Fish skin color detection plays a critical role in aquaculture. However, challenges arise from image color cast and the limited dataset, impacting the accuracy of the skin color detection process. To address these issues, we proposed a novel fish skin color detection method, termed VH-YOLOv5s. Specifically, we constructed a dataset for fish skin color detection to tackle the limitation posed by the scarcity of available datasets. Additionally, we proposed a Variance Gray World Algorithm (VGWA) to correct the image color cast. Moreover, the designed Hybrid Spatial Pyramid Pooling (HSPP) module effectively performs multi-scale feature fusion, thereby enhancing the feature representation capability. Extensive experiments have demonstrated that VH-YOLOv5s achieves excellent detection results on the Plectropomus leopardus skin color dataset, with a precision of 91.7%, recall of 90.1%, mAP@0.5 of 95.2%, and mAP@0.5:0.95 of 57.5%. When compared to other models such as Centernet, AutoAssign, and YOLOX-s, VH-YOLOv5s exhibits superior detection performance, surpassing them by 2.5%, 1.8%, and 1.7%, respectively. Furthermore, our model can be deployed directly on mobile phones, making it highly suitable for practical applications.

With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

The increase in phishing attacks through email and short message service (SMS) has shown no signs of deceleration. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases that reach end users. Without understanding these characteristics, anti-phishing countermeasures cannot evolve. In this study, we propose an approach using Twitter as a new observation point to immediately collect and characterize phishing cases via e-mail and SMS that evade countermeasures and reach users. Specifically, we propose CrowdCanary, a system capable of structurally and accurately extracting phishing information (e.g., URLs and domains) from tweets about phishing by users who have actually discovered or encountered it. In our three months of live operation, CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports. We confirmed that 31,960 (90.2%) of these phishing URLs were later detected by the anti-virus engine, demonstrating that CrowdCanary is superior to existing systems in both accuracy and volume of threat extraction. We also analyzed users who shared phishing threats by utilizing the extracted phishing URLs and categorized them into two distinct groups - namely, experts and non-experts. As a result, we found that CrowdCanary could collect information that is specifically included in non-expert reports, such as information shared only by the company brand name in the tweet, information about phishing attacks that we find only in the image of the tweet, and information about the landing page before the redirect. Furthermore, we conducted a detailed analysis of the collected information on phishing sites and discovered that certain biases exist in the domain names and hosting servers of phishing sites, revealing new characteristics useful for unknown phishing site detection.

Efficient multiplexing of ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic, as well as ensuring the various reliability requirements of these traffic types in 5G wireless communications, is becoming increasingly important, particularly for vertical services. Interference management techniques, such as coordinated inter-cell scheduling, can enhance reliability in dense cell deployments. However, tight inter-cell coordination necessitates frequent information exchange between cells, which limits implementation. This paper introduces a novel RAN slicing framework based on centralized frequency-domain interference control per slice and link adaptation optimized for URLLC. The proposed framework does not require tight inter-cell coordination but can fulfill the requirements of both the decoding error probability and the delay violation probability of each packet flow. These controls are based on a power-law estimation of the lower tail distribution of a measured data set with a smaller number of discrete samples. As design guidelines, we derived a theoretical minimum radio resource size of a slice to guarantee the delay violation probability requirement. Simulation results demonstrate that the proposed RAN slicing framework can achieve the reliability targets of the URLLC slice while improving the spectrum efficiency of the eMBB slice in a well-balanced manner compared to other evaluated benchmarks.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel scheme where the information bits are conveyed through the subcarrier activation pattern (SAP) and the symbols on the active subcarriers. Specifically, the subcarriers are partitioned into many subblocks and the subcarriers in each subblock can have two states, active or idle. Unfortunately, OFDM-IM inherits the high peak-to-average power ratio (PAPR) problem from the classical OFDM. The OFDM-IM signal with high PAPR induces in-band distortion and out-of-band radiation when it passes through high power amplifier (HPA). Recently, there are attempts to reduce PAPR by exploiting the unique structure of OFDM-IM, which is adding dither signals in the idle subcarriers. The most recent work dealing with the dither signals is using dithers signals with various amplitude constraints according to the characteristic of the corresponding OFDM-IM subblock. This is reasonable because OFDM subblocks have distinct levels of robustness against noise. However, the amplitude constraint in the recent work is efficient for only additive white Gaussian noise (AWGN) channels and cannot be used for maximum likelihood (ML) detection. Therefore, in this paper, based on pairwise error probability (PEP) analysis, a specific constraint for the dither signals is derived over a Rayleigh fading channel.

As a hierarchical network framework, clustering aims to divide nodes with similar mobility characteristics into the same cluster to form a more structured hierarchical network, which can effectively solve the problem of high dynamics of the network topology caused by the high-speed movement of nodes in aeronautical ad hoc networks. Based on this goal, we propose a multi-hop distributed clustering algorithm based on link duration. The algorithm is based on the idea of multi-hop clustering, which ensures the coverage and stability of clustering. In the clustering phase, the link duration is used to accurately measure the degree of stability between nodes. At the same time, we also use the link duration threshold to filter out relatively stable links and use the gravity factor to let nodes set conditions for actively creating links based on neighbor distribution. When selecting the cluster head, we select the most stable node as the cluster head node based on the defined node stability weight. The node stability weight comprehensively considers the connectivity degree of nodes and the link duration between nodes. In order to verify the effectiveness of the proposed method, we compare them with the N-hop and K-means algorithms from four indicators: average cluster head duration, average cluster member duration, number of cluster head changes, and average number of intra-cluster link changes. Experiments show that the proposed method can effectively improve the stability of the topology.

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. Let C(t,e) denote the cyclic code with two nonzero αt and αe, where α is a generator of 𝔽*3m. In this letter, we investigate the ternary cyclic codes with parameters [3m - 1, 3m - 1 - 2m, 4] based on some results proposed by Ding and Helleseth in 2013. Two new classes of optimal ternary cyclic codes C(t,e) are presented by choosing the proper t and e and determining the solutions of certain equations over 𝔽3m.

Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

The auto-correlation property of Huffman sequence makes it a good candidate for its application in radar and communication systems. However, high peak-to-average power ratio (PAPR) of Huffman sequence severely limits its application value. In this paper, we propose a novel algorithm to construct Huffman sequences with low PAPR. We have used the roots of the polynomials corresponding to Huffman sequences of length M + 1 to construct Huffman sequences of length 2M + 1, with low PAPR.

Stochastic Computing (SC) allows additions and multiplications to be realized with lower power than the conventional binary operations if we admit some errors. However, for many complex functions which cannot be realized by only additions and multiplications, we do not know a generic efficient method to calculate a function by using an SC circuit; it is necessary to realize an SC circuit by using a generic method such as polynomial approximation methods for such a function, which may lose the advantage of SC. Thus, there have been many researches to consider efficient SC realization for specific functions; an efficient SC square root circuit with a feedback circuit was proposed by D. Wu et al. recently. This paper generalizes the SC square root circuit with a feedback circuit; we identify a situation when we can implement a function efficiently by an SC circuit with a feedback circuit. As examples of our generalization, we propose SC circuits to calculate the n-th root calculation and division. We also show our analysis on the accuracy of our SC circuits and the hardware costs; our results show the effectiveness of our method compared to the conventional SC designs; our framework may be able to implement a SC circuit that is better than the existing methods in terms of the hardware cost or the calculation error.

Federated learning is a distributed machine learning approach in which clients train models locally with their own data and upload them to a server so that their trained results are shared between them without uploading raw data to the server. There are some challenges in federated learning, such as communication size reduction and client heterogeneity. The former can mitigate the communication overheads, and the latter can allow the clients to choose proper models depending on their available compute resources. To address these challenges, in this paper, we utilize Neural ODE based models for federated learning. The proposed flexible federated learning approach can reduce the communication size while aggregating models with different iteration counts or depths. Our contribution is that we experimentally demonstrate that the proposed federated learning can aggregate models with different iteration counts or depths. It is compared with a different federated learning approach in terms of the accuracy. Furthermore, we show that our approach can reduce communication size by up to 89.4% compared with a baseline ResNet model using CIFAR-10 dataset.

The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

We explore the maximum total number of edge crossings and the maximum geometric thickness of the Euclidean minimum-weight (k, ℓ)-tight graph on a planar point set P. In this paper, we show that (10/7－ε)|P| and (11/6－ε)|P| are lower bounds for the maximum total number of edge crossings for any ε ＞ 0 in cases (k,ℓ)=(2,3) and (2,2), respectively. We also show that the lower bound for the maximum geometric thickness is 3 for both cases. In the proofs, we apply the method of arranging isomorphic units regularly. While the method is developed for the proof in case (k,ℓ)=(2,3), it also works for different ℓ.

Simple and quick tests at medical clinics have become increasingly important. Magnetic sensing techniques have been developed to detect biomarkers using magnetic nanoparticles in liquid-phase assays. We developed a biomarker assay that involves using an alternating current (AC) susceptibility measurement system that uses functional magnetic particles and magnetic sensing technology. We also developed compact biomarker measuring equipment to enable quick testing. Our assay is a one-step homogeneous assay that involves simply mixing a sample with a reagent, shortening testing time and simplifying processing. Using our compact measuring equipment, which includes anisotropic magneto resistance (AMR) sensors, we conducted high-sensitivity measurements of extremely small amounts of two biomarkers (C-reactive protein, CRP and α-Fetoprotein, AFP) used for diagnosing arteriosclerosis and malignant tumors. The results indicate that an extremely small amount of CRP and AFP could be detected within 15 min, which demonstrated the possibility of a simple and quick high-sensitivity immunoassay that involves using an AC-susceptibility measurement system.

A 0.13mJ/prediction with 68.6% accuracy wired-logic deep neural network (DNN) processor is developed in a single 16-nm field-programmable gate array (FPGA) chip. Compared with conventional von-Neumann architecture DNN processors, the energy efficiency is greatly improved by eliminating DRAM/BRAM access. A technical challenge for conventional wired-logic processors is the large amount of hardware resources required for implementing large-scale neural networks. To implement a large-scale convolutional neural network (CNN) into a single FPGA chip, two technologies are introduced: (1) a sparse neural network known as a non-linear neural network (NNN), and (2) a newly developed raster-scan wired-logic architecture. Furthermore, a novel high-level synthesis (HLS) technique for wired-logic processor is proposed. The proposed HLS technique enables the automatic generation of two key components: (1) Verilog-hardware description language (HDL) code for a raster-scan-based wired-logic processor and (2) test bench code for conducting equivalence checking. The automated process significantly mitigates the time and effort required for implementation and debugging. Compared with the state-of-the-art FPGA-based processor, 238 times better energy efficiency is achieved with only a slight decrease in accuracy on the CIFAR-100 task. In addition, 7 times better energy efficiency is achieved compared with the state-of-the-art network-optimized application-specific integrated circuit (ASIC).

Handover is an important property of cellular communication that enables the user to move from one cell to another without losing the connection. It is a very crucial process for the quality of the user’s experience because it may interrupt data transmission. Therefore, good handover management is very important in the current and future cellular systems. Several techniques have been employed to improve the handover performance, usually to increase the probability of a successful handover. One of the techniques is predictive handover which predicts the target cell using some methods other than the traditional measurement-based algorithm, including using machine learning. Several studies have been conducted in the implementation of predictive handover, most of them by modifying the internal algorithm of existing network elements, such as the base station. We implemented a predictive handover algorithm using an intelligent node outside the existing network elements to minimize the modification of the network and to create modularity in the system. Using a recently standardized Open Radio Access Network (O-RAN) Near Realtime Radio Intelligent Controller (Near-RT RIC), we created a modular application that can improve the handover performance by determining the target cell using machine learning techniques. In our previous research, we modified The Near-RT RIC original software that is using vector autoregression to determine the target cell by predicting the throughput of each neighboring cell. We also modified the method using a Multi-Layer Perceptron (MLP) neural network. In this paper, we redesigned the neural network using Long Short-Term Memory (LSTM) that can better handle time series data. We proved that our proposed LSTM-based machine learning algorithms used in Near-RT RIC can improve the handover performance compared to the traditional measurement-based algorithm.

Cooperative Communication is one of the most effective techniques to improve the desired signal quality of the typical user. This paper studies an indoor cellular network system that deploys the Reconfigurable Intelligent Surfaces (RIS) at the position of BSs to enable the cooperative features. To evaluate the network performance, the coverage probability expression of the typical user in the indoor wireless environment with presence of walls and effects of Rayleigh fading is derived. The analytical results shows that the RIS-assisted system outperforms the regular one in terms of coverage probability.

This letter studies the secrecy outage probability (SOP) and the secrecy diversity order of Alamouti STBC with decision feedback (DF) detection over the time-selective fading channels. For given temporal correlations, we have derived the exact SOPs and their asymptotic approximations for all possible combinations of detection schemes including joint maximum likehood (JML), zero-forcing (ZF), and DF at Bob and Eve. We reveal that the SOP is mainly influenced by the detection scheme of the legitimate receiver rather than eavesdropper and the achievable secrecy diversity order converges to two and one for JML only at Bob (i.e., JML-JML/ZF/DF) and for the other cases (i.e., ZF-JML/ZF/DF, DF-JML/ZF/DF), respectively. Here, p-q combination pair indicates that Bob and Eve adopt the detection method p ∈ {JML, ZF, DF} and q ∈ {JML, ZF, DF}, respectively.

Leukemia is a common and highly dangerous blood disease that requires early detection and treatment. Currently, the diagnosis of leukemia types mainly relies on the pathologist’s morphological examination of blood cell images, which is a tedious and time-consuming process, and the diagnosis results are highly subjective and prone to misdiagnosis and missed diagnosis. This research suggests a blood cell image recognition technique based on an enhanced Vision Transformer to address these problems. Firstly, this paper incorporate convolutions with token embedding to replace the positional encoding which represent coarse spatial information. Then based on the Transformer’s self-attention mechanism, this paper proposes a sparse attention module that can select identifying regions in the image, further enhancing the model’s fine-grained feature expression capability. Finally, this paper uses a contrastive loss function to further increase the intra-class consistency and inter-class difference of classification features. According to experimental results, The model in this study has an identification accuracy of 92.49% on the Munich single-cell morphological dataset, which is an improvement of 1.41% over the baseline. And comparing with sota Swin transformer, this method still get greater performance. So our method has the potential to provide reference for clinical diagnosis by physicians.

This paper discusses the resilience of networks based on graph theory and stochastic process. The electric power network where edges may fail simultaneously and the performance of the network is measured by the ratio of connected nodes is supposed for the target network. For the restoration, under the constraint that the resources are limited, the failed edges are repaired one by one, and the order of the repair for several failed edges is determined with the priority to the edge that the amount of increasing system performance is the largest after the completion of repair. Two types of resilience are discussed, one is resilience in the recovery stage according to the conventional definition of resilience and the other is steady state operational resilience considering the long-term operation in which the network state changes stochastically. The second represents a comprehensive capacity of resilience for a system and is analytically derived by Markov analysis. We assume that the large-scale disruption occurs due to the simultaneous failure of edges caused by the common cause failures in the analysis. Marshall-Olkin type shock model and α factor method are incorporated to model the common cause failures. Then two resilience measures, “operational resilience” and “operational resilience in recovery stage” are proposed. We also propose approximation methods to obtain these two operational resilience measures for complex networks.