Lead: location estimation of WSN node using angular deviation based error correction algorithm

In this work we present a flexible, probabilistic and reference-free method of error correction for high throughput DNA sequencing data. The key is to exploit the high coverage of sequencing data and model short sequence outputs as independent realizations of a Hidden Markov Model (HMM). We pose the problem of error correction of reads as one of maximum likelihood sequence detection over this HMM. While time and memory considerations rule out an implementation of the optimal Baum-Welch algorithm (for parameter estimation) and the optimal Viterbi algorithm (for error correction), we propose low-complexity approximate versions of both. Specifically, we propose an approximate Viterbi and a sequential decoding based algorithm for the error correction. Our results show that when compared with Reptile, a state-of-the-art error correction method, our methods consistently achieve superior performances on both simulated and real data sets.

Error correction is important in classical and quantum computation. Decoherence caused by the inevitable interaction of quantum bits with their environment leads to dephasing or even relaxation. Correction of the concomitant errors is therefore a fundamental requirement for scalable quantum computation. Although algorithms for error correction have been known for some time, experimental realizations are scarce. Here we show quantum error correction in a heterogeneous, solid-state spin system. We demonstrate that joint initialization, projective readout and fast local and non-local gate operations can all be achieved in diamond spin systems, even under ambient conditions. High-fidelity initialization of a whole spin register (99 per cent) and single-shot readout of multiple individual nuclear spins are achieved by using the ancillary electron spin of a nitrogen-vacancy defect. Implementation of a novel non-local gate generic to our electron-nuclear quantum register allows the preparation of entangled states of three nuclear spins, with fidelities exceeding 85 per cent. With these techniques, we demonstrate three-qubit phase-flip error correction. Using optimal control, all of the above operations achieve fidelities approaching those needed for fault-tolerant quantum operation, thus paving the way to large-scale quantum computation. Besides their use with diamond spin systems, our techniques can be used to improve scaling of quantum networks relying on phosphorus in silicon, quantum dots, silicon carbide or rare-earth ions in solids.

Hard-decision renormalization group (HDRG) decoders are an important class of decoding algorithms for topological quantum error correction. Due to their versatility, they have been used to decode systems with fractal logical operators, color codes, qudit topological codes, and non-Abelian systems. In this work, we develop a method of performing HDRG decoding which combines strengths of existing decoders and further improves upon them. In particular, we increase the minimal number of errors necessary for a logical error in a system of linear size L from to for any We apply our algorithm to decoding quantum double models and a non-Abelian anyon model with Fibonacci-like fusion rules, and show that it indeed significantly outperforms previous HDRG decoders. Furthermore, we provide the first study of continuous error correction with imperfect syndrome measurements for the quantum double models. The parallelized runtime of our algorithm is for the perfect measurement case. In the continuous case with imperfect syndrome measurements, the averaged runtime is for Abelian systems, while continuous error correction for non-Abelian anyons stays an open problem.

Error detection and correction techniques are base for faultless data transmission. Hamming code is widely used technique for single bit error detection and correction. Aside from this, low power circuits are in a wide use too, as they can lead to many advantages like cost minimization, performance improvement, etc. Hamming distance-based error data detection and correction algorithm is presented, which has integrated low power XOR circuit. Experiments show, that by using proposed method, power consumption is being decreased by about $\sim 19$% compared with design with standard gates. However, as less low power gates are slower, delays in circuit has been increased by about $\sim 14 \%$.

Köetter and Kschischang proposed a coding algorithm for network error correction based on subspace codes, which, however, has a high communication overhead (100%). This paper improves upon their coding algorithm and presents a coding algorithm for network error correction with lower communication overhead, which is similar to the communication overhead of classical random network coding. In particular, we utilize the inherent symmetry in subspace codes to optimize the construction process, leading to a more efficient algorithm. At the same time, this paper also studies the construction problem of constant dimension subspace codes, utilizing parallel construction and multilevel construction. By exploiting the symmetry in these methods, we generalize previous results and derive new lower bounds for constant dimension subspace codes.

Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error-correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. In this paper, we evaluate errorcorrection algorithms' ability to fix errors across different types of datasets that contain various levels of heterogeneity. We perform a realistic evaluation of several error correction tools. To measure the efficacy of these techniques, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. We also identify the techniques that offer a good balance between precision and sensitivity. This highlight showcases our paper's main findings [1], showing the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology.

A method obtaining position data(x, y) of object accurately is proposed by using a pair of ultrasonic sensors composed of one transmitter and two receivers. And the driving system which controls the light of flat LED lamp locally using array of ultrasonic sensors (3 transmitters and 6 receivers) is developed. As a result, measured values of y are relatively reliable due to its small average of absolute errors of 1.03[cm]. The measured values of x have average error of 8.52[cm], and it is a large value. However, the average error is decreased by 0.65[cm] after applying algorithm for error correction. The experiments to control the light of flat LED lamp locally with algorithm for error correction are carried out. From the result, measured values of x with average error of 0.97[cm] are obtained and they are very good approximations of actual values.

In the modern age of IOT and Robotics, different intelligent entities like robots, drones, IOT nodes or smart vehicles need fast and error-free communication of data, which is predominantly in the form of images. The medium used for communication of this data is mainly wireless and it can be short distance, medium distance, long haul or even satellite links crossing the ionosphere layers. Different wireless mediums incorporate different types of noises in the images being transmitted by Drones, Robots or IOT Nodes. For better analysis and then performing subsequent action on the basis of these received images using artificial intelligence, machine learning or machine vision, it is imperative that the images transmitted are encoded and recovered as fast and as error-free as possible. Normal conventional methods use different image correction algorithms for detection and correction of errors in images. Reed Solomon codes, which are normally used for error detection and correction at data link layer in TCP/IP protocol stack, have a high probability of signal correction and are highly efficient due to their burst error detection and correction capabilities. The RS codes can be implemented where there is a large number of input symbols and noise duration is relatively small as compared to the code word. Sometimes at the receiver end, we get images which are partially corrupted and only half or some part of them is visible. Most of the filters used for image reconstruction insert the approximated bits in place of the corrupted bits by using some algorithms but if only partial part of the image is corrupted, no filter will be able to recover the images properly as it will also change the bits in the non-corrupted part of the image. We have proposed a novel approach of using RS codes for the detection and correction of errors in the images. This novel technique can be used over a variety of applications including robotics, drones, IOT nodes, smart vehicles using wireless and satellite communication, which include the transfer of images and decision making on the basis of the content of the images.

In Residue Number System (RNS), a large integer is represented as a set of smaller integers called residues. Redundant Residue Number System (RRNS) is obtained by adding some redundant residues which brings in error detection and error correction capability. This capability makes RRNS a promising way to provide a very fast arithmetic in the field of digital computers. At the receiver, RRNS codes are decoded using Chinese Remainder theorem (CRT). An alternative scheme called Mixed Radix Conversion (MRC) is also used which results in low decoding complexity when compared to CRT. This paper presents a single bit error correction algorithm using RRNS. This algorithm doesn't require table lookup which helps to save memory during implementation. Representative examples are given.

The most important features of error correction tools for sequencing data are accuracy, memory efficiency and fast runtime. The previous version of BLESS was highly memory-efficient and accurate, but it was too slow to handle reads from large genomes. We have developed a new version of BLESS to improve runtime and accuracy while maintaining a small memory usage. The new version, called BLESS 2, has an error correction algorithm that is more accurate than BLESS, and the algorithm has been parallelized using hybrid MPI and OpenMP programming. BLESS 2 was compared with five top-performing tools, and it was found to be the fastest when it was executed on two computing nodes using MPI, with each node containing twelve cores. Also, BLESS 2 showed at least 11% higher gain while retaining the memory efficiency of the previous version for large genomes. Freely available at https://sourceforge.net/projects/bless-ec dchen@illinois.edu Supplementary data are available at Bioinformatics online.

The Bose – Chaudhuri – Hocquenghem type of linear cyclic codes (BCH codes) is one of the most popular and widespread error-correcting codes. Their close connection with the theory of Galois fields gave an opportunity to create a theory of the norms of syndromes for BCH codes, namely, syndrome invariants of the G-orbits of errors, and to develop a theory of polynomial invariants of the G-orbits of errors. This theory as a whole served as the basis for the development of effective permutation polynomial-norm methods and error correction algorithms that significantly reduce the influence of the selector problem. To date, these methods represent the only approach to error correction with non-primitive BCH codes, the multiplicity of which goes beyond design boundaries. This work is dedicated to a special error-correcting code class – generic Bose – Chaudhuri – Hocquenghem codes or simply GBCH-codes. Sufficiently accurate evaluation of the quantity of such codes in each length was produced during our work. We have investigated some properties and connections between different GBCH-codes. Special attention was devoted to codes with constructive distances of 3 and 5, as those codes are usual for practical use. Their almost complete description is given in the range of lengths from 7 to 107. The paper contains a fairly clear theoretical classification of GBCH-codes. Special attention is paid to the corrective capabilities of the codes of this class, namely, to the calculation of the minimal distances of these codes with various parameters. The codes are found whose corrective capabilities significantly exceed those of the well-known GBCH-codes with the same design parameters.

Next generation sequencing (NGS) revolutionized genomic data generation by enabling high-throughput parallel sequencing, making large scale genomic data analysis a crucial task. To improve NGS data quality, we developed an efficient algorithm that uses a flexible read decomposition method to improve accuracy of error correction. We further proposed a statistical framework to differentiate infrequently observed sub reads from sequencing errors in the prevalence of genomic repeats. To enable the analysis of microbial organism composition in environmental samples, we developed a parallel solution for metagenomic sequence clustering integrating sketching, quasi-clique enumeration and MapReduce techniques.

Many operating agencies are developing computerized traffic management systems that support traffic operations as part of intelligent transportation system (ITS) user service improvements. To facilitate the prediction, diagnosis, and control decisions made with uncertain information, several numerical analysis methods are available for predictive traffic control. However, these methods cannot predict short-term traffic demand accurately. A neural network approach was proposed to improve short-term traffic demand prediction for the development of advanced traffic control strategies. A promising neural network approach to improving demand prediction and traffic data modeling to support proactive traffic control was examined. Specifically, the use of time-series detector data from a real-world freeway management system that were processed through neural network models was analyzed and the results were compared with real-world observations. In all cases, the proposed neural network approach can successfully predict traffic demand patterns. The proposed study approach of combining both neural networks and error correction was found to be promising for traffic prediction. Once the neural nets are successfully trained, the system can quickly pick up traffic demand trends for proactive traffic demand management. The error correction algorithm can further smooth out errors that may be caused by sharp neural net prediction.

The commonly used Goldschmidt's floating-point divider algorithm performs two multiplications in one iteration. In this paper, a tentative error corrected K'th Goldschmidt's floating-point number divider algorithm which performs K times multiplications in one iteration is proposed. Since the number of multiplications performed by the proposed algorithm is dependent on the input values, the average number of multiplications per an operation in single precision and double precision divider is derived from many reciprocal tables with varying sizes. In addition, an error correction algorithm, which consists of one multiplication and a decision, to get exact result in divider is proposed. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a divider unit. Also, it can be used to construct optimized approximate reciprocal tables.

In recent years, modern trams have developed rapidly and groove rails are mainly used for tramway tracks. However, there is less research on the wear detection of groove rails, and even fewer wear detection applications. With the help of the laser triangulation method, this paper developed a set of wear detection of modern tramway groove rails. Among them, a set of data filtering algorithms with self-adaptive function and combined with the geometric characteristics of the groove track are proposed to eliminate data interference points and smooth the data. At the same time, an error correction algorithm is used to correct the detection error angle caused by snake driving, vibration and sensor installation deviation. In addition, this paper also proposes a set of groove rails profile matching algorithms and wear calculation algorithms to achieve accurate measurement of the vertical wear, side wear and total wear of the grooved rail. Experimental verification shows that the wear detection algorithm has strong anti-interference ability and comprehensive data error correction ability.