Picture. This Kerberos will be altered to surrender way better security arrangements and to supply centralized get to control arrangement for each client with diverse parameters like confirmation, speed of encryption and decoding and productivity of transactions. This altered approach upgrades the secure confirmation of clients with non-reputation include by joining open key cryptography and covering up the message of differential get to control for each client by utilizing mystery picture. This differential get to control approach is being implemented straightforwardly to the clients whereas doing the verification of clients through Kerberos convention. The GnuPG has the capability to produce the key combine (ie, private key and open key) within the client itself. So this proposed framework utilized this key combine for encryption and decoding of messages and takes the advantage of non exchanging or non bringing in the private key from any gadgets or any nodes.

PyRS: Cross-platform Data Fault-tolerant Storage Library Based on RS Erasure Code

&lt;p&gt;Erasure code has been used by more and more researchers to solve the problem of efficient, reliable, and fault-tolerant data storage. However, the existing libraries based on erase code can only run on the Linux platform, and some of them need GPU support. This paper implements a cross-platform data fault-tolerant storage library based on RS erasure code, PyRS, which is running on CPU without GPU support and developed in Python. PyRS uses Vandermonde matrix as the coding matrix and Numba and NumPy libraries to speed up and optimize the program. This paper compares PyRS with Jerasure on the same Linux platform. The results show that the encoding and decoding speed of PyRS is 8 times faster than that of Jerasure. The CPU usage rate of both is about 25% and the memory usage rate of PyRS is about 5% higher than that of Jerasure. The same experiments are carried out on PyRS on the Windows platform. Experimental results show that compared with running on the Linux platform, PyRS running on the Windows platform has almost the same speed of encoding and decoding, and its CPU usage rate increases by about 15%, while its memory usage rate decreases by about 5%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

OpenVVC Decoder Parameterized and Interfaced Synchronous Dataflow (PiSDF) Model: Tile Based Parallelism.

The emergence of the new video coding standard, Versatile Video Coding (VVC), has resulted in a 40-50% coding gain over its predecessor HEVC for the same visual quality. However, this is accompanied by a sharp increase in computational complexity. The emergence of the VVC standard and the increase in video resolution have exceeded the capacity of single-core architectures. This fact has led researchers to use multicore architectures for the implementation of video standards and to use the parallelism of these architectures for real-time applications. With the strong growth in both areas, video coding and multicore architecture, there is a great need for a design methodology that facilitates the exploration of heterogeneous multicore architectures, which automatically generates optimized code for these architectures in order to reduce time to market. In this context, this paper aims to use the methodology based on data flow modeling associated with the PREESM software. This paper shows how the software has been used to model a complete standard VVC video decoder using Parameterized and Interfaced Synchronous Dataflow (PiSDF) model. The proposed model takes advantage of the parallelism strategies of the OpenVVC decoder and in particular the tile-based parallelism. Experimental results show that the speed of the VVC decoder in PiSDF is slightly higher than the OpenVVC decoder handwritten in C/C++ languages, by up to 11% speedup on a 24-core processor. Thus, the proposed decoder outperforms the state-of-the-art dataflow decoders based on the RVC-CAL model.

Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference

Objective. Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment. Approach. To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the variational Bayesian inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters. Main results. Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved. Significance. Our work can be applied to rapidly decode large-scale multichannel neural spike trains in BMIs.

Linking aberrant pauses during object naming to letter and word decoding speed in elderly with attention complaints

Attention deficit and reading difficulty are often comorbid in neuropsychiatric disorders of childhood and adolescence. Although recent research has shown how these two domains may interact in children, knowledge about such interaction in elderly is lacking. The present study tested whether this association is also present in healthy elderly with undiagnosed attention problems. Thirty-two subjects (65+ years) with life-long complaints of attention and with a Mini Mental (MMSE) cutoff of 27 points were tested with MapCog Spectra (MCS), with a word recognition test (Word Chains test) and CANTAB subtests of attention. All tests were presented on a tablet, except for the Word Chains test. The participants mean MMSE score was 29 points and their mean age was 71.5 years. Strong correlations were seen between the Word Chains test and the MCS, suggesting that a high number of aberrantly long pauses during serial naming was associated with fewer identifications of letters, words and sentences. The number of aberrant pauses was also associated with slower Reaction Time and a lower score on the Attention Shifting task of the CANTAB. The results were not associated with either gender or general intelligence. This study shows that attention is linked to decoding speed irrespective of intelligence and gender. We therefore suggest that a clinical assessment of attention deficit should also include an assessment of decoding ability, and vice versa, as these cognitive functions are strongly interdependent.