Correction Codes Research Articles

Joint Learning and Channel Coding for Error-Tolerant IoT Systems Based on Machine Learning

In several machine learning (ML)-based Internet of Things (IoT) systems, data is captured by IoT devices and then transmitted over a wireless channel for remote processing. Since noise often appears on the channel (so causing data corruption and consequently an incorrect ML result), channel protection must be provided to guarantee an acceptable error rate for the transmitted data, especially in safety-critical applications. An often-used protection technique employs error correction codes (ECCs); however, even with some improved designs, the power dissipation required by an ECC implementation may still not meet the strict requirements of hardware-constrained platforms. To address this issue, a “joint learning and channel coding” (JLCC) scheme is proposed in this paper. In such a scheme, the ML model is retrained using two methods to tolerate some channel errors, such that the system requires an ECC with significantly lower protection capability. Since ML training is executed remotely, JLCC achieves a significant power reduction for ECC without introducing any additional overhead to the IoT device. An electrocardiogram (ECG) system is taken as a case study to illustrate the proposed JLCC scheme and evaluate its effectiveness. A low-density parity-check (LDPC) code is employed for protection of the system with/without JLCC; its analysis and implementation are presented. Simulation results show that, when employing JLCC with the proposed two retraining methods, an average reduction of 29.15% and 34.82% in the dissipated power is achieved for the ECG sensor when compared to the original system.

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

utomated grading systems (autograders) assist the process of teaching in introductory programming courses (CS1). However, the sole focus on correctness can obfuscate the assessment of other characteristics present in code. In this work, we investigated if code, deemed correct by an autograder, were developed with characteristics that indicated potential misunderstandings of the concepts taught in CS1. These characteristics were named Misconceptions in Correct Code (MC³). By analyzing 2,441 codes developed by CS1 students, we curated an initial list of 45 MC³. This list was assessed by CS1 instructors, resulting in the identification of MC³ that should be addressed in classes. We selected the 15 most severe MC³ for further investigation, including a semi-structured observation in a CS1 course and an automated detection software using static code analysis. The results suggested that students develop these MC³ either due to an incomplete comprehension of the concepts taught in CS1 course or a lack of attention while elaborating their code, with correctness being their primary goal. We believe our results can contribute to: (1) the research field of misconceptions in CS1; (2) promoting alternative approaches to complement the use of autograders in CS1 classes; and (3) providing insights that can serve as the foundation for teaching interventions involving MC³ in CS1.

Optimizing Communication Systems with Applied Nonlinear Analysis Techniques

The introduction of applied nonlinear analytic techniques is a disruptive force in the field of modern communication systems, revolutionising the methodology for system optimisation. This investigation into nonlinear approaches provides a deep grasp of the complex dynamics in communication networks and opens a realm beyond the confines of conventional linear models. This paper has become a beacon, illuminating the potential of nonlinear optimisation to tackle issues inherent in linear frameworks by exploring the intricacies of nonlinear systems. Nonlinear analytic integration has impacted concrete implementations in various communication domains, surpassing theoretical limitations. The application of nonlinear optimisation has produced robust error correction schemes, optimised signal processing algorithms, and adaptive modulation techniques that can dynamically adapt to the unpredictable network landscape. These innovations span wireless and optical communication, error correction coding, and adaptive modulation techniques. But there are obstacles on this journey of transformation. Implementation challenges in real-time and computational complexity still exist. However, communication systems strengthened by nonlinear analysis have a bright future ahead of them. With new technologies like artificial intelligence (AI) and machine learning positioned to be powerful partners that may accelerate and simplify nonlinear optimisation, the field of communication systems seems set for a period of intelligent, responsive, and adaptive networks. In conclusion, a new age for communication systems is being heralded by the integration of applied nonlinear analytic techniques, which promises resilience, efficiency, and adaptability. With new technologies and methods emerging to overcome obstacles, it seems that communication systems will continue to evolve in the future, ushering in a new era of network architecture.

Coherence requirements for quantum communication from hybrid circuit dynamics

The coherent superposition of quantum states is an important resource for quantum information processing which distinguishes quantum dynamics and information from their classical counterparts. In this article we determine the coherence requirements to communicate quantum information in a broad setting encompassing monitored quantum dynamics and quantum error correction codes. We determine these requirements by considering hybrid circuits that are generated by a quantum information game played between two opponents, Alice and Eve, who compete by applying unitaries and measurements on a fixed number of qubits. Alice applies unitaries in an attempt to maintain quantum channel capacity, while Eve applies measurements in an attempt to destroy it. By limiting the coherence generating or destroying operations available to each opponent, we determine Alice’s coherence requirements. When Alice plays a random strategy aimed at mimicking generic monitored quantum dynamics, we discover a coherence-tuned phase transitions in entanglement and quantum channel capacity. We then derive a theorem giving the minimum coherence required by Alice in any successful strategy, and conclude by proving that coherence sets an upper bound on the code distance in any stabelizer quantum error correction code. Such bounds provide a rigorous quantification of the coherence resource requirements for quantum communication and error correction.

The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges due to the fundamental difference in the nature and the construct of general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this article, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models and evaluate their performance on four widely used ML libraries, with newly created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. ( Good ) Neural code generation models perform significantly better on ML tasks than on non-ML tasks with an average difference of 10.6 points in BLEU-4 scores. ( Bad ) More than 80% of the generated code is semantically incorrect. ( Bad ) Code generation models do not have significance in improving developers’ completion time. ( Good ) The generated code can help developers write correct code by providing developers with clues for using correct APIs. ( Missing ) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into API sequence identification and API usage generation.

On the ℓ-DLIPs of codes over finite commutative rings

Generalizing the linear complementary duals, the linear complementary pairs and the hull of codes, we introduce the concept of ℓ-dimension linear intersection pairs (ℓ-DLIPs) of codes over a finite commutative ring (R), for some positive integer ℓ. In this paper, we study ℓ-DLIP of codes over R in a very general setting by a uniform method. Besides, we provide a necessary and sufficient condition for the existence of a non-free (or free) ℓ-DLIP of codes over a finite commutative Frobenius ring. In addition, we obtain a generator set of the intersection of two constacyclic codes over a finite chain ring which helps us to get an important characterization of ℓ-DLIP of constacyclic codes. Finally, the ℓ-DLIP of constacyclic codes over finite chain rings are used to construct new entanglement-assisted quantum error correcting (EAQEC) codes.

Data Remanence Based Approach towards Stable Key Generation from Physically Unclonable Function Response of Embedded SRAMs using Binary Search

Today’s device authentications in IoT devices use public and private key cryptography. Nevertheless, they are still vulnerable to threats because keys or device IDs digitally stored in IoT devices can be stolen or cloned. In contrast, SRAM PUFs utilize physical variations in memory cells of embedded SRAM in microcontrollers or standalone SRAM chips. These inherent physical characteristics are unpredictable and practically impossible to duplicate. They are negligible to affect regular SRAM operation but large enough to be used for authentication purposes in SRAM PUFs operation. However, SRAM PUFs have poor stability and a relatively high bit error rate (BER). Temporal Majority Voting (TMV) and other error correction codes (ECCs) have improved SRAM PUFs performance, but they require a lot of processing time and hardware resources. The data remanence nature of SRAM cells can be utilized to select SRAM PUFs bits with much lower BER and more stable bits, but a suitable algorithm is required to find the best possible power-off time for each type of chip. This paper proposes using the data remanence method and binary search algorithm to obtain the strong SRAM PUFs characteristics of the selected SRAMs at the optimal power-off time. These SRAMs include embedded SRAMs of AtMega328P, STM32F108C, ESP8266 microcontrollers, and an off-the-shelf SRAM chip 23LC1024-I/P, which are being used in various IoT applications. The strong SRAM PUF has more stable characteristics that reduce BER to 0% and increase stability to 99.999%. This proposed method can be utilized on any IoT platform which deals with essential data and requires less resource-hungry security and authentication protocols.

AN IMPROVED POLARIZATION WEIGHTS CONSTRUCTION FOR POLAR CODES

Polar codes proposed by Arikan based on channel polarization have been proven to achieve Symmetric Binary-Discrete Input Memoryless Channels capacity. 3GPP has also chosen the Polar codes as the error correction code in the fifth mobile communication system (5G New Radio - 5G NR) with a maximum codeword length of 1024 bits. The design of the code based on the reliability of the synthetic channels has been researched with many different methods to satisfy the requirements demanded in the application of 5G NR. The Polar codes design requirements are ease of implementation, low decoding complexity, and high performance in error correction. This paper has proposed a method of designing Polar codes by determining a synthetic channels reliability index sequence based on the Polarization Weights (PW) method called the Improved Polarization Weights (IPW) method. The simulation results show that the error correction performance of the code designed according to IPW is better than the code structure selected for 5G NR and designed according to the original PW method.

ANALYSIS OF IDENTIFICATION FEATURES OF MODERN DECORATIVE TEXTILE MATERIALS FOR CUSTOMS PURPOSES

the article analyses the identification features of modern decorative textile materials used for the manufacture of curtains for declaration for customs purposes by participants of foreign economic activity. Since a large number of such materials are purchased by import in Russia, the distinctive features of the ma- terials “blackout”, “dimout”, “chenille” are shown to describe the goods and assign the correct HS code of the EAEU.

Research on double camouflage encryption mechanism of QR code based on UAV landing scenario

Usually, the landing area of the drone is presented with QR code images, so it is crucial to ensure the information security of the landing area and prevent it from being occupied by other users. This paper proposes a double camouflage encryption method of QR code based on UAV landing scenario. For the QR code image required for UAV landing, the private key and carrier image are used to complete double camouflage encryption, and then the public key is modulated according to the principle of ghost imaging to obtain the ciphertext. After receiving the ciphertext, the receiver first decrypts the camouflage image according to the public key, and then decrypts the QR code image using the private key. The UAV receives ciphertext information through the detector, for non-users, the correct QR code image cannot be decrypted through the wrong key. Even if the eavesdropper obtains the public key information, they can only decrypt the camouflage image and cannot land. For our users, the public key and the double private key can decrypt the correct QR code image for landing. This encryption method can effectively decrypt the image at non-full sampling rate, while also resisting the external noise attack, and has high security.

BEP evaluation of 5G LDPC codes in a pre-amplified optical PPM receiver

We present results for the performance of a pre-amplified optical pulse-position modulation (PPM) receiver that utilizes the low-density parity-check (LDPC) correction codes of the 5G standard. The code construction is suitable for correcting burst errors that are introduced by PPM. Simulation results show that the LDPC codes can provide a very significant real gain, provided that the code rate is chosen appropriately. We find that the code rates that minimize the bit-error-probabilities (BEPs) of the system range between 2/3 and 22/26, with higher code rates being required in receivers that operate under increased optical noise. It is also shown that a comparable real gain is obtained for both the sum–product and min-sum decoders, while the power penalty of the latter one is only limited to 0.2 dB, when the aforementioned code rates are utilized.

Deep Learning Based Pilot-Free Transmission: Error Correction Coding for Low-Resolution Reception Under Time-Varying Channels

Deep Learning Based Pilot-Free Transmission: Error Correction Coding for Low-Resolution Reception Under Time-Varying Channels

A Novel Implementation Methodology for Error Correction Codes on a Neuromorphic Architecture

The Internet of Things infrastructure connects a massive number of edge devices with an increasing demand for intelligent sensing and inferencing capability. Such data-sensitive functions necessitate energy-efficient and programmable implementations of Error Correction Codes (ECC) and decoders. The algorithmic flow of ECCs with concurrent accumulation and comparison types of operations are innately exploitable by neuromorphic architectures for energy efficient execution–an area that is relatively unexplored outside of machine learning applications. For the first time, we propose a methodology to map the hard-decision class of decoder algorithms on a neuromorphic architecture. We present the implementation of the Gallager B (GaB) decoding algorithm on a TrueNorth-inspired architecture that is emulated on the Xilinx Zynq ZCU102 MPSoC. Over this reference implementation, we propose architectural modifications at the neuron block level that result in a reduction of energy consumption by 31% with a negligible increase in resource usage while achieving the same error correction performance.

ML-driven risk estimation for memory failure in a data center environment with convolutional neural networks, self-supervised data labeling and distribution-based model drift determination

With the trend towards multi-socket server systems, the demand for random access memory (RAM) per server increased. The consequence are more DIMM sockets per server. Since every dual in-line memory module (DIMM), which comprises a series of dynamic random-access memory integrated circuits, has a probability of failure, RAM issues became a dominant failure pattern for servers. The concept introduced in this work contributes to improving the reliability of data centers by avoiding RAM failures and mitigating their impact. For this purpose, an ML-driven framework is provided to estimate the probability of memory failure for each RAM module. The ML framework is based on structural information between correctable (CE) and uncorrectable errors (UE). In a common memory scenario, a corrupted bit within a module can be restored by redundancy using an error correction code (ECC), resulting in a CE. However, if there is more than one corrupted bit within a group of bits covered by the ECC, the information cannot be restored, resulting in a UE.Consequently, the related task requesting the memory content, and the corresponding service may crash. There is evidence that UEs have a CE history and structural relation between the CEs. However, for the case of UEs without a CE history or of a false decision of the ML framework, we extend the total framework by engineering measures to mitigate the impact of a UE by avoiding kernel panic and using backups. The engineering measures use a mapping between physical and logical memory addresses.

An optimal channel coding scheme for high-speed data communication

This paper presents a novel channel-coding scheme called Binary to Gray Code Conversion based Error Detection and Correction (BGCC-EDAC) code to correct the maximum number of errors that occur at the receiving end during wireless transmission. The proposed method adds the parity bit using Binary to Gray Code Conversion (BGCC). The decoding algorithm proposed in this paper will not perform the regular Gray-to-Binary code conversion. Instead, a novel decoding algorithm is proposed to detect and correct most of the errors in the message bits. It will require retransmission only if any parity error occurs. The efficiency of the proposed channel code stems from its redundant bit calculation and decoding algorithm. The simulation results using Cadence 90 nm technology show that the proposed BGCC-EDAC code consumes low power, less area, and less propagation delay. Simulation analysis using MATLAB reveals that the proposed BGCC-EDAC outperforms the existing error-correcting codes in terms of BER performance. This makes the proposed BGCC-EDAC code, with a shorter code word length, highly suitable for high-speed, reliable data communications.

Development of an AI-based IT tool to support medical device nomenclature standardization for post-market surveillance by automated mapping from GMDN to EMDN standards

Abstract Introduction The EU Medical Device Regulation 2017/745 has defined new rules for medical device (MD) certification and post-market surveillance (PMS). In particular, a centralized database (Eudamed) has been established to collect and share information about the lifecycle of MD in EU market. To become effective for PMS, the European MD Nomenclature (EMDN), a 7-levels hierarchical system, has been adopted as a free standard among EU to describe MDs to this aim. However, Global MD Nomenclature (GMDN), a poly-hierarchical not-for-free system, was previously used in several EU Member States, and it is still adopted in other countries (i.e., UK, Australia, Brazil). A complete mapping between these nomenclatures is still missing, or not publicly and freely available, and this multiplicity of nomenclatures is going to affect the traceability of MD in PMS, ultimately generating an adverse impact on the patient. Purpose 1) To develop an IT tool to automatically map a given GMDN description to the most appropriate EMDN code, to facilitate and accelerate the uptake of EMDN and the mapping process between nomenclatures. 2) To introduce a user-science approach towards a continuous improvement of the tool, allowing user interaction to highlight potentially incorrect mapping and suggest the most appropriate one, to be further included in the database after manual verification. Methods By text analytics methods, the freely available list of MD on the Italian market, described by EMDN code, was compared with the same MD on the USA market, described by GMDN. As a result, 379317 devices were found present in both the Italian and USA markets, and 9683 different mappings between GMDN and EMDN are used to define an initial mapping from GMDN to EMDN. Then, given in input a GMDN description, semantic similarity was searched in the vocabulary for the proper GMDN with the corresponding EMDN, as well as for all EMDN descriptions, using a pre-trained AI transformer model (all-mpnet-base-v2). As a result, two possible EMDN codes with the respective reliability were given as output. The mapping performance at different EMDN levels was tested using a leave-one-out approach only on 2863 unique GMDN descriptions. Results From the GMDN descriptions, the correct EMDN code at level 1 (22 categories available) was given in 2343 cases (81.84%), at level 2 in 2088 cases (72.93%) and at level 3 in 1605 cases (56.06%). In Figure 1, an example of the developed user interface with the resulting two possible EMDN codes and their reliability, given a GMDN description of interest, is shown. The ability to select the most appropriate mapping, or to suggest a different one is given to the user, thus indirectly giving information for improving the tool. Conclusions The proposed approach provides with good accuracy the automatic mapping between GMDN and EMDN, thus potentially accelerating the nomenclature standardization process and improving effectiveness of MD PMS in EU and beyond.Figure 1

The impact of rapid diagnostic testing on hospital administrative coding accuracy for influenza.

Hospital administrative coding may underestimate the true incidence of influenza-associated hospitalisation. Earlier availability of test results could lead to improved accuracy of administrative coding. In this study we evaluated International Classification of Diseases 10 (ICD-10) coding for influenza (with [J09-J10] or without [J11] virus identified) in adult inpatients who underwent testing in the year prior, compared to those in the 2.5 years after, the introduction of rapid PCR testing in 2017. Other factors associated with influenza coding were evaluated using logistic regression. Discharge summaries were audited to assess the impact of documentation and result availability on coding accuracy. Influenza was confirmed by laboratory testing in 862 of 5755 (15%) patients tested after rapid PCR introduction compared with 170 of 926 (18%) prior. Following the introduction of rapid testing there was a significant increase in patients allocated J09 or J10 ICD-10 codes (768 of 860 [89%] vs 107 of 140 [79%], P=0.001). On multivariable analysis, factors independently associated with correct coding were rapid PCR testing (aOR 4.36 95% CI [2.75-6.90]) and increasing length of stay (aOR 1.01, 95% CI [1.00-1.01]). Correctly coded patients were more likely to have documentation of influenza in their discharge summaries (95 of 101 [89%] vs 11 of 101 [10%], P<0.001) and less likely to have pending results at discharge (8 of 101 [8%] vs 65 of 101 [61%], P<0.001). The introduction of rapid PCR testing for influenza was associated with more accurate hospital coding. One possible explanation is faster test turnaround leading to improvement in clinical documentation.

Robust image steganography against lossy JPEG compression based on embedding domain selection and adaptive error correction

Transmitting images for communication on social networks has become routine, which is helpful for covert communication. The traditional steganography algorithm is unable to successfully convey secret information since the social network channel will perform lossy operations on images, such as JPEG compression. Previous studies tried to solve this problem by enhancing the robustness or making the cover adapt to the channel processing. In this study, we proposed a robust image steganography method against lossy JPEG compression based on embedding domain selection and adaptive error correction. To improve anti-steganalysis performance, the embedding domain is selected adaptively. To increase robustness and lessen the impact on anti-steganalysis performance, the error correction capacity of the error correction code is adaptively adjusted to eliminate redundancy. The experimental results show that the proposed method achieves better anti-steganalysis and robustness.

Interleaved LDPC Decoding Scheme Improves 3-D TLC NAND Flash Memory System Performance

Although NAND flash memory does a lot of work in effectively using Error Correcting Code(ECC) to reduce Uncorrectable Bit Error Rate(UBER). However, if the Frame Error Rate(FER) is not reduced, the lower UBER cannot effectively reduce the read latency of the flash memory system. This phenomenon is especially evident at the end of the flash memory lifetime, where conventional methods significantly reduce the UBER but not to zero, and the remaining error bits are still evenly distributed throughout the flash memory page, resulting in a significant increase in read latency. In this paper, an interleaved LDPC decoding scheme is proposed. By re-evaluating the flash memory channel during the decoding process, the codewords in the flash memory page are corrected frame by frame, and the problem of high FER is solved at the end of the flash memory lifetime. Compared with the conventional algorithm, the proposed method can reduce the FER by up to 34%, reduce the average decoding iterations by 63.4%, and reduce the read latency by up to 65%.

T-depth-optimized quantum search with quantum data-access machine

Abstract Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-box operation—so-called oracle, even though this process, if not appropriately designed, may adversely diminish the quantum query advantage. Here, we introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM), and present a general architecture for quantum search algorithm. We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code. Specifically, we introduce a measure involving two computational complexities, i.e. quantum query and T-depth complexities, which can be critical to assess performance since the logical non-Clifford gates, such as the T (i.e. π / 8 rotation) gate, are known to be costliest to implement in FTQC. Our analysis shows that for N searching data, a QDAM model exhibiting a logarithmic, i.e. O ( log N ) , growth of the T-depth complexity can be constructed. Further analysis reveals that our QDAM-embedded quantum search requires O ( N × log N ) runtime cost. Our study thus demonstrates that the quantum data search algorithm can truly speed up over classical approaches with the logarithmic T-depth QDAM as a key component.