Number Of Detected Errors Research Articles

МОДУЛЬНЫЕ КОДЫ С СУММИРОВАНИЕМ С ПОСЛЕДОВАТЕЛЬНОСТЬЮ ВЕСОВЫХ КОЭФФИЦИЕНТОВ, ОБРАЗУЮЩЕЙ НАТУРАЛЬНЫЙ РЯД ЧИСЕЛ ЗА ИСКЛЮЧЕНИЕМ СТЕПЕНЕЙ ДВОЙКИ

The paper presents the results of studies on the characteristics of modular weight-based sum codes, where the sequence is formed by a natural series of numbers excluding powers of two. The consideration of the studied characteristics is advisable when developing discrete systems and their diagnostic support. Catalogs of the considered codes obtained using modules M = 4, 8, 16, 32, and 64 are provided. The choice of these specific modules is due to the fact that the number of check symbols in the codewords of the considered codes is small, which minimizes structural redundancy when constructing discrete systems and their diagnostic support, resulting in self-checking, controllable, and fault-tolerant structures. Modular weight-based sum codes detect all single errors with the number of data symbols . It is also demonstrated that the considered codes detect no fewer errors than the well-known modular sum codes of single digits (classical modular sum codes). As the module value increases, the gain in the tota number of errors detected by the modular weighted codes compared to classical modular codes significantly increases. Modular weighted codes are more effective at detecting multidirectional errors of even multiplicity in data vectors containing groups of distortions {0→1, 1→0} (symmetric errors) than classical modular sum codes. However, modular weight-based sum codes are less effective in handling asymmetric errors occurring in data vectors. In an experiment with test combinational circuits, it was shown that modular weighted codes with a module M = 4 do not detect more errors occurring at circuit outputs than classical modular codes with this module. Modular weight-based sum codes and a module M = 8 have slightly better characteristics. However, a general recommendation is to use modules that are powers of two starting from M = 16. Increasing the module allows for an increase in the number of detectable errors at the outputs of benchmarks, up to 100 % coverage. Modular weight-based sum codes, with a sequence of weight coefficients forming a natural series of numbers excluding powers of two, can be effectively used in the development of discrete systems and their diagnostic support.

A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives.

Automatic worm detection to solve overlapping problems using a convolutional neural network

The nematode Caenorhabditis elegans is a powerful experimental model to investigate vital functions of higher organisms. We recently established a novel method, named "pond assay for the sensory systems (PASS)”, that dramatically improves both the evaluation accuracy of sensory response of worms and the efficiency of experiments. This method uses many worms in numbers that are impractical to count manually. Although several automated detection systems have been introduced, detection of overlapped worms remains difficult. To overcome this problem, we developed an automated worm detection system based on a deep neural network (DNN). Our DNN was based on a “YOLOv4″ one-stage detector with one-class classification (OCC) and multi-class classification (MCC). The OCC defined a single class for worms, while the MCC defined four classes for the number of overlapped worms. For the training data, a total of 2000 model sub-images were prepared by manually drawing square worm bounding boxes from 150 images. To make simulated images, a total of 10–80 model images for each class were randomly selected and randomly placed on a simulated microscope field. A total of 19,000 training datasets and 1000 validation datasets with a ground-truth bounding-box were prepared. We evaluated detection accuracy using 150 images, which were different from the training data. Evaluation metrics were detection error, precision, recall, and average precision (AP). Precision values were 0.91 for both OCC and MCC. However, the recall value for MCC (= 0.93) was higher than that for OCC (= 0.79). The number of detection errors for OCC increased with increasing the ground truth; however, that for MCC was independent of the ground truth. AP values were 0.78 and 0.90 for the OCC and the MCC, respectively. Our worm detection system with MCC provided better detection accuracy for large numbers of worms with overlapping positions than that with the OCC.

Signal detection in turbid water using temporally encoded polarimetric integral imaging.

To improve signal detection in a turbid medium, we propose temporally encoded single shot polarimetric integral imaging. An optical signal is temporally encoded using gold coded sequences and transmitted through a turbid medium. The encoded signals are captured as a sequence of elemental images by two orthogonal polarized image sensor arrays. Polarimetric and polarization difference imaging are used to suppress the partially polarized and unpolarized background noise such that only the polarized ballistic signal photons are captured at the sensor. Multidimensional integral imaging is used to obtain 4D reconstructed data, and multidimensional nonlinear correlation is performed on the reconstructed data to detect the optical signal. We compare the effectiveness of the proposed polarimetric underwater optical signal detection approach to conventional (non-polarimetric) integral imaging-based and 2D imaging-based signal detection systems. The underwater signal detection capabilities are measured through performance metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical measures, including the Kullback-Leibler divergence, signal-to-noise ratio (SNR), and peak-to-correlation energy (PCE), are also calculated to show the improved performance of the proposed system. Our experimental results show that the proposed polarimetric integral-imaging approach significantly outperforms the conventional imaging-based methods. To the best of our knowledge, this is the first report on temporally encoded single shot polarimetric integral imaging for signal detection in turbid water.

Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources.

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

Low Energy yet Reliable Data Communication Scheme for Network-on-Chip

In this paper, a low energy yet reliable communication scheme for network-on-chip is suggested. To reduce the communication energy consumption, we invoke low-swing signals for transmitting data, as well as data encoding techniques, for minimizing both self and coupling switching capacitance activity factors. To maintain the communication reliability of communication at low-voltage swing, an error control coding (ECC) technique is exploited. The decision about end-to-end or hop-to-hop ECC schemes and the proper number of detectable errors are determined through high-level mathematical analysis on the energy and reliability characteristics of the techniques. Based on the analysis, the extended single error correction double-error detecting end-to-end coding technique with three bits of error detection is used in the network layer. For minimization of the self and coupling switching capacitance activity factors, the odd, even, full invert scheme is employed in the data link layer. This coding has an inherent error detection probability for the flits, which is exploited in the suggested technique. The efficiency of the scheme is studied by using both synthetic and real traffic scenarios. The study reveals savings of up to 43% and 58%, for power dissipation and energy consumption, respectively, without any significant performance degradation and overhead in the network interface.

Conspecific presence makes exploiting cryptic prey more difficult in wild-caught nutmeg mannikins

Solitary predators respond to cryptic prey by adopting counterstrategies such as forming a search image or reducing their search rate. However, the response of group-foraging individuals to cryptic prey remains poorly studied. We investigated the effect of the presence of an experienced or a naive conspecific on the ability of nutmeg mannikins, Lonchura punctulata, to exploit millet seeds presented on cryptic and noncryptic backgrounds. The conspecific's level of experience did not affect a focal bird's foraging, so we lumped both experience condition for analysis. The presence of a competitor tended to increase the occurrences of vigilance and food searching, but it did not affect the duration of vigilance. In contrast, foraging on a cryptic background significantly affected foraging by reducing the occurrences of food searching and vigilance, and increasing the latency to eat the first seed as well as the number of detection errors. Background type and competitor presence interacted significantly for searching bout duration, number of seeds eaten and foraging efficiency. In all cases, competitor presence negatively affected foraging efficiency under cryptic backgrounds. Finally, the foraging efficiency of individuals that had previously foraged with a competitor on cryptic seeds remained low even after the competitor had been removed. Thus, costs of foraging on cryptic prey may be greater for social foragers than for solitary foragers.

Effects of screen size and task difficulty on vigilance performance of older adults.

This paper describes an experiment to elucidate the effects of aging on vigilance performance with VDTs. In addition, the experiment was undertaken to evaluate whether the aging effects are affected by the degree of task difficulty and by the screen size of the VDTs. Forty healthy male volunteers were studied in two age groups of 20 participants. In the experiment, the participants were required to perform vigilance tasks, which were presented either on a conventional 17-inch display or a large-sized 110-inch display. The 60-minute task period was divided into three 20-minute phases assigning the tasks with two scenarios of different degrees of difficulty. Performance was measured using reaction time for detection and number of detection errors. The results pointed out three effects: (1) the performance of the vigilance tasks deteriorated more markedly among the older adults than among the younger adults; (2) the between-screen differences on the vigilance performance were significant for the older adults, but not for the younger adults; (3) the effect of screen size was amplified by increasing the degree of task difficulty. These data indicate that screen size and task difficulty have an effect on vigilance performance with VDTs of older adult.