High Bit Research Articles

Optimization of LDPC decoding algorithm in semi-conductor storage based on artificial neural networks

ABSTRACT Low-density parity check digit is a linear block code, which is applied to semiconductor storage to achieve effective information storage. However, in semiconductor storage, the existing ways have weak decoding performance and high bit error rate. Therefore, the decoding algorithm in the existing low-density parity check digit is optimised. Then, a semiconductor storage decoding optimisation algorithm based on artificial neural networks is designed to improve decoding performance. In addition, a multi-layer perceptron neural network is introduced to construct an optimised decoding method. The experimental results showed that when the threshold was 5, the throughput of the low-density parity check decoding algorithm based on the multi-layer perceptron decreased slowly with the increase in the signal-to-noise ratio. The minimum throughput rate was 1.18. The proposed semiconductor storage decoding optimisation algorithm based on artificial neural networks has better decoding performance. In summary, the proposed decoding optimisation method for semiconductor storage has good performance, which can effectively achieve information decoding in semiconductor storage.

Neuronal Imaging at 8-Bit Depth to Combine High Spatial and High Temporal Resolution With Acquisition Rates Up To 40 kHz.

A challenge in neuroimaging is acquiring frame sequences at high temporal resolution from the largest possible number of pixels. Measuring 1%-10% fluorescence changes normally requires 12-bit or higher bit depth, constraining the frame size allowing imaging in the kHz range. We resolved Ca2+ or membrane potential signals from cell populations or single neurons in brain slices by acquiring fluorescence at 8-bit depth and by binning pixels offline, achieving unprecedented frame sizes at kHz rates. In hippocampal slices stained with the Ca2+ indicator Fluo-4 AM, we resolved transients at 2 kHz from large frames. Along the apical dendrite of a layer-5 pyramidal neuron, we measured Ca2+ signals associated with a back-propagating action potential at 10 kHz. Finally, in the axon initial segment of the same cell type, we recorded an action potential at 40 kHz by voltage-sensitive dye imaging. This approach unlocks the potential for a range of imaging measurements.

Evolutionary Bioinformatics with Veiled Biological Database for Health Care Operations

The tremendous growth of biological data processing systems in the realm of health care applications has made real-time information accessible to everyone with no processing lags. Bioinformatics is even integrated into most wireless technology applications to account for all physical characteristics. The planned model focuses on evolutionary bioinformatics for medical sensor applications in health care. The optimization scenario is executed by combining genetic and ant colony optimization methods (GACO). In the proposed technique, the design concerns are implemented with appropriate transmitting and receiving modules, and individual bits are framed for extra bioinformatics data processing components. a design that completely minimizes all errors in the big data processing stage. Such a design completely lowers the overall error in the huge data processing state since all channels can be accessed in accordance with the framed bits. Furthermore, the quality of service is maximized because all channels carrying bioinformatics data are kept at high quality bits, increasing utility rates. The experiments were conducted using five scenarios to evaluate the effectiveness of the proposed design. The findings indicate that the proposed technique can handle bioinformatics data for healthcare in real time with a service quality of 95%.

Bit efficiency of distributed- and collocated-massive MIMO base station systems in OTA measurement and simulation

Abstract This paper presents the bit efficiency of 28 GHz digital beamforming in over-the-air (OTA) measurements and simulations for distributed massive multiple-input–multiple-output (D-MIMO) and collocated massive multiple-input–multiple-output (C-MIMO) systems, as well as simulations for a 3.75 GHz small-cell scenario. Under the condition that users are randomly located in the line of sight coverage indoor area and spatially selected from each other by the normalized zero-forcing method, the OTA measured D-MIMO system exhibits an average of 4–7 dB better signal-to-noise ratio compared to C-MIMO when the number of simultaneously connected users “K” approaches the number of transceivers “M.” This means that the D-MIMO system provides higher bit efficiency than the C-MIMO system when K/M is large. Furthermore, the D-MIMO 3.75 GHz simulation predicts a relatively approximate 30% higher maximum efficiency than C-MIMO due to the shorter average distances between user equipment and access points in the D-MIMO system. To the best of the author’s knowledge, an earlier version of this paper has been presented at the 53rd European Microwave Conference as a first report on the 28 GHz OTA measured bit efficiency between C-MIMO and D-MIMO, highlighting D-MIMO’s advantage.

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Investigation of Multiple LED Array Failure on DCO-FBMC Modulation for Indoor Visible Light Communication

Visible light communication (VLC) is a high-speed and power-efficient optical wireless communication method that has gained momentum for data transmission in indoor environments. Indoor VLC systems often use multiple LED arrays to increase the uniformity of brightness and enhance the received energy and the four LED array model is the most commonly used. To suppress the impact of intersymbol interference (ISI) and achieve higher data rates, multicarrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) have been introduced in VLC systems. The FBMC technique is a promising candidate for the 5G communication system and it has recently been applied to VLC systems. FBMC has several advantages: higher spectrum efficiency, narrower out-of-band radiations and asynchronous transmission. This study investigates the performance of an FBMC-based VLC system when one or more lamps in the multiple LED arrays experience failure. The VLC- FBMC model was implemented using a 4-QAM and 16-QAM format with three various LED array configurations while assuming a line of sight (LOS) model. The LED array model was evaluated based on its ability to achieve good bit error rate (BER) performance, communication quality and high bit rate and meet the required signal-to-noise ratio (SNR). The proposed FBMC model simulation's outcome indicates that the system is still able to transmit data reliably, even in the event of a failure of up to 50% of the lamps. The results indicate that the 2- and 4-lamp models yielded good BER performance, achieving a value of 3.6 × 10−5 and 5.4 × 10−5 respectively. Furthermore, the simulation demonstrated that the optical FBMC-based VLC system attained a high bit rate of 73.2 Mbit/s in the 4-QAM format using the 2-lamp model and a bit rate of 29.3 Mbit/s in 16-QAM format using the 4-lamp model, while maintaining acceptable BER performance.

Forecasting Power Consumption in the DSL Access Network System based on Network Activities by using Machine Learning

The widespread telecommunication networks and Internet services like Digital Subscriber Line (DSL) access has contributed to the growing demand of electricity. With environmental consciousness arising from non-renewable energy sources, coupled with rising demand, industries need to implement green strategies. This research focuses on predicting power consumption in DSL access networks using machine learning approaches. With an emphasis on Very High Bit Rate Digital Subscriber Line 2 (VDSL2) technology, the study investigates DSL modem’s power usage under an ideal and a bridge tap faulty conditions of copper cable along distances ranging from 100 to 1000 meters. The different levels of network activities are generated by IxChariot software and Pearson’s correlation techniques are used to analyse the relationship between power consumption and network activities. Combining the Train-Valid-Test and Random Forest algorithms, a predictive Machine Learning model is developed to forecast the power consumption based on relevant variables. The results show that power consumption tends to increase as the network activities are heavier and the proposed model presents a low minimum absolute error as a good model to forecast the power. It may aid Internet service providers to predict the optimum power limit in the network systems and pursue further investigation on how to reduce electricity use in the telecommunication field.

GAN-based multi-view video coding with spatio-temporal EPI reconstruction

The introduction of multiple viewpoints in video scenes inevitably increases the bitrates required for storage and transmission. To reduce bitrates, researchers have developed methods to skip intermediate viewpoints during compression and delivery, and ultimately reconstruct them using Side Information (SInfo). Typically, depth maps are used to construct SInfo. However, these methods suffer from reconstruction inaccuracies and inherently high bitrates. In this paper, we propose a novel multi-view video coding method that leverages the image generation capabilities of Generative Adversarial Network (GAN) to improve the reconstruction accuracy of SInfo. Additionally, we consider incorporating information from adjacent temporal and spatial viewpoints to further reduce SInfo redundancy. At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize a convolutional network to extract the latent code of a GAN as SInfo. At the decoder, we combine the SInfo and adjacent viewpoints to reconstruct intermediate views using the GAN generator. Specifically, we establish a joint encoder constraint for reconstruction cost and SInfo entropy to achieve an optimal trade-off between reconstruction quality and bitrate overhead. Experiments demonstrate the significant improvement in Rate–Distortion (RD) performance compared to state-of-the-art methods.

Evaluating the effectiveness of Doppler frequency shift determination using pilots in broadband transmission

In underwater communications, reciprocal motion between transmitter and receiver has a significant impact on reception quality. In orthogonal broadband systems that provide high bit rates, this problem becomes more important, especially in the higher frequency range, where the absolute Doppler shift is the greatest. Due to the low propagation speed of acoustic wave underwater, a substantial difference exists between the Doppler shift for lower and upper frequencies of the utilized spectrum. Consequently, a frequency-independent Doppler shift factor is employed. One of the most popular methods for determining the Doppler shift is the use of pilots. The problem of selecting the number and determining the frequency of pilots in such a way as to obtain the lowest possible error rate was identified. Real-world testing was conducted in a multipath propagation environment with relative speeds of up to 1.5 m/s. The effectiveness of Doppler shift determination was evaluated by analyzing the bit error rate. The results of the conducted tests indicate that, based on the achieved error rate, it is sufficient to employ 7 pilots positioned at low frequencies.

A Novel 3D 2TnC FeRAM Architecture and Operation Scheme with Improved Disturbance for High-Bit-Density Dynamic Random-Access Memory

In this paper, we proposed the development of stackable 3D ferroelectric random-access memory (FeRAM), with two select transistors and n capacitors (2TnC), to address scaling limitations for bit density growth and the complicated manufacturing of 3D dynamic random-access memory (DRAM). The proposed 3D FeRAM has a 3D NAND-like architecture, with stacked metal–ferroelectric–metal (MFM) capacitors serving as memory cells in a unit string. A similar manufacturing process is used to achieve a cost-effective process and high bit density for next-generation DRAM applications. The two access transistors, string–select–line (SSL) and ground–select–line (GSL), are perfect string selections. We confirmed that the grounded back gate (GBG) of the proposed architecture can significantly improve the worst disturbance case compared to a floating back gate (FBG) like the 1TnC structure. Also, we confirmed the feasibility of performing the random-access operation during the read operation regardless of the data pattern of the selected string.

A novel carrier index M‐ary differential chaos shift keying modulation scheme

AbstractTo obtain better spectral efficiency and higher bit rate, a novel carrier index M‐ary differential chaos shift keying modulation scheme is proposed. In this system, part of subcarriers is assigned to the chaotic reference so that it can carry extra data bits through carrier index modulation, while one of the remaining subcarriers is activated to transmit the data‐bearing signal. In addition, M‐ary DCSK modulation is also applied to data‐bearing signals based on the constellation theory and Walsh codes. Theoretical bit error rate expressions are derived over the multipath Rayleigh fading and additive white Gaussian noise channels. Simulations and comparisons are performed with various combinations of chaotic sequence lengths, subcarrier numbers and constellation sizes. Results show that the proposed scheme can outperform other counterparts in both spectral efficiency and bit error rate performances.

Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules

ABSTRACTCurrently, researchers and commercial entities worldwide are highly interested in optical wireless communication links operating in terrestrial environments. These links offer the distinct advantage of enabling high‐speed data transmission while keeping operational and installation costs low, all without requiring licensing. Free‐space optical (FSO) communication has established itself as a reliable method for delivering ultra‐high data‐rate services. However, the presence of atmospheric turbulence‐induced fading poses a significant challenge for FSO communication systems, particularly when operating over channels affected by time‐varying turbulence. The effectiveness of FSO systems is greatly influenced by atmospheric conditions in a specific area because the laser beam propagates through the atmosphere. As a consequence, this can lead to a significant degradation such as high complexity, high estimation error, and high bit error rate (BER) in the performance of FSO systems. This paper presents a stacked dual attention LSTM network with layered neural Turing machine (SDALSTM‐LaNTM) for FSO channel estimation over lognormal turbulence‐induced fading channels. In this approach, the stacked structure ensures a robust feature extraction capacity of SDALSTM and enhances its ability to capture intricate relationships among multiple atmospheric variables. After employing the stacked LSTM networks, the extracted features are combined with the augmented features obtained through the dual attention (DAttn) mechanism by concatenation. Then the LaNTM architecture used external memory to solve the channel parameter estimation problem and update new set of hidden features for estimation task. The ability to access and update memory enables the model to capture long‐term dependencies and context, making it beneficial for channel prediction in FSO systems. Presently, we are in the process of verifying the channel estimation model using measured FSO channel data. Simulation results clearly demonstrate the outstanding estimation performance of the SDALSTM‐LaNTM model in FSO systems, specifically when dealing with lognormal turbulence‐induced fading channels. Moreover, we highlight the effectiveness of the SDALSTM‐LaNTM model by conducting a comprehensive comparison with existing models, considering factors such as average capacity performance, outage probability estimation, and BER.

Sensory-substitution based sound perception using a spinal computer–brain interface

Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task—where chance-level performance was 33.3%—participants (n=8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=8$$\\end{document}) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task (n=5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=5$$\\end{document}) confirmed that reduced bitrates significantly impaired participants’ ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.

Blind Recognition of Frame Synchronization Based on Deep Learning.

In this paper, a deep-learning-based frame synchronization blind recognition algorithm is proposed to improve the detection performance in non-cooperative communication systems. Current methods face challenges in accurately detecting frames under high bit error rates (BER). Our approach begins with flat-top interpolation of binary data and converting it into a series of grayscale images, enabling the application of image processing techniques. By incorporating a scaling factor, we generate RGB images. Based on the matching radius, frame length, and frame synchronization code, RGB images with distinct stripe features are classified as positive samples for each category, while the remaining images are classified as negative samples. Finally, the neural network is trained on these sets to classify test data effectively. Simulation results demonstrate that the proposed algorithm achieves a 100% probability in frame recognition when BER is below 0.2. Even with a BER of 0.25, the recognition probability remains above 90%, which exhibits a performance improvement of over 60% compared with traditional algorithms. This work addresses the shortcomings of existing methods under high error conditions, and the idea of converting sequences into RGB images also provides a reliable solution for frame synchronization in challenging communication environments.

A color image encryption algorithm based on a novel 4D hyperchaotic system and bit-level diffusion

As digital images are widely used in social media, medical, military and other fields, ensuring the privacy and security of image data has become a critical concern. Firstly, we propose a novel four-dimensional hyperchaotic system and validate that it exhibits a broad chaotic range, as demonstrated by bifurcation diagrams and Lyapunov exponent experiments. Additionally, simulated circuit diagrams verify the hardware feasibility of the proposed system. Secondly, we design a dynamic iterative scrambling (DIS) scheme that dynamically divides the image into multiple matrices for spatially indexed scrambling. Excellent substitution performance can be ensured by multiple iterations. In the diffusion stage, a multidirectional bit-level L-shaped (MDBL) scheme is proposed. Diffusion is conducted on the bit plane using a designed cross-multiplanar selection algorithm, which fuses the high and low bit planes, thereby enhancing the diffusion performance of MDBL. Thirdly, Based on the above concepts, a novel four-dimensional hyperchaotic system and an encryption algorithm based on bit-level diffusion are proposed. Finally, experimental results and security analyses demonstrate the effectiveness of the novel 4D hyperchaotic system and image encryption scheme. The proposed encryption scheme exhibits robust anti-interference capabilities and effectively safeguards image security.

Novel Minimalist Hardware Architecture for Long Sync Word Frame Synchronization and Payload Capture

Sync word-based frame synchronization is an established method of frame synchronization that is employed in many low-resource applications because of its simplicity, and it involves the attachment of sync words (digital binary sequences) to the frames, which are then used for detection. Despite its simplicity, the method is underutilized today because there is no available hardware scheme that can perform correlation-based sync word frame synchronization with long sync words (>80 bits) efficiently. For this reason, the sync word methods that are used typically employ sync words of shorter lengths (<50 bits) with limited accuracy. This article introduces a highly modifiable, minimalist hardware architecture that performs correlation-based sync word frame synchronization using long sync words with evident accuracy gains over existing methods. As a bonus, the architecture not only detects the frame but also captures its payload regardless of size. Its low complexity allows for its deployment at a low cost in terms of hardware resources and power while providing high bit rates. Its flexibility is demonstrated by using a low-cost FPGA for implementation.

Secrecy Rate Maximization for Symbiotic Radio Network with Relay-Obstacle

The idea that everything can communicate with each other with high bit rate and low latency is the main goal for next generation communication systems. In this context, allocating spectrum resources and providing energy to each device that can communicate is a big problem. In order to develop different techniques in this regard, symbiotic radio networks (SRNs) have been proposed in the literature. In SRN, devices transmit information to the same receiver by using the communication infrastructure together. However, this situation may create a security problem. In this paper, SRN with relay-obstacle is proposed to test physical layer security (PLS). This model is the first approach that maximizes the secrecy rate of SRN by using the ambient radio frequency resource in the presence of relay-obstacle. There are two different clusters in the system model and each cluster contains a device, a relay and an obstacle. An eavesdropper (ED) overhearing to the signals transmitted by the relays and is blocked by a cooperative jammer. The proposed system model is mathematically modeled and the secrecy rate expression is maximized over the time parameters. In the numerical analysis, the advantages of using the channel symbiotically compared to the nonsymbiotic scenario where the energy harvest-then-transmit (HTT) protocol is used in the literature are evaluated in terms of the reflection coefficient, noise power, signal transmission power and quality of service (QoS) of the devices and its superiority is revealed.

Anti-noise performance analysis in amplitude-modulated collinear holographic data storage using deep learning.

In an amplitude-modulated collinear holographic data storage system, optical system aberration and experimental noise due to the recording medium often result in a high bit error rate (BER) and low signal-to-noise ratio (SNR) in directly read detector data. This study proposes an anti-noise performance analysis using deep learning. End-to-end convolutional neural networks were employed to analyze noise resistance in encoded data pages captured by the detector. Experimental results demonstrate that these networks effectively correct system imaging aberrations, detector light intensity response, holographic storage medium response non-uniformity, and defocusing noise from the recording objective lens. Consequently, the BER of reconstructed encoded data pages can be reduced to 1/10 of that from direct detection, while the SNR can be increased more than fivefold, enhancing the accuracy and reliability of data reading in amplitude holographic data storage systems.

ARCHITECTURE OF A DEVICE FOR MONITORING THE HEALTH OF THE HUMAN BODY BASED ON ULTRASONIC MEASUREMENTS

This article explores the development of a device architecture for non-invasive monitoring of the human body's performance based on ultrasound measurements. The implementation of this architecture is carried out using programmable integrated circuits (FPGAs). Ultrasonic sensors are connected to the FPGA to make measurements. The purpose of the study is a detailed description of the architecture of the device under development. The proposed technical device is capable of detecting pathologies in the human vascular system at a precision stage, which in turn, with surgical treatment, can increase the life expectancy of an individual. Methodology. To ensure the correct functionality of the device with high accuracy, it is necessary to ensure sufficient processing speed of the data coming from each sensor and output of the result. To increase the accuracy of the measured parameters, it is necessary to install ultrasonic sensors in the form of a phased array. Since ultrasonic sensors are analog, it is necessary to use high-frequency digital-to-analog and analog-to-digital converters with high bit depth to obtain high-quality data. These transducers are connected to ultrasonic sensors via signal amplifiers. The proposed architecture provides optimal performance and flexible configuration for measuring ultrasonic signals using sensors. The results of the study. A device demonstrating high data processing speed has been developed and experiments have been conducted on its use. The device has a compact size, which allows you to carry it on yourself without restricting movement. The scope of application. The device is designed to study the work of the heart and human cardiac activity, and is used in the field of healthcare.

Implementation of photonic crystal based optical full adder using ring resonators

In this paper, the implementation of an all-optical full adder based on a two-dimensional photonic crystal is proposed. The proposed structure consists of hexagonal lattice of silicon rods embedded in air substrate. To design the proposed optical full adder, three nonlinear ring resonators, some linear waveguides, and some point defects are used. The switching mechanism of the ring resonators is based on nonlinear Kerr effect. The performance of the proposed structure has been analyzed and investigated by using plane wave expansion (PWE) and finite difference time domain (FDTD) methods. The minimum contrast ratio of the structure is 17.02 dB. The response time of the structure is 0.724 ps and can operate at a bit rate of 1.381 Tb/s. The proposed structure is simple, compact, and offers high contrast ratio, fast response time along with high bit rate and therefore can be a promising candidate for high speed data processing, computing, networking, and optical integrated circuits.