Binary Digits Research Articles

Novel Real-Time Decision-Based Carrier Tracking for Software-Defined Radios Using M-ARY QAM Modulation

In this paper, a scalable and real-time decision-based carrier tracking for software defined radio (SDR) for M-ary QAM modulation has been proposed and tested in real-time using the universal software radio peripheral (USRP)-2922. The proposed system provides real-time decision-based carrier tracking for improved efficiency and performance. The real-time transfer of data has been done using the USRP-2922 hardware using the M-ary QAM modulation scheme for data transmission. The novelty of the proposed system is that one can transfer random data (binary bits), text or an image which can be encoded using the desired forward error correction codes (FEC) namely the convolution coding/Viterbi soft decision decoding and the turbo coding/decoding. The proposed novel SDR transceiver system with real-time decision-based carrier tracking shows an improved performance for different parameters such as the bit error rate (BER) and signal to noise ratio (Eb/No).

Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection

Protein content is an essential index for evaluating rice quality. This work discussed the feasibility of rapid detection of protein content in rice using spectral data fusion technology. An improved binary particle swarm optimization algorithm (IBPSO) was proposed to select the characteristic wavelength of Raman and near-infrared spectroscopy fusion data, which improved the detection accuracy of the partial least squares correction model. The determination coefficient of prediction, root mean square error of prediction, and mean relative error of prediction of the protein content detection model established by IBPSO were 0.903, 0.235%, and 2.768%, respectively, which were better than the modeling performance of the other four algorithms. The research shows that IBPSO can efficiently acquire high correlation modeling wavelength variables through the guiding optimization of binary bits with a value of '1′. The combination of IBPSO and spectral data fusion strategy can realize the rapid detection of protein content in rice, which provides theoretical support for developing related online detection equipment.

DDQR (dynamic DNA QR coding): An efficient algorithm to represent DNA barcode sequences.

A DNA barcode is a short piece of standard DNA sequence used for species determination and discrimination. Representation of DNA barcodes is essential for DNA barcodes' applications in the transportation and recognition of biological materials. Previously, we have compared different strategies for representing the DNA barcodes. In the present study, we have developed a compression algorithm based on binary coding or Huffman coding scheme, followed by converting the binary digits into Base64 digits. The combination of this compression algorithm and the QR representation leads to the dynamic DNA QR coding algorithm (DDQR). We tested the DDQR algorithm on simulated data and real DNA barcode sequences from the commonly used plant and animal DNA barcode markers: rbcL, matK, trnH-psbA, ITS2, and COI. We compared the compression efficiency of DDQR and another state-of-the-art DNA compression algorithm GeCo3 for sequences with various base compositions and lengths. We found that DDQR had a higher compression rate than GeCo3 for DNA sequences shorter than 800 bp, which is the typical size range for DNA barcodes. We also upgraded a web server (http://www.1kmpg.cn/ddqr) that provides three functions: retrieval of DNA barcode sequences, encoding DNA barcode sequences to DDQR codes, and decoding DDQR codes to DNA barcode sequences. The DDQR algorithm and the webserver will be invaluable to applying DNA barcode technology in the food and traditional medicine industries.

Different Power Adaptive Transmission Schemes Over Alternate Rician Shadowed Fading Channels

The two-sided bimodality property of Alternate Rician Shadowed (ARS) fading channels makes it a suitable fading model for body-centric wireless communication. The ARS fading model consists of two fluctuating specular components in which only one is active at a time. To provide excellent spectral efficiency with higher quality of service, system transmitter employs an adaptive transmission scheme. This paper derives the exact closed-form expressions of the ARS fading channels capacity. Various adaptive transmission schemes such as channel inversion with a fixed-rate, truncated channel inversion with a fixed-rate, and optimal rate adaptation are considered. A comparison of these schemes is also highlighted. Performance expressions such as binary bit error rate for various coherent and non-coherent modulation schemes and the outage probability are also derived. The derived results are plotted and analyzed for varying fading parameters of the ARS channel. Finally, the results of all performance metrics are verified with Monte-Carlo simulation results.

Nanoscale topotactic phase transformation modulated by triboelectrification for high memory storage

The applications of triboelectrification have attracted much attention due to the direct regulation of charges with external mechanical stimuli. Atomic force microscopy (AFM) is an effective method to achieve controllable nanoscale friction. Herein, we demonstrated a phase transformation from the brownmillerite SrCoO2.5 (BM-SCO) to perovskite SrCoO3-δ (PV-SCO) as modulated by nanoscale friction with the contact mode AFM. Besides, such phase transformation can be reversible by applying a negative voltage. More importantly, when defining the surface potential of different phases as binary bits of “1” and “0”, we could reach high-density data storage as the surface charge is constantly written and erased in nanoscale regions. This work opens a new way for the regulation of phase transformation by triboelectrification, and realizes the surface charge patterning based on different phases at the nanoscale by AFM tip friction, which is of great significance for the application of nano devices in high-density data storage.

Adaptive HEVC Steganography Based on Steganographic Compression Efficiency Degradation Model

High Efficiency Video Coding (HEVC) places great emphasis on optimizing compression efficiency, where compression efficiency denotes file size ratio before and after compression. The current HEVC steganography is prone to cause degradation in compression efficiency. To analyze and avoid this problem, a Steganographic Compression Efficiency Degradation Model (SCEDM) is first proposed, which leverages the area ratio of different types of Coding Units (CU) as the distribution of block partitioning structure and combines with the K-L divergence to describe the compression efficiency degradation. By minimizing the output of the SCEDM, the degradation of compression efficiency caused by steganographies can be minimized. Besides, it is also proved that this minimizing process will not increase extra visual quality distortion. Based on this model, a novel adaptive steganography using HEVC intra block partitioning structure is proposed. This steganography consists of three parts: the CU Depth based Hierarchical Coding (CDHC) method, the structure merging strategy and the adaptive matching method. The CDHC method can convert secret binary bits to different block structures. The structure merging strategy improves the capacity, and the adaptive matching method minimizes the compression efficiency degradation according to the proposed SCEDM. The proposed steganography is further compared with state-of-the-art steganographies to confirm the effectiveness and advantages of the proposed model and steganography in compression efficiency, capacity, visual quality and resistance to video steganalysis.

Distributed Resilient State Estimation for Cyber-Physical Systems Against Bit Errors: A Zonotopic Set-Membership Approach

This paper addresses the distributed resilient state estimation issue for a class of large-scale cyber-physical systems (CPSs) against bit errors. Owing to the reliability of the binary data, a novel binary encoding-decoding scheme is developed to enable digital communication of the CPSs. We consider the situation where the transmitted binary bits may be flipped over noisy communication channels. The primary purpose of this paper is to design a distributed resilient estimation scheme under a bit-flipped detection mechanism to achieve a reliable estimation of the CPSs. First, by means of the Minkowski sum of sets, a novel distributed zonotopic resilient estimation scheme is proposed where the optimal estimator gain is obtained in the minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> -radius sense. Furthermore, a two-stage bit-flipped detection scheme is developed by taking advantage of 1) the prediction output zonotope and 2) the intersection between the prediction zonotope and the estimation zonotope. Furthermore, in order to improve the algorithm efficiency, a reduction operator is introduced to reduce the order of the zonotope. Finally, the IEEE 30-bus systems are applied to verify theoretical results of the developed distributed resilient estimation algorithm.

Modelling of energy productivity prediction systems of shallots classification growth phase system using convolutional neural network

The energy productivity of agriculture is the effectiveness of energy consumed to carry out agricultural processes. In that case, energy prediction is needed in the future to achieve optimal agricultural yields for the farmers, make a strategy of the environmental health based on the energy field, especially non-renewable energy like fuel and consider the impact on the environment like pesticides, fertilizer and irrigation. This study used function modelling to predict energy productivity during shallot cultivation in Hutajulu, Sumatera Utara. The closest model equation is the Cobb-Douglas Model, a general model for predicting the production yield of a farm. Furthermore, a mathematical model was developed with Convolutional Neural Network (CNN). The system classified the image data based on the phases of shallot growth using CNN to process data in images. The model prediction used Ordinary Least Square (OLS) Regression to develop the Cobb-Douglas Mathematics model. It then continued with CNN modelling stages: Data Setup, Load Data, Preprocess Image, Model Development, Train Model, Model Evaluation, Test Model and Save Model. At the image classification stage, based on the growth phase, CNN was used with a sequential model with three hidden layers. The activation function used was ReLU and sigmoid to divide the class of growth phase into two based on binary digits (0.1). The energy productivity (Yi, kg MJ-1) model was lnYI = 4.177 - 0.0152lnX1 - 0.05lnX2 + 0.02lnX3 + 0.098lnX3 - 0.882lnX3 - 0.027 lnXer with an accuracy of 100% using Jupyter Notebook. The CNN model produces a percentage accuracy for the image classification of 95.3% with 100 epochs.

PARTIAL IMAGE ENCRYPTION USING DCT IMAGE-BASED DES ALGORITHM

Recently, there has been a lot of interest in the DES algorithm, due to its importance in data encryption. DES and PIN code are always synonymous, DES has also been adopted in non-digital media such as phone lines, audio audiences and the banking industry. One of the advantages of the DES algorithm is that it works on binary numbers (1s, 0s) and bits. In our proposal, we present a new method for partial encryption-images by a specific approach from the frequencies of DCT coefficients. The best solution to such risks is encryption of all data of these applications, this procedure is inconvenient when dealing with images because if users want to obtain a vision of such data, they require a complete data processing for decryption, which requires a large processing capacity. In this paper, we show how partial encoding can meet application requirements without full encoding load, in the introduction paragraph, reference is made to diagrams explaining the combination of image coding and coding. Then, accreditation is an approach that implements partial image encoding based on JPEG. This procedure is based on a technique that aims to perform two basic conditions: The first is to keep the value of the public bit, while the second is to keep agreeing with JPEG format. We present a new proposal that includes multiple coding, enforceability, self-sufficiency, spatial selectivity, and coordination compliance. We also show how the application needs are met in real-time. When comparing this style with other technology as quad tree AES. Keywords: Partial Encryption, DCT, Selective Encryption, Pluralism DOI： https://doi.org/10.35741/issn.0258-2724.58.1.49

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an accuracy rate of 98.97%, a precision rate of 97.65%, a gesture recognition rate of 98.04%, a sensitivity rate of 96.99%, and an efficiency rate of 95.12%.

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences.

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Fully Connected Hashing Neural Networks for Indexing Large-Scale Remote Sensing Images

With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) with a supervised pairwise neural network with the aim of improving RSI retrieval performance with a few binary bits. First, a fully connected hashing neural network (FCHNN) is proposed in order to map RSI features into binary (feature-to-binary) codes. Compared with pixel-to-binary frameworks, such as DPSH (deep pairwise-supervised hashing), FCHNN only contains three fully connected layers and incorporates another new constraint, so it can be significantly accelerated to obtain desirable performance. Second, five types of image features, including mid-level and deep features, were investigated in the learning of the FCHNN to achieve state-of-the-art performances. The mid-level features were based on Fisher encoding with affine-invariant local descriptors, and the deep features were extracted by pretrained or fine-tuned CNNs (e.g., CaffeNet and VGG-VD16). Experiments on five recently released large-scale RSI datasets (i.e., AID, NWPU45, PatternNet, RSI-CB128, and RSI-CB256) demonstrated the effectiveness of the proposed method in comparison with existing handcrafted or deep-based hashing methods.

Y-Shaped Net-Based Signal Detection for OFDM-IM Systems

This letter presents the power of deep learning for signal detection in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. In this letter, we propose the Y-shaped net-based scheme with fully connected layers (Y-FC) and with bidirectional long short-term memory units (Y-BLSTM) to further optimize the data reception. In the Y-shaped scheme, the active indices and the constellation demapping are learned by two parallel sub-neural networks respectively, the output features are then concatenated and flattened to obtain binary bits. The performance is evaluated under various system parameters, such as modulation methods, number of hidden neural nodes and channel states. Simulation results show that compared with the data-driven signal detection with direct input-output, the proposed scheme achieves several dB of bit-error-rate performance improvement with almost the same running time.

A smooth PON evolution based on PAM4 complement code

We propose a smooth evolution of passive optical network (PON) based on PAM4 complement code (PCC). In PCC, a PAM4 symbol is added after every-three binary bits to complement them so the code efficiency of PCC is 75%. The PCC based PON evolution is demonstrated and tested by simulation. The tested results verify the feasibility of the proposed PCC based PON evolution. The reception for PCC is traditional binary reception (not PAM4 reception). Compared with widely applied Manchester code, PCC shows almost the same performance while the code efficiency is increased to 75%. Compared with 6b8b code whose code efficiency is also 75%, PCC shows much better performance.

On the binary digits of n and n2

Let s(n) denote the sum of digits in the binary expansion of the integer n. Hare, Laishram and Stoll (2011) studied the number of odd integers such that s(n)=s(n2)=k, for a given integer k≥1. The remaining cases that could not be treated by these authors were k∈{9,10,11,14,15}. In this paper we show that there is only a finite number of solutions for k∈{9,10,11} and comment on the difficulties to settle the two remaining cases k∈{14,15}. A related problem is to study the solutions of s(n2)=4 for odd integers. Bennett, Bugeaud and Mignotte (2012) proved that there are only finitely many solutions and conjectured that n=13,15,47,111 are the only solutions. In this paper, we give an algorithm to find all solutions with fixed sum of digits value, supporting this conjecture, as well as show related results for s(n2)=5.

Detector Processor for a 5G Base Station.

Nonlinear soft bit detection is essential for the uplink receivers of 5G base stations, especially for users around the cell edge. However, its throughput and computing complexity are always challenges for both research and industry. A low-cost and low-power parallel implementation of a soft-output detector based on sorted QR decomposition (SQRD) and the K-best breadth-first search algorithm is thus proposed to reduce computational complexity and latency. In addition, to save area and reduce latency, two improvement methods are used: (1) reduce the computing cost by saturating and truncating large values during PED computing and (2) reduce the sorting cost by using the binary bit sorting method for a reduced sample set with finite accuracy. Furthermore, a pipelined VLSI architecture is designed using 28-nm digital CMOS technology offered by Semiconductor Manufacturing International Corporation (SMIC). It can achieve a peak throughput of 6400 Mbps while consuming 153 K gates (including all flip-flops) for SMIC’s 28-nm technology and running at 800 MHz, a 32% cost reduction compared with the published reference design.

Design of a novel bilayered structure of ferromagnetic metal and nonmagnetic insulator wires while maintaining the distance between the constituent skyrmions

Magnetic-skyrmion-based racetrack memory is a high-density memory for storing information driven by low-power, wherein the arrangement of skyrmions is stored in the form of binary digit combinations. This arrangement is altered based on the skyrmion motion resulting from the influence of the spin-transfer torque. We propose a novel layered structure of a ferromagnetic metal (FM) wire/nonmagnetic insulator wire/FM wire in which the skyrmions move while maintaining the arrangement. It consists of two FM wires that are perpendicularly magnetized. One of these comprises several skyrmions, which act as binary digits (data skyrmions). The other wire constitutes a sufficient number of skyrmions that are closely packed and allow the spin-polarized current to flow. A dipole–dipole interaction acts between these two FM wires. The motion of the data skyrmion is induced by the current-induced motion of the packed skyrmion. The motion of skyrmions is simulated via micromagnetic approaches. The data skyrmions move while maintaining the distance between each skyrmion. The analog position of the data skyrmion is controlled by the current flowing through another wire. The stable data skyrmion motion has a threshold current. When the current is larger than the threshold current, the skyrmion breaks, and/or the distance between skyrmions breaks. The threshold current density is found to decrease with an increasing vacuum layer thickness and increase with increasing thickness of the FM wire flowing current. Our results show that the skyrmion motion in the proposed structure can drive skyrmions with a spin-current density lower than that of the coupled granular/continuous structure and the edge-notched nanowire structure. This structure can be used in developing low-power operational devices based on skyrmion motion.

Spin-selective thermoelectric transport along a vibrating α-helical protein molecule

Motivated by the development of bio-thermoelectricity and spin caloritronics, we studied the nonlinear spin-selective transport along a vibrating α-helical protein molecule in the presence of thermal bias by using the standard nonequilibrium Green’s function formalism. Our results demonstrate that the thermal bias induces the oscillation of spin-polarization between positive and negative values accompanied by spin current with increasing the chain length. Moreover, even for the very short preparable peptide chains, external electron-phonon interaction can give rise to the spin-selectivity, whereas characteristic electron-phonon interaction can not, but in conjunction with thermal bias, it has an important impact on the total current’s direction and the spin-polarized current intensity. Finally, the spin-polarization induced by thermal bias can be modulated by gate-bias much more easily as compared to that induced by electric bias. We conclude that by applying thermal bias and gate-bias, α-helical protein molecules are conducive to the storage of binary digits.

First Digits’ Shannon Entropy

Related to the letters of an alphabet, entropy means the average number of binary digits required for the transmission of one character. Checking tables of statistical data, one finds that, in the first position of the numbers, the digits 1 to 9 occur with different frequencies. Correspondingly, from these probabilities, a value for the Shannon entropy H can be determined as well. Although in many cases, the Newcomb–Benford Law applies, distributions have been found where the 1 in the first position occurs up to more than 40 times as frequently as the 9. In this case, the probability of the occurrence of a particular first digit can be derived from a power function with a negative exponent p > 1. While the entropy of the first digits following an NB distribution amounts to H = 2.88, for other data distributions (diameters of craters on Venus or the weight of fragments of crushed minerals), entropy values of 2.76 and 2.04 bits per digit have been found.

A 12-Bit Current-Steering DAC With Unary- Splitting -Binary Segmented Architecture and Improved Decoding Circuit Topology

This article presents a “three unary bits +five splitting bits + four binary bits” segmented 500-MS/s current-steering digital-to-analog converter (DAC). The proposed splitting decoding method is between the unary decoding and binary decoding, in terms of number of switched elements. It can optimize the differential nonlinearity and output glitches of DAC, with a more simplified circuit scale than unary decoding method. Due to the data-transmission topology, both the proposed thermometer and splitting decoders feature lower transistor count and occupy less area than other competitors. Their low latency accommodates to fast synchronization control of current source switches and its beneficial to the spurious-free dynamic range (SFDR) improvement of DAC. When implemented in a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, the 12-bit DAC occupies an active area of 536 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times \,\,340\,\,\mu \text{m}$ </tex-math></inline-formula> , with the proposed decoders occupying only 28 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times \,\,75\,\,\mu \text{m}$ </tex-math></inline-formula> . The DAC dissipates 17.20 mW at 500 MS/s with a 1.8-V analog supply and a 1.2-V digital supply. The differential and integral nonlinearities are measured to be 0.28 and 1.16 LSB, respectively. The DAC also features SFDRs of 68.83 and 62.06 dB for input signals of 6.34 and 239.74 MHz, respectively.