De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

One of the issues in Computer Vision is the automatic development of descriptions for images,sometimes known as image captioning.Deep Learning techniques have made significant progress in this area.The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem.This paper aims to find optimized models for these two subsystems.For the image feature extraction subsystem,the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image.For the caption generation lingual subsystem,this paper tested three different pre-trained language embedding models:Glove(Global Vectors for Word Representation),BERT(Bidirectional Encoder Representations from Transformers),and TaCL(Token-aware Contrastive Learning),to select from them the most accurate pre-trained language embedding model.Our experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN(Shiftedwindow)and PVT(PyramidVision Transformer)as an image feature extractor,combined with the TaCL language embedding model is the best result among the other combinations.

LLR Processing of Polar Codes in Concatenation Systems

This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)decoding.It is known that the absolute incorrect log-likelihood ratio(LLR)values from the SC decoding can be very large.This phenomenon dramatically deteriorates the error correcting performance of the outer LDPC codes.In this paper,the LLR values of polar codes are regulated by a log processing before being sent to the LDPC decoder.Simulation results show that the log processing is an efficient approach with a low optimization complexity compared with the existing procedures to improve the performance of the serial concatenation systems.

Achieving Energy Efficiency in Wireless Sensor Networks Using Dynamic Channel Polling and Packet Concatenation

There has been a significant interest of researchers to combine different schemes focused on optimizing energy performance while developing aMAC protocol for Wireless Sensor Networks(WSNs).In this paper,we propose to integrate two cross-layer schemes:dynamic channel polling and packet concatenation using a recent asynchronous MAC protocol“Adaptive&Dynamic Polling MAC”(ADPMAC).ADP-MAC dynamically selects the polling interval distribution based on characterization of incoming traffic patterns using Coefficient of variation(CV).Packet Concatenation(PC)refers to combining the individually generated data packets into a single super packet and sending it at the polling instant.Also,the Block Acknowledgement(BA)scheme has been developed for ADP-MAC to work in conjunction with the packet concatenation.The proposed schemes have been implemented in Tiny-OS for Mica2 platform and Avrora emulator has been used for conducting experiments.Simulation results have revealed that the performance both in terms of energy&packet loss improves when ADP-MAC is used in conjunction with the additional features of PC&BA.Furthermore,the proposed scheme has been compared with a stateof-art packet concatenation primitive PiP(Packet-in-Packet).It has been observed that ADP-MAC supersedes the performance of PiP in terms of PDR(Packet Delivery Ratio)due to better management of synchronization between source and sink.

Multi-Core Virtual Concatenation Scheme Considering Inter-Core Crosstalk in Spatial Division Multiplexing Enabled Elastic Optical Networks

Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks(EONs) with single mode fiber or single core fiber. In this paper, multicore virtual concatenation(MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation(SCVC) scheme, in terms of blocking probability and spectrum utilization.

On Some Properties of Graph of Prefix Code

We investigate decomposition of codes and finite languages. A prime decomposition is a decomposition of a code or languages into a concatenation of nontrivial prime codes or languages. A code is prime if it cannot be decomposed into at least two nontrivial codes as the same for the languages. In the paper, a linear time algorithm is designed, which finds the prime decomposition. If codes or finite languages are presented as given by its minimal deterministic automaton, then from the point of view of abstract algebra and graph theory, this automaton has special properties. The study was conducted using system for computational Discrete Algebra GAP. .

Improved Belief Propagation Decoder for LDPC-CRC-Polar Codes with Bit-Freezing

Though belief propagation bit-flip(BPBF)decoding improves the error correction performance of polar codes,it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding,thus resulting in high decoding complexity and latency.To alleviate this issue,we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing(LDPCCRC-Polar codes BPBFz).The proposed LDPCCRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set,i.e.,critical set(CS),and dynamically update it.The modified CS is further utilized for the identification of error-prone bits.The proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL(L=16)decoder under medium-to-high signal-to-noise ratio(SNR)regions.It gains up to 1.2dB and 0.9dB at a fixed BLER=10-4compared with BP and BPBF(CS-1),respectively.In addition,the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF,i.e.,it is 15 times faster than CA-SCL(L=16)at high SNR regions.

Breast Pathological Image Classification Based on VGG16 Feature Concatenation

Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.

基于1D-Concatenate的信道估计DNN模型优化方法

为提高DNN模型在无线通信中信道估计精度,提出一种基于1D-Concatenate的信道估计DNN模型优化方法。该方法将Concatenate进行一维(1D)数据转换,以跳跃连接的方式引入DNN模型,抑制梯度消失问题,运用1D-Concatenate恢复网络训练过程中丢失的数据特征,提高DNN信道估计精度。为验证优化方法的有效性,选取较典型的基于DNN的无线通信信道估计模型进行对比仿真实验。实验结果表明,本文提出的优化方法对已有DNN模型的估计增益提升可达77.10%,在高信噪比下信道增益提升可达3 dB。该优化方法能有效提高DNN模型在无线通信中的信道估计精度,特别是高信噪比下提升效果显著。

Nannochloropsis artificial chromosomes(Nanno ACs)loom on the horizon

Species in genus Nannochloropsis,especially N.oceanica and N.gaditana,have been evolving as the model microalgae for both application and theory studies.The position effect of genome integration,the carrying capability limitation of integrative vectors and the instability of non-integrative vectors have hindered Nannochloropsis genetic modification with concatenate genes and extremely long DNA fragments.The molecular tools including genetic transformation,homologous recombination,gene edition,gene stacking and episome vectors for transient gene expression and diverse reporters and selection markers have been rapidly developing in Nannochloropsis species.The construction of animal and plant artificial chromosomes with“top down”strategy has set fine examples for the construction of Nannochloropsis artificial chromosomes(NannoACs).It seems that the methods and materials to set the foundation for constructing NannoACs are at hands.In this review,we outlined the current status of transgenes in Nannochloropsis species,summarized the limitations of both integrative and non-integrative vectors,and proposed a tentative approach to construct NannoACs by doubling and stabilizing the genome first,and then truncating the natural chromosomes.NannoACs once constructed will facilitate transferring the desired traits and concatenate genes into Nannochloropsis genetic backgrounds,thus contributing towards its genetic improvement and synthetic biological studies.

Text Extraction with Optimal Bi-LSTM

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the images.Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results.This study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain characters.Then pass them to deep learning layers to extract feature maps and reform the predicted words.Bidirectional Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training phase.The Connectionist Temporal Classification(CTC)function calcifies the training and validation loss rates.In addition to decoding the extracted feature to reform characters again and linking them according to their time sequence.The proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)datasets.The result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and OLRelu.In the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

Improved Design and SOVA Algorithm for Serial Concatenated Convolutional Code

To improve the performance of the short interleaved serial concatenated convolutional code(SCCC) with low decoding iterative times, the structure of Log MAP algorithm is introduced into the conventional SOVA decoder to improve its performance at short interleaving delay. The combination of Log MAP and SOVA avoids updating the matrices of the maximum path, and also makes a contribution to the requirement of short delay. The simulation results of several SCCCs show that the improved decoder can obtain satisfied performance with short frame interleaver and it is suitable to the high bit rate low delay communication systems.

Concatenated Trellis Coding and CPM for DS/SSMA Communications in Rayleigh Fading Channel

In this paper, direct sequence spread spectrum multiple access (DS/SSMA) communication system employing serially concatenated trellis coded modulation (TCM) and continuous phase modulation (CPM) over flat Rayleigh fading channel are presented. The performance of this concatenated TCM/CPM DS/SSMA system is exploited by the theoretical analysis and numerical simulations. The results demonstrate that significant improvements in error probability of this DS/SSMA system over the system with single TCM or CPM of different modulation indices can be achieved under the same conditions.

Improved Authenticated Multi-Key Agreement Protocol

Zhou et al give an attack on Ham＇s modified authenticated multi-key agreement protocol, and give a protocol that can prevent the unknown key-share attack. The paper points out that the protocol is vulnerable to a concatenation attack. This paper proposes an improved authenticated multi-key agreement protocol which shows how to make Harn＇s protocol more secure by modifying the signature and verification. And this protocol can escape the concatenation attack.

Time-Domain Analysis of the Periodically Discontinuously Forced Fractional Oscillators

A new method for the solution of non-sinusoidal periodic states in linear fractionally damped oscillators is presented. The oscillator is forced by a periodic discontinuous waveform and a viscous element is taken into account. The presented method avoids completely the Fourier series calculations of the input and output oscillator waveforms. In the proposed method, the steady-state response of fractionally damped oscillator is formulated directly in the time domain as a superposition of the zero-input and forced responses for each continuous piecewise segments of the forcing waveform, separately. The whole periodic response is reached by taking into account the continuity and periodicity conditions at instants of discontinuities of the excitation and then using the concatenation procedure for all segments. The method can be applied efficiently to discontinuous and continuous non-harmonic excitations equally well. Solutions are exact and there is no need to apply any of the widely up-to-date used frequency approaches. The Fourier series is completely cut out of the oscillator analysis.

Effective method for the design of S-Boxes satisfying the strict avalanche criterion(SAC)

The strict avalanche criterion(SAC)is one of the most important cryptographic criteria for substitution boxes(S-boxes)used in many symmetric encryption systems.However,there are few constructive methods for S-boxes fulfilling the SAC until now.In this paper,to construct S-boxes satisfying the SAC directly,we generalize the concatenation techniques of Boolean functions to S-boxes.Using the idea of concatenating small variable S-boxes,we present a simple yet effective construction method of S-boxes satisfying the SAC.Finally,a simple example on how to construct SAC S-boxes with large input variables by small variables SAC S-boxes is given.

The Homotopical Proof of Π₁(<i>S</i>, <i>x_o</i>) as a Fundamental Group in a General Interval

The aim of this study is to establish that, the equivalent class which is made up of homotopic loops is a group with respect to in the general interval [m,n]. The study proved from homotopical point of view that is associative, has an identity and inverse function. The study established with proof that is a fundamental group in [m,n] ,.

Samuel Johnson on Shakespeare:“A Poet of Nature”

Though many scholars and critics have made efforts explaining its connotation and influence from philosophical,religious and historical perspective about Samuel Johnson’s statement that Shakespeare is“A Poet of Nature”,it is still worthy of discussion.As a Neoclassical critic,Johnson revealed Shakespeare’s genuine faculties through comparison with Homer following John Dryden and Alexzander Popeto illustrate the dramatic writing process.Then Shakespeare’s genius in natural acquisition of human nature and literature tradition,adaptation and concatenation to make something novelty,and selection of original language and characters is exemplified to analyze mixed genre,irregular structure,natural dialogue and characters under the principle of general nature.

SERIALLY CONCATENATED CONTINUOUS PHASE MODULATION WITH REDUCED ITERATIVE DEMODULATION AND DETECTION

A reduced state Soft Input Soft Output (SISO) a posteriori probability algorithm for Seri-ally Concatenated Continuous Phase Modulation (SCCPM) is proposed in this paper. Based on the Reduced State Sequence Detection (RSSD),it has more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number,thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the perform-ance degradation is little with proper reduction scheme.

Iterative Decoding of Parallel Concatenated Block Codes and Coset Based MAP Decoding Algorithm for F24 Code

A multi dimensional concatenation scheme for block codes is introduced, in which information symbols are interleaved and re encoded for more than once. It provides a convenient platform to design high performance codes with flexible interleaver size. Coset based MAP soft in/soft out decoding algorithms are presented for the F24 code. Simulation results show that the proposed coding scheme can achieve high coding gain with flexible interleaver length and very low decoding complexity.