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In this paper, we introduce the notion of π-weight distribution for linear error-Block (LEB) codes. We compute the π-weight enumerator of simplex and Hamming LEB codes. We also show that some cosets have uniquely determined distributions. We prove that if the weight distribution of the cosets is known, and that the dimension of the LEB code is increased by one, then, the π-weight can be explicitly determined. Thereafter, we compute the π-weight enumerator of the direct sum of two LEB codes. Finally, we compute the π-weight enumerator of punctured and shortened LEB codes. [ABSTRACT FROM AUTHOR]

Steganographic schemes are used in many fields, as it provides high-security transmission of messages. The strength of the steganographic scheme depends on embedding efficiency. Embedding efficiency is understood as the average number of random data bits that must be embedded in an object per one embedding change. The below-done work introduces an efficient embedding technique using various linear block codes useful to both spatial and frequency domain of digital images. Linear block coding algorithms like binary hamming code, Random linear code, Cyclic code, Reed Solomon code, etc., are applied to embed restricted data inside the image, which improves embedding efficiency. Higher embedding efficiency translates to better steganographic security. The Mean squared error (MSE), Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Error (NAE) and Correlation Coefficient values for each algorithm is calculated and compared. Histogram attack is one of the steganalysis techniques which is used in this paper to confirm the security provided to the restricted data. This comparison shows that Binary Hamming code in the spatial domain and transform domain provides better results as compared to the other coding theory algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on a linear code C with all symbol locality such that its code symbols set is partitioned to s disjoint components and every code symbol in each component j has t -pairwise disjoint recovery sets of size r j , for j ∈ { 1 , 2 , ... , s } , where r 1 , ... , r s and t are given integers. Code C is a locally recoverable code with multiple (r j) j ∈ [ s ] -localities and availability t and is defined by t − M L s − L R C. We present an upper bound on minimum Hamming distance d for s ≥ 2 and explain the result of the new bound using an example. [ABSTRACT FROM AUTHOR]

• Text-dependent speaker verification means the process of verifying the claimed identity of a person based on the pre-defined utterances for the system. • This can be done using the standard procedure or a novel method called one-for-all classification. • In this type, the difference between the features and reference values will be used and only one binary classification will be performed for all speakers of the dataset. • Significant improvements in system performance and an increase in classification duration are the results of using this method. Presenting a system for verifying the identity of people so that the data could be recorded in the simplest possible way, performing identity verification with acceptable accuracy, and being robust to attacks and noises is one of some important issues in today's era. This paper presents a text-dependent speaker verification system in which features are extracted from the data using a method called discrete wavelet transform based on linear prediction coding. In addition to the conventional classification in these systems, a new method for classification in which one binary classification will perform for all speakers of the dataset can also be done, which will be introduced in this research. Then the performance of the designed system on ASVspoof 2015, ASVspoof 2017, ASVspoof 2019, AudioMNIST, and TIMIT datasets will be evaluated using accuracy, precision, sensitivity, specificity, F1-score, and equal error rate (EER) metrics. It will be shown that the EER for the case where classification is done once for each speaker is 0.14 ± 0.83 % for the ASVspoof 2017 dataset and the accuracy and EER for the case where classification is done once for all speakers is 100 ± 0.00 % and 0.00 ± 0.00 % for all datasets. [ABSTRACT FROM AUTHOR]

Classifying bubbles in liquids is a crucial problem that is demanded within multiple fields. This paper discusses a new method for classifying bubble sizes in non-contact and inexpensive approach using ultrasound analysis. Exploiting the principle of buoyancy, free rising bubbles with larger volumes elevate faster to the surface compared to the smaller ones, given that they have the same densities. An envelope detector is proposed which tracks the changes in the ultrasound signals reflected by the bubbles when they cross the ultrasound field. These changes in the reflected signals are distinctive for the sizes under consideration. Relevant spectral and linear predictive coding features that represent the distinct characteristics are extracted. These features are fed to a feed-forward artificial neural network to successfully classify air bubbles according to their sizes with an accuracy of 98.8%. This method provides promising applications to be implemented in industrial, biomedical and environmental fields. [ABSTRACT FROM AUTHOR]

A code whose every codeword symbol can be recovered from other certain symbols is called a locally repairable code (LRC). The locality of a locally repairable code for a distributed storage system is the number of nodes that participate in the repair process, which characterizes the repair cost. In this paper, firstly, some LRCs with locality r = 2 generated by special matrices are proved, and some methods of constructing generator matrix are proposed. Next, we construct generator matrices of k-dimensional optimal ternary linear codes and obtain their locality ( k ≤ 4). Many optimal codes with small locality have been found. [ABSTRACT FROM AUTHOR]

We present a transformation rule to convert linear codes into arithmetic codes. Linear codes are usually used for error detection and correction in broadcast and storage systems. In contrast, arithmetic codes are very suitable for protection of software processing in computer systems. This paper shows how to transform linear codes protecting the data stored in a computer system into arithmetic codes safeguarding the operations built on this data. Combination of the advantages of both coding mechanisms will increase the error detection capability in safety critical applications for embedded systems by detection and correction of arbitrary hardware faults. [ABSTRACT FROM AUTHOR]

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method. [ABSTRACT FROM AUTHOR]

With increasing use of data transmission between digital systems as well as subsystems, the need for more reliable communication is felt. In this research, a new approach to the linear error correcting codes is introduced. Among advantages of this technique are simpler code construction and also decoding algorithm, as well as easier understanding of the basic concept. Based on an earlier paper and also recent work done, we first discuss briefly the use of Karnaugh map and its advantages in constructing simple codes. We then prove a theorem on application of the map to multiple error correcting codes. Using these results, we propose a simple technique that leads to obtaining a code with more capabilities. We also discuss another advantage of using the Karnaugh map in error correcting codes. As an example, we discuss a special case of generating a double error correcting code and using Karnaugh map features to give it extended capabilities. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive deep learning-based unmanned aerial vehicle (UAV) receiver design for coded multiple-input multiple-output (MIMO) systems, where the noise in the systems presents some correlation among time domain, which deteriorates the system transmission performance severely. To improve the system performance, we employ the linear convolutional code at the transmitter, and then propose an adaptive deep learning based iterative UAV receiver. The iterative UAV receiver contains three parts: the detector such as zero-forcing (ZF) or minimum mean square error (MMSE) detector, the deep convolutional neural network (DCNN) which can help suppress the noise by capturing the correlation characteristics among noise, and the decoder such as Viterbi decoding. In particular, the cyclic redundancy check (CRC) appended to the code can help control the iteration of the detection, DCNN and decoding, which leads to an adaptive implementation of receiver. Simulation results demonstrate that the proposed UAV receiver can achieve a much better bit error rate (BER) performance over conventional receivers with a reduced computational complexity. [ABSTRACT FROM AUTHOR]