Medical imaging plays an indispensable role in precise patient diagnosis. The integration of deep learning into medical diagnostics is becoming increasingly common. However, existing deep learning models face performance and efficiency challenges, especially in resource-constrained scenarios. To overcome these challenges, we introduce a novel dendritic neural efficientnet model called DEN, inspired by the function of brain neurons, which efficiently extracts image features and enhances image classification performance. Assessments on a diabetic retinopathy fundus image dataset reveal DEN’s superior performance compared to EfficientNet and other classical neural network models.

We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.

Detecting key frames in videos has garnered substantial attention in recent years, it is a point-level task and has deep research value and application prospect in daily life. For instances, video surveillance system, video cover generation and highlight moment flashback all demands the technique of key frame detection. However, the task is beset by challenges such as the sparsity of key frame instances, imbalances between target frames and background frames, and the absence of post-processing method. In response to these problems, we introduce a novel and effective Temporal Interval Guided (TIG) framework to precisely localize specific frames. The framework is incorporated with a proposed Point-Level-Soft non-maximum suppression (PLS-NMS) post-processing algorithm which is suitable for point-level task, facilitated by the well-designed confidence score decay function. Furthermore, we propose a TIG-loss, exhibiting sensitivity to temporal interval from target frame, to optimize the two-stage framework. The proposed method can be broadly applied to key frame detection in video understanding, including action start detection and static video summarization. Extensive experimentation validates the efficacy of our approach on action start detection benchmark datasets: THUMOS’14 and Activitynet v1.3, and we have reached state-of-the-art performance. Competitive results are also demonstrated on SumMe and TVSum datasets for deep learning based static video summarization.

Automatic detection and recognition systems have numerous applications in smart city implementation. Despite the accuracy and widespread use of device-based and optical methods, several issues remain. These include device limitations, environmental limitations, and privacy concerns. The FMWC sensor can overcome these issues to detect and track moving people accurately in commercial environments. However, single-chip mmWave sensor solutions might struggle to recognize standing and sitting people due to the necessary static removal module. To address these issues, we propose a real-time indoor people detection and tracking fusion system using mmWave radar and cameras. The proposed fusion system approaches an overall detection accuracy of 93.8% with a median position error of 1.7 m in a commercial environment. Compared to our single-chip mmWave radar solution addressing an overall accuracy of 83.5% for walking people, it performs better in detecting individual stillness, which may feed the security needs in retail. This system visualizes customer information, including trajectories and the number of people. It helps commercial environments prevent crowds during the COVID-19 pandemic and analyze customer visiting patterns for efficient management and marketing. Powered by an IoT platform, the system can be deployed in the cloud for easy large-scale implementation.

Entity alignment (EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs (KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing embedding-based methods generate aligning entity representation by mining the relevance of triple elements, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA - Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above shortcomings from the perspective of ensemble triple representation considering triple specificity and diversity features of entity role. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic and type spaces, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, the role diversity of triple elements is modeled via triple-aware entity enhancement in TTEA for EA-oriented entity representation. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework makes comparative results.

Aiming at the problem that large-scale traffic data lack labels and take too long for feature extraction in network intrusion detection, an unsupervised intrusion detection method ACOPOD based on Adam asymmetric autoencoder and COPOD (Copula-Based Outlier Detection) algorithm is proposed. This method uses the Adam asymmetric autoencoder with a reduced structure to extract features from the network data and reduce the data dimension. Then, based on the Copula function, the joint probability distribution of all features is represented by the edge probability of each feature, and then the outliers are detected. Experiments on the published NSL-KDD dataset with six other traditional unsupervised anomaly detection methods show that ACOPOD achieves higher precision and has obvious advantages in running speed. Experiments on the real civil aviation air traffic management network dataset further prove that the method can effectively detect intrusion behavior in the real network environment, and the results are interpretable and helpful for attack source tracing.

In this paper, we propose two algorithms, B-N2N and B-N2S, that solve the node-to-node and node-to-set disjoint paths problems in the bicube, respectively. We prove their correctness and that the time complexities of the B-N2N and B-N2S algorithms are O(n2) and O(n2 log n), respectively, if they are applied in an n-dimensional bicube with n ≥ 5. Also, we prove that the maximum lengths of the paths generated by B-N2N and B-N2S are both n + 2. Furthermore, we have shown that the algorithms can be applied in the locally twisted cube, too, with the same performance.

Emergency Medical Services (EMS) play a crucial role in healthcare systems, managing pre-hospital or out-of-hospital emergencies from the onset of an emergency call to the patient’s arrival at a healthcare facility. The design of an efficient ambulance location model is pivotal in enhancing survival rates, controlling morbidity, and preventing disability. Key factors in the classical models typically include travel time, demand zones, and the number of stations. While urban EMS systems have received extensive examination due to their centralized populations, rural areas pose distinct challenges. These include lower population density and longer response distances, contributing to a higher fatality rate due to sparse population distribution, limited EMS stations, and extended travel times. To address these challenges, we introduce a novel mathematical model that aims to optimize coverage and equity. A distinctive feature of our model is the integration of equity within the objective function, coupled with a focus on practical response time that includes the period required for personal protective equipment procedures, ensuring the model’s applicability and realism in emergency response scenarios. We tackle the proposed problem using a tailored genetic algorithm and propose a greedy algorithm for solution construction. The implementation of our tailored Genetic Algorithm promises efficient and effective EMS solutions, potentially enhancing emergency care and health outcomes in rural communities.

This article describes the idea of utilizing Attested Execution Secure Processors (AESPs) that fit into building a secure Self-Sovereign Identity (SSI) system satisfying Sybil-resistance under permissionless blockchains. Today’s circumstances requiring people to be more online have encouraged us to address digital identity preserving privacy. There is a momentum of research addressing SSI, and many researchers approach blockchain technology as a foundation. SSI brings natural persons various benefits such as owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. The main idea in our proposal is to utilize AESPs for three reasons: first is the use of attested execution capability along with tamper-resistance, which is a strong assumption; second is powerfulness and flexibility, allowing various open-source programs to be executed within a secure enclave, and the third is that equipping hardware-assisted security in mobile devices has become a norm. Rafael Pass et al.’s formal abstraction of AESPs and the ideal functionality $\color{brown}{\mathcal{G}_\mathtt{att}}$ enable us to formulate how hardware-assisted security works for secure digital identity systems preserving privacy under permissionless blockchains mathematically. Our proposal of the AESP-based SSI architecture and system protocols, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$, demonstrates the advantages of building a proper SSI system that satisfies the Sybil-resistant requirement. The protocols may eliminate the online distributed committee assumed in other research, such as CanDID, because of assuming AESPs; thus, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$ allows not to rely on multi-party computation (MPC), bringing drastic flexibility and efficiency compared with the existing SSI systems.

Radiation-induced temporal errors become a significant issue for circuit reliability. We measured the pulse widths of radiation-induced single event transients (SETs) from pMOSFETs and nMOSFETs separately. Test results show that heavy-ion induced SET rates of nMOSFETs were twice as high as those of pMOSFETs and that neutron-induced SETs occurred only in nMOSFETs. It was confirmed that the SET distribution from inverter chains can be estimated using the SET distribution from pMOSFETs and nMOSFETs by considering the difference in load capacitance of the measurement circuits.

This paper introduces a computer-aided low-power design method for tapered buffers that address given load capacitances, output transition times, and source impedances. Cross-voltage-domain tapered buffers involving a low-voltage domain in the frontier stages and a high-voltage domain in the posterior stages are further discussed which breaks the trade-off between the energy dissipation and the driving capability in conventional designs. As an essential circuit block, a dedicated analytical model for the level-shifter is proposed. The energy-optimized tapered buffer design is verified for different source and load conditions in a 180-nm CMOS process. The single-VDD buffer model achieves an average inaccuracy of 8.65% on the transition loss compared with Spice simulation results. Cross-voltage tapered buffers can be optimized to further remarkably reduce the energy consumption. The study finds wide applications in energy-efficient switching-mode analog applications.

A reduced dark leakage current, without degrading the near-infrared responsivity, is reported for a vertical pin structure of Ge photodiodes (PDs) on n+-Si substrate, which usually shows a leakage current higher than PDs on p+-Si. The peripheral/surface leakage, the dominant leakage in PDs on n+-Si, is significantly suppressed by globally implanting P+ in the i-Si cap layer protecting the fragile surface of i-Ge epitaxial layer before locally implanting B+/BF2+ for the top p+ region of the pin junction. The P+ implantation compensates free holes unintentionally induced due to the Fermi level pinning at the surface/interface of Ge. By preventing the hole conduction from the periphery to the top p+ region under a negative/reverse bias, a reduction in the leakage current of PDs on n+-Si is realized.

In this research, we investigated the digital/analog-operation utilizing ferroelectric nondoped HfO2 (FeND-HfO2) as a blocking layer (BL) in the Hf-based metal/oxide/nitride/oxide/Si (MONOS) nonvolatile memory (NVM), so called FeNOS NVM. The Al/HfN0.5/HfN1.1/HfO2/p-Si(100) FeNOS diodes realized small equivalent oxide thickness (EOT) of 4.5 nm with the density of interface states (Dit) of 5.3 × 1010 eV-1cm-2 which were suitable for high-speed and low-voltage operation. The flat-band voltage (VFB) was well controlled as 80-100 mV with the input pulses of ±3 V/100 ms controlled by the partial polarization of FeND-HfO2 BL at each 2-bit state operated by the charge injection with the input pulses of +8 V/1-100 ms.

In this paper, a cluster-based three-dimensional (3D) non-stationary vehicle-to-vehicle (V2V) channel model with circular arc motions and antenna rotates is proposed. The channel model simulates the complex urban communication scenario where clusters move with arbitrary velocities and directions. A novel cluster evolution algorithm with time-array consistency is developed to capture the non-stationarity. For time evolution, the birth-and-death (BD) property of clusters including birth, death, and rebirth are taken into account. Additionally, a visibility region (VR) method is proposed for array evolution, which is verified to be applicable to circular motions. Based on the Taylor expansion formula, a detailed derivation of space-time correlation function (ST-CF) with circular arc motions is shown. Statistical properties including ST-CF, Doppler power spectrum density (PSD), quasi-stationary interval, instantaneous Doppler frequency, root mean square delay spread (RMS-DS), delay PSD, and angular PSD are derived and analyzed. According to the simulated results, the non-stationarity in time, space, delay, and angular domains is captured. The presented results show that motion modes including linear motions as well as circular motions, the dynamic property of the scattering environment, and the velocity of the vehicle all have significant impacts on the statistical properties.

Using a limited number of probes has always been a focus in interface-level network topology probing to discover complete network topologies. Stop-set-based network topology probing methods significantly reduce the number of probes sent but suffer from the side effect of incomplete topology information discovery. This study proposes an optimized probing method based on stop probabilities (SPs) that builds on existing stop-set-based network topology discovery methods to address the issue of incomplete topology information owing to multipath routing. The statistics of repeat nodes (RNs) and multipath routing on the Internet are analyzed and combined with the principles of stop-set-based probing methods, highlighting that stopping probing at the first RN compromises the completeness of topology discovery. To address this issue, SPs are introduced to adjust the stopping strategy upon encountering RNs during probing. A method is designed for generating SPs that achieves high completeness and low cost based on the distribution of the number of RNs. Simulation experiments demonstrate that the proposed stop-probability-based probing method almost completely discovers network nodes and links across different regions and times over a two-year period, while significantly reducing probing redundancy. In addition, the proposed approach balances and optimizes the trade-off between complete topology discovery and reduced probing costs compared with existing topology probing methods. Building on this, the factors influencing the probing cost of the proposed method and methods to further reduce the number of probes while ensuring completeness are analyzed. The proposed method yields universally applicable SPs in the current Internet environment.

A novel temporal convolution network-gated recurrent unit (NTCN-GRU) algorithm is proposed for the greatest of constant false alarm rate (GO-CFAR) frequency hopping (FH) prediction, integrating GRU and Bayesian optimization (BO). GRU efficiently captures the semantic associations among long FH sequences, and mitigates the phenomenon of gradient vanishing or explosion. BO improves extracting data features by optimizing hyperparameters besides. Simulations demonstrate that the proposed algorithm effectively reduces the loss in the training process, greatly improves the FH prediction effect, and outperforms the existing FH sequence prediction model. The model runtime is also reduced by three-quarters compared with others FH sequence prediction models.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a problem of bent functions.

Minutiae pattern extraction plays a crucial role in fingerprint registration and identification for electronic applications. However, the extraction accuracy is seriously compromised by the presence of contaminated ridge lines and complex background scenarios. General image processing-based methods, which rely on many prior hypotheses, fail to effectively handle minutiae extraction in complex scenarios. Previous works have shown that CNN-based methods can perform well in object detection tasks. However, the deep neural networks (DNNs)-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To address these challenges comprehensively, this paper presents a fully automated minutiae extraction method leveraging DNNs. Firstly, we create a fingerprint minutiae dataset using a semi-automated minutiae annotation algorithm. Subsequently, we propose a minutiae extraction model based on Residual Networks (Resnet) that enables end-to-end prediction of minutiae. Moreover, we introduce a novel non-maximal suppression (NMS) procedure, guided by the Generalized Intersection over Union (GIoU) metric, during the inference phase to effectively handle outliers. Experimental evaluations conducted on the NIST SD4 and FVC 2004 databases demonstrate the superiority of the proposed method over existing state-of-the-art minutiae extraction approaches.

Images with rich color information are an important source of information that people obtain from the objective world. Occasionally, it is difficult for people with red-green color vision deficiencies to obtain color information from color images. We propose a method of color correction for dichromats based on the physiological characteristics of dichromats, considering hue information. First, the hue loss of color pairs under normal color vision was defined, an objective function was constructed on its basis, and the resultant image was obtained by minimizing it. Finally, the effectiveness of the proposed method is verified through comparison tests. Red-green color vision deficient people fail to distinguish between partial red and green colors. When the red and green connecting lines are parallel to the a* axis of CIE L*a*b*, red and green perception defectives cannot distinguish the color pair, but can distinguish the color pair parallel to the b* axis. Therefore, when two colors are parallel to the a* axis, their color correction yields good results. When color correction is performed on a color, the hue loss between the two colors under normal color vision is supplemented with b* so that red-green color vision-deficient individuals can distinguish the color difference between the color pairs. The magnitude of the correction is greatest when the connecting lines of the color pairs are parallel to the a* axis, and no color correction is applied when the connecting lines are parallel to the b* axis. The objective evaluation results show that the method achieves a higher score, indicating that the proposed method can maintain the naturalness of the image while reducing confusing colors.