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.

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. .

Thermal Stabilization of EDFA gain by hybrid concatenation of antimony-doped silica EDF

Sb-doped silica EDF showed an opposite temperature dependent gain profile compared to Al-doped silica EDF. Concatenation of those two EDFs showed a gain variation less than ?0.4 dB over 40nm of C-band with the 15dB gain, in the temperature range of- 40 to + 80℃.

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.

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.

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.

Decoding Technique of Concatenated HadamardCodes and Its Performance

The decoding technique of concatenated Hadamard codes and its performance are studied. Efficient soft in soft out decoding algorithms based on the fast Hadamard transform are developed. Performance required by CDMA mobile or PCS speech services, e.g. , BER=10 -3 , can be achieved at Eb/No =0.9 dB using short interleaving length of 192 bits.

A Novel Concatenated Chaotic Communication System

A strategy for a novel concatenated chaotic communication system is presented. The transmitter system comprises chaotic turbo encoder and logistic CSK block in a serially concatenated form. Chaotic turbo code is capable of reducing bit error rate (BER) of the chaotic system in the AWGN channel. Through the chaotic turbo encoder, the coded sequence, which has quasi-chaotic properties, will be transmitted into the logistic CSK block. Having a very sensitive dependence on initial conditions of the map, the logistic CSK block can also be taken as the chaotic authentication method. The receiver, which has logistic demodulation block and chaotic decoder, is a linear asymptotic approximation to the inverse of the transmitter system. A chaotic iterative soft-decision decoding algorithm is also developed based on conventional maximum A posteriori decoding algorithm. At last, a two-step authentication method of this chaotic system is also presented.

Optimum of Interleaving Schemes over Combined Channels

The optimum choice of key parameters in the interleaving schemes used in RS codes/Viterbi-de-coded convolutional codes concatenated coding systems is discussed. At the same time the simulation results and other optional interleaving schemes are given.

Chemical Synthesis and Cloning of Overlapping Concatenated Human Atrial Natriuretic Factor Gene with Gene Engineering

Eight oligonucleotide fragments were designed with the aid of a computer and synthesizedaccording to the amino add sequcnce of human atrial natriuretic factor(ANF).By means of an-nealing and ligation,these fragments were assembled into an overlapping concatenator consisting oftwo ANF genes ligated by TGATG for termination and initiation of translation.Theconcatenator was omserted into plasmid pRC23 and the recobinant DNA was transformed into E.coli strain TAP106.Analysis by restriction enzyme mapping,hybridization and DNA sequenongshowed that the orientation and reading frame of the gene were correct.

Degenerate asymmetric quantum concatenated codes for correcting biased quantum errors

In most practical quantum mechanical systems,quantum noise due to decoherence is highly biased towards dephasing.The quantum state suffers from phase flip noise much more seriously than from the bit flip noise.In this work,we construct new families of asymmetric quantum concatenated codes(AQCCs)to deal with such biased quantum noise.Our construction is based on a novel concatenation scheme for constructing AQCCs with large asymmetries,in which classical tensor product codes and concatenated codes are utilized to correct phase flip noise and bit flip noise,respectively.We generalize the original concatenation scheme to a more general case for better correcting degenerate errors.Moreover,we focus on constructing nonbinary AQCCs that are highly degenerate.Compared to previous literatures,AQCCs constructed in this paper show much better parameter performance than existed ones.Furthermore,we design the specific encoding circuit of the AQCCs.It is shown that our codes can be encoded more efficiently than standard quantum codes.

Performance Evaluation of OFDM Coding System Using Concatenated BCH and LDPC Codes

This paper establishes a resilient concatenated coding platform for Orthogonal Frequency Division Multiplexing (OFDM) with Quadrature Amplitude Modulation (QAM) over Additive White Gaussian Noise (AWGN) channel. At the Forward Error Correction (FEC) coding unit, our proposed concatenated coding scheme utilizes standard type of BCH as external coding and LDPC as inner coding. In this scheme, Interleaver inclusion is seen as a catalyst to enhance the systems performance.

Turbo Coded OFDM with Large Number of Subcarriers

Wireless communication systems have greatly advanced during the last years. A significant contributor in these systems’ performance has been Orthogonal Frequency Division Multiplexing (OFDM). Since its invention, it is considered to be a technological leap. This leap in splitting an information stream in multiple frequency carriers has been adapted by various scientists working on the development of wireless systems. Moreover, as OFDM presented excellent tolerance of channel fading and noise signals, the evolvement in terms of speed and reliability was consequent, because only a small stream of information is lost due to noise effects. OFDM along with the knowledge that Turbo codes is another excellent scheme of reducing BER, has triggered us to expand our research. So, we experimented in simulation level not only in joining OFDM with Turbo Codes but even in finding a better Turbo scheme compared to a typical PCCC, SCCC and a Convolutional encoder with Viterbi decoder. As the last goal has already been accomplished, in this paper is presented the new OFDM system consisted of our Turbo scheme. The analysis of the previous system took into consideration the effects of an AWGN channel. Also, this noise analysis was conducted using a simulation platform with specific attributes such as transmitting and receiving fixed number of subcarriers (2048 carriers after IFFT block) while using different types of convolutional concatenated codes, such as PCCC (Parallel), SCCC (Serial) and the new PCCC scheme. The results clearly show not only the improvement in the BER performance of the Turbo Coded OFDM systems (compared to others consisted of Viterbi decoders) but the overall superiority of the proposed design.