The encoded large DNA can be cloned and stored in vivo, capable of write-once and stable replication for multiple retrievals, offering potential in economic data archiving. Nanopore sequencing is advantageous in data access of large DNA due to its rapidity and long-read sequencing capability. However, the data readout is commonly limited by insertion and deletion (indel) errors and sequence assembly complexity. Here, a pragmatic soft-decision data readout is presented, achieving assembly-free sequence reconstruction, indel error correction, and ultra-low coverage data readout. Specifically, the watermark is cleverly embedded within large DNA fragments, allowing for the direct localization of raw reads via watermark alignment to avoid complex read assembly. A soft-decision forward-backward algorithm is proposed, which can identify indel errors and provide probability information to the error correction code, enabling error-free data recovery. Additionally, a minimum state transition is maintained, and a read segmentation is incorporated to achieve fast information reading. The readout assays for two circular plasmids (~51 kb) with different coding rates were demonstrated and achieved error-free recovery directly from noisy reads (error rate ~1%) at coverage of 1-4×. Simulations conducted on large-scale datasets across various error rates further confirm the scalability of the method and its robust performance under extreme conditions. This readout method enables nearly single-molecule recovery of large DNA, particularly suitable for rapid readout of DNA storage.

Abstract The difficulty of anomaly detection lies in balancing the impact of noise on the network (noise suppression) and distinguishing the real anomaly from the noise (abnormal exposure). So, a deep anomaly detector Batch Quadratic Programming (BQP) network with Maximum Entropy Constraint is proposed. It imposes quadratic programming constraints on Support Vector Data Description through the BQP output layer to achieve noise suppression. In BQP network processes’ batch data, Maximum Entropy Constraint is used to balance abnormal samples and noise. The experiment compared the shallow method with the currently popular deep method on MNIST and CIFAR‐10 data sets and proved that the BQP network with Maximum Entropy Constraint has excellent performance.

The lack of knowledge of the transmitted codeword limits the synchronization capability of the concatenated code,consisting of a low-density parity-check (LDPC) code and a watermark code, which is used to correct nonbinary insertion/deletion errors. In order to improve the synchronization error-locating capability of the watermark decoder and further decrease the error probability of the system, an iterative decoding method is presented in which the extrinsic soft information from the LDPC decoder is fed back into the watermark decoder. Simulation results show that, compared with the non- iterative counterpart, significant performance gain is achieved by employing the proposed decoding scheme.

Differential pulse-position modulation (DPPM) shows significant power and bandwidth efficiency in wireless optical communications and does not need symbol synchronization, but suffers from serious insertion/deletion errors if the low-complexity symbol-by-symbol detection is used. In this paper, the DPPM transmission scheme combining the watermark with the low-density parity-check (LDPC) code is proposed. Specifically, the equivalent source and channel models are developed to disclose the insertion/deletion characteristics. Then, trellis graphs are built based on the watermark and the equivalent source and channel models. On the trellis, Viterbi algorithm can be executed to convert the insertions/deletions to small number of substitutions, which can be finally recovered using LDPC codes. Simulation results reveal that the scheme performs well with the insertions and deletions introduced by low complexity symbol-by-symbol detection. The proposed method lays the feasibility of the practical applications of DPPM considering the complexity and significant performance.

The performance of low-density parity-check (LDPC) codes can be evaluated by using the dual adaptive importance sampling (DAIS) method. To further accelerate the convergence speed of simulation, an improved method is proposed in which the biased distribution is multiplied with an auxiliary biased function. In this proposed method, samples are limited in the restricted region, where most of the events result in decoding failure, by multiplying the original biased distribution of DAIS method by a second bias. This scheme speeds up the convergence of simulation in the restricted region. Compared with the traditional DAIS method, simulation results for two specific LDPC codes verify that this proposed method achieves the significant simulation gain without sacrificing the accuracy.

The system proposed in this study deals with pedestrian counting and detection in intelligent video surveillance systems. It is a hybrid of map‐based and detection‐based approaches, and combines the advantages of both. After the foreground objects being segmented, the map‐based module, which implicitly compensates the perspective distortion by integrally projecting the features onto a given direction, is triggered to estimate the number of pedestrians in each foreground region. Then, a class‐specific Hough forest is employed to locate individuals. Experimental results have validated our strategy. The proposed map‐based module has the ability of accurately estimating the count for each region. Also, the estimation can speed up the process of locating individuals by providing cues like the number of targets and the approximate size of each target. The proposed detection‐based module not only locates pedestrians, but deals with enhancing the accuracy of the counting as well.

Low-complexity encoders for non-binary low-density parity-check (LDPC) codes with code rate of 1/3 are implemented with Field Programmable Gate Arrays. In this design, the locations of non-zero entries in the parity-check matrix are generated on the fly with several integers called address generators. Furthermore, the non-zero entries are limited to only a pair of permuted elements and thus a single bit is used to choose the different constant coefficient multipliers rather than using the general multipliers in Galois fields. In this way, efficient encoders are implemented using only several constant-coefficient multipliers and limited memories for a type of non-binary LDPC codes, which show significant error correction performance when the code length is large.

An improved dual adaptive importance sampling (DAIS) method is proposed to accelerate the simulation of low density parity check (LDPC) codes at high Signal-to-Noise Ratios (SNRs). First, we observed an interesting phenomenon that the conditional probabilities of the error events, which are caused by different noise realizations in the same control quantity bin at the different SNRs, are nearly identical in DAIS. Then, we obtain the joint probabilities of the unconstrained simulations at the high SNRs by substituting the conditional probabilities at the high SNRs with that at the low SNRs based on the observation. This spares the decoding computations of the unconstrained simulation at the high SNRs, and therefore reduces the simulation time. For the specific LDPC code, simulation results verify that the improved method achieves significant simulation gain compared with traditional DAIS, while maintaining the same accuracy.

Minimum distance lower bounds are derived using graphical enumeration and integer programming for girth-constrained repeat-accumulate (RA) code ensembles. Moreover, the worst subgraphs in Tanner graphs leading to the minimum distance lower bounds and the probabilities they occur are analyzed.

Non-binary LDPC codes are now recognized as a potential competitor to binary coded solutions, especially when the codeword length is small or moderate. More and more works are reported with good performance/complexity tradeoffs, which make non-binary solutions interesting for practical applications, such as 4G-wireless systems or DVB-like systems. In this paper, we show that proposing non-binary LDPC codes built on finite fields is actually a limitation, both from performance and implementation points of view. By considering non-binary codes on the general linear group, we show in particular that a slight performance improvement can be obtained, compared to Galois Field codes, with reasonable additional cost in the hardware implementation. The performance gain is quite small, but comes at a slight extra decoding cost, and is obtained by proper generalization of the code optimization techniques that are standard for non-binary LDPC codes on fields.

A two-stage face recognition method is presented in this paper. In the first stage, the set of candidate patterns is narrowed down with the global similarity being taken into account. In the second stage, synergetic approach is employed to perform further recognition. Face image is segmented into meaningful regions, each of which is represented as a prototype vector. The similarity between a given region of the test pattern and a stored sample is obtained as the order parameter which serves as an element of the order vector. Finally, a modified definition of the potential function is given, and the dynamic model of recognition is derived from it. The effectiveness of the proposed method is experimentally confirmed.