Channel Capacity of Molecular Signaling via Diffusion in Confined Microenvironment

Aims: To model molecular signal propagation in confined environment. Background: Molecular communication (MC) is rooted in the concepts of understanding, modeling, and engineering information exchange among naturally and artificially synthesized nanosystems. To develop or analyze an MC system, there is the need to model the communication channel through which the molecular signal diffuse, from the transmitter to the receiver. Many models for the diffusion-based MC channel have been proposed in the literature for evaluating the performance of MC systems. Most of the contemporary works assume, and rightly so for some scenarios, that the MC channels under consideration have infinite boundaries. However, this assumption becomes invalid in bounded domains such as the interiors of natural cells and artificially synthesized nanosystems. Objective: In this paper, the model of molecular propagation in a confined. microenvironment is employ to explore the effect of such an environment on the MC system. Method: The mutual information of the channel and specifically the closed-form expression of the channel capacity of the molecular signaling in the confined geometry is derive. Result: Numerical results showing the variation in the channel capacity as the function of the channel dimension are presented. Conclusion: Results showed that the channel capacity increases with the decrease in the channel dimension. Subsequently, as the dimension of the channel tends to the nanoscale range typical of many artificially synthesized nanosystems, the effect of the channel width on the capacity and by induction on many other system metrics increases.

A Lightweight Convolutional Neural Network Based on Group-Wise Hybrid Attention for Remote Sensing Scene Classification

With the development of computer vision, attention mechanisms have been widely studied. Although the introduction of an attention module into a network model can help to improve e classification performance on remote sensing scene images, the direct introduction of an attention module can increase the number of model parameters and amount of calculation, resulting in slower model operations. To solve this problem, we carried out the following work. First, a channel attention module and spatial attention module were constructed. The input features were enhanced through channel attention and spatial attention separately, and the features recalibrated by the attention modules were fused to obtain the features with hybrid attention. Then, to reduce the increase in parameters caused by the attention module, a group-wise hybrid attention module was constructed. The group-wise hybrid attention module divided the input features into four groups along the channel dimension, then used the hybrid attention mechanism to enhance the features in the channel and spatial dimensions for each group, then fused the features of the four groups along the channel dimension. Through the use of the group-wise hybrid attention module, the number of parameters and computational burden of the network were greatly reduced, and the running time of the network was shortened. Finally, a lightweight convolutional neural network was constructed based on the group-wise hybrid attention (LCNN-GWHA) for remote sensing scene image classification. Experiments on four open and challenging remote sensing scene datasets demonstrated that the proposed method has great advantages, in terms of classification accuracy, even with a very low number of parameters.

MHANet: A hybrid attention mechanism for retinal diseases classification

With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.

Segmentation Technology of Nucleus Image Based on U-Net Network

To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operating without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can obtain a perfect CSI at no cost. Then, we provide four achievable schemes, where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay–destination link into two parts and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observations to the destination node. Numerical results show that, with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds that come quite close to the upper bound on a wide range of relevant system parameters.

Analisis Dan Evaluasi Sistem Drainase (Studi Kasus: Ruas Jl. Coklat, Kel. Bongkaran Kec. Pabean Cantikan, Kota Surabaya)

The city of Surabaya is one of the big cities in Indonesia. Like other cities, Surabaya also has problems, especially problems regarding flooding. The problem of flooding in the city of Surabaya can be classified into 2 (two). Namely, drainage problems due to natural factors and man-made. One of the roads that are still regularly flooded in Surabaya when it rains is Jalan Coklat. In this study the authors analyzed using secondary data from related agencies from field observations. These data include rainfall data, inundation data, tide data and dimensions of existing canals and other complementary structures. From these data, it can be obtained that the average rainfall value is 97.54 mm / day, the rain intensity is 214.88 mm / hour for the 10th period, while the Flood Debit Q Planning is 0.95 m3 / sec, and the discharge in the existing channel can be 0, 849 m3 / second. From the Tide Data, it is obtained that the peak of the tide is 150 cm high on December 13, 2016 and the lowest low tide is 130 cm on January 25, 2016. From the calculation and analysis results in the area that affects the occurrence of inundation is the channel dimension with Width: 50 x Height: 80 cm From the analysis of the calculation of the redesign using the box culvert, the dimensions of each box are obtained, namely the width of the box culvert: 80 x height: 100 x length: 120 cm for the 10th period with a capacity of 2.227 m3 / second. If the rainfall is high in the area and the water level in the Kalimas river is overflowing, the results of the analysis need to install a floodgate with the dimensions of the sluice gate Width: 60 x Height: 180 x Thickness: 1 cm so that when there is back water in the jl area . Brown floodgates function and when the rainfall is also high, the floodgates are already operating, so to reduce water puddles, it is necessary to use the help of a pump. From the analysis results, the pump capacity is 2.5 m3 / second.

Perencanaan Jaringan Drainase Pemukiman Pada Perumahan Istana Safira Jalan Jambu Semanding Sumbersekar Dau Kabupaten Malang

Drainage means to drain, dispose, or drain water. In general drainage can be defined as a series of water structures that function to reduce or remove excess water from an area or land, so that the land can be used optimally. The purpose of this research is to plan the drainage system of the Istana Safira Dau housing in Malang Regency, which in general is not all planned. Analysis and data management methods used are manual calculations in accordance with the rational method for calculating discharges, and the formula for finding the water level using h SNI for channel discharge. After the calculation, the primary channel dimension is obtained with the base channel width b = 0.50 m and channel depth 0.50 m, secondary channel dimensions are channel width b = 0.40 m and channel depth is 0.40 m, tertiary channel dimensions is channel width = 0.30 m and channel depth 0.30 m with guard height for each channel is 3% of the planned channel depth. Rectangular cross section of a rectangular channel.

Nonlocal spatial attention module for image classification

To enhance the capability of neural networks, research on attention mechanism have been deepened. In this area, attention modules make forward inference along channel dimension and spatial dimension sequentially, parallelly, or simultaneously. However, we have found that spatial attention modules mainly apply convolution layers to generate attention maps, which aggregate feature responses only based on local receptive fields. In this article, we take advantage of this finding to create a nonlocal spatial attention module (NL-SAM), which collects context information from all pixels to adaptively recalibrate spatial responses in a convolutional feature map. NL-SAM overcomes the limitations of repeating local operations and exports a 2D spatial attention map to emphasize or suppress responses in different locations. Experiments on three benchmark datasets show at least 0.58% improvements on variant ResNets. Furthermore, this module is simple and can be easily integrated with existing channel attention modules, such as squeeze-and-excitation and gather-excite, to exceed these significant models at a minimal additional computational cost (0.196%).