Frontiers of Information Technology & Electronic Engineering最新文献

The synthetic minority oversampling technique (SMOTE) is a popular algorithm to reduce the impact of class imbalance in building classifiers, and has received several enhancements over the past 20 years. SMOTE and its variants synthesize a number of minority-class sample points in the original sample space to alleviate the adverse effects of class imbalance. This approach works well in many cases, but problems arise when synthetic sample points are generated in overlapping areas between different classes, which further complicates classifier training. To address this issue, this paper proposes a novel generalization-oriented rather than imputation-oriented minority-class sample point generation algorithm, named overlapping minimization SMOTE (OM-SMOTE). This algorithm is designed specifically for binary imbalanced classification problems. OM-SMOTE first maps the original sample points into a new sample space by balancing sample encoding and classifier generalization. Then, OM-SMOTE employs a set of sophisticated minority-class sample point imputation rules to generate synthetic sample points that are as far as possible from overlapping areas between classes. Extensive experiments have been conducted on 32 imbalanced datasets to validate the effectiveness of OM-SMOTE. Results show that using OM-SMOTE to generate synthetic minority-class sample points leads to better classifier training performances for the naive Bayes, support vector machine, decision tree, and logistic regression classifiers than the 11 state-of-the-art SMOTE-based imputation algorithms. This demonstrates that OM-SMOTE is a viable approach for supporting the training of high-quality classifiers for imbalanced classification. The implementation of OM-SMOTE is shared publicly on the GitHub platform at https://github.com/luxuan123123/OM-SMOTE/.

This paper concerns the event-triggered distributed cross-dimensional formation control problem of heterogeneous multi-agent systems (HMASs) subject to limited network resources. The central aim is to design an effective distributed formation control scheme that will achieve the desired formation control objectives even in the presence of restricted communication. Consequently, a multi-dimensional HMAS is first developed, where a group of agents are assigned to several subgroups based on their dimensions. Then, to mitigate the excessive consumption of communication resources, a cross-dimensional event-triggered communication mechanism is designed to reduce the information interaction among agents with different dimensions. Under the proposed event-based communication mechanism, the problem of HMAS cross-dimensional formation control is transformed into the asymptotic stability problem of a closed-loop error system. Furthermore, several stability criteria for designing a cross-dimensional formation control protocol and communication schedule are presented in an environment where there is no information interaction among follower agents. Finally, a simulation case study is provided to validate the effectiveness of the proposed formation control protocol.

Mid-wavelength infrared (MWIR) detection and long-wavelength infrared (LWIR) detection constitute the key technologies for space-based Earth observation and astronomical detection. The advanced ability of infrared (IR) detection technology to penetrate the atmosphere and identify the camouflaged targets makes it excellent for space-based remote sensing. Thus, such detectors play an essential role in detecting and tracking low-temperature and far-distance moving targets. However, due to the diverse scenarios in which space-based IR detection systems are built, the key parameters of IR technologies are subject to unique demands. We review the developments and features of MWIR and LWIR detectors with a particular focus on their applications in space-based detection. We conduct a comprehensive analysis of key performance indicators for IR detection systems, including the ground sampling distance (GSD), operation range, and noise equivalent temperature difference (NETD) among others, and their interconnections with IR detector parameters. Additionally, the influences of pixel distance, focal plane array size, and operation temperature of space-based IR remote sensing are evaluated. The development requirements and technical challenges of MWIR and LWIR detection systems are also identified to achieve high-quality space-based observation platforms.

Phone number recycling (PNR) refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner. It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms. Specifically, a new owner of a reassigned number can access the application account with which the number is associated, and may perform fraudulent activities. Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users. Thus, alternative solutions that depend on only the information of the applications are imperative. In this work, we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers. Our analysis on Meituan’s real-world dataset shows that compromised accounts have unique statistical features and temporal patterns. Based on the observations, we propose a novel model called temporal pattern and statistical feature fusion model (TSF) to tackle the problem, which integrates a temporal pattern encoder and a statistical feature encoder to capture behavioral evolutionary interaction and significant operation features. Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the baselines, demonstrating its effectiveness in detecting compromised accounts due to reassigned numbers.

Many areas are now experiencing data streams that contain privacy-sensitive information. Although the sharing and release of these data are of great commercial value, if these data are released directly, the private user information in the data will be disclosed. Therefore, how to continuously generate publishable histograms (meeting privacy protection requirements) based on sliding data stream windows has become a critical issue, especially when sending data to an untrusted third party. Existing histogram publication methods are unsatisfactory in terms of time and storage costs, because they must cache all elements in the current sliding window (SW). Our work addresses this drawback by designing an efficient online histogram publication (EOHP) method for local differential privacy data streams. Specifically, in the EOHP method, the data collector first crafts a histogram of the current SW using an approximate counting method. Second, the data collector reduces the privacy budget by using the optimized budget absorption mechanism and adds appropriate noise to the approximate histogram, making it possible to publish the histogram while retaining satisfactory data utility. Extensive experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage costs and improves data utility compared to other existing algorithms.

This article investigates the event-triggered adaptive neural network (NN) tracking control problem with deferred asymmetric time-varying (DATV) output constraints. To deal with the DATV output constraints, an asymmetric time-varying barrier Lyapunov function (ATBLF) is first built to make the stability analysis and the controller construction simpler. Second, an event-triggered adaptive NN tracking controller is constructed by incorporating an error-shifting function, which ensures that the tracking error converges to an arbitrarily small neighborhood of the origin within a predetermined settling time, consequently optimizing the utilization of network resources. It is theoretically proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), while the initial value is outside the constraint boundary. Finally, a single-link robotic arm (SLRA) application example is employed to verify the viability of the acquired control algorithm.

A low-profile dual-broadband dual-circularly-polarized (dual-CP) reflectarray (RA) is proposed and demonstrated, supporting independent beamforming for right-/left-handed CP waves at both K-band and Ka-band. Such functionality is achieved by incorporating multi-layered phase shifting elements individually operating in the K- and Ka-band, which are then interleaved in a shared aperture, resulting in a cell thickness of only about 0.1λ_L. By rotating the designed K- and Ka-band elements around their own geometrical centers, the dual-CP waves in each band can be modulated separately. To reduce the overall profile, planar K-/Ka-band dual-CP feeds with a broad band are designed based on the magnetoelectric dipoles and multi-branch hybrid couplers. The planar feeds achieve bandwidths of about 32% and 26% at K- and Ka-band respectively with reflection magnitudes below −13 dB, an axial ratio smaller than 2 dB, and a gain variation of less than 1 dB. A proof-of-concept dual-band dual-CP RA integrated with the planar feeds is fabricated and characterized which is capable of generating asymmetrically distributed dual-band dual-CP beams. The measured peak gain values of the beams are around 24.3 and 27.3 dBic, with joint gain variation <1 dB and axial ratio <2 dB bandwidths wider than 20.6% and 14.6% at the lower and higher bands, respectively. The demonstrated dual-broadband dual-CP RA with four degrees of freedom of beamforming could be a promising candidate for space and satellite communications.

We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.

Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatial domain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysis and detection process, and can only be applied to 3D mesh objects, so there is a lack of steganalysis algorithms for 3D point cloud objects. To change the fact that steganalysis is limited to 3D mesh and eliminate the redundant features in the 3D mesh steganalysis feature set, we propose a 3D point cloud steganalysis algorithm based on composite operator feature enhancement. First, the 3D point cloud is normalized and smoothed. Second, the feature points that may contain secret information in 3D point clouds and their neighboring points are extracted as the feature enhancement region by the improved 3DHarris-ISS composite operator. Feature enhancement is performed in the feature enhancement region to form a feature-enhanced 3D point cloud, which highlights the feature points while suppressing the interference created by the rest of the vertices. Third, the existing 3D mesh feature set is screened to reduce the data redundancy of more relevant features, and the newly proposed local neighborhood feature set is added to the screened feature set to form the 3D point cloud steganography feature set POINT72. Finally, the steganographic features are extracted from the enhanced 3D point cloud using the POINT72 feature set, and steganalysis experiments are carried out. Experimental analysis shows that the algorithm can accurately analyze the 3D point cloud’s spatial steganography and determine whether the 3D point cloud contains hidden information, so the accuracy of 3D point cloud steganalysis, under the prerequisite of missing edge and face information, is close to that of the existing 3D mesh steganalysis algorithms.

As the number of cores in a multicore system increases, the communication pressure on the interconnection network also increases. The network-on-chip (NoC) architecture is expected to take on the ever-expanding communication demands triggered by the ever-increasing number of cores. The communication behavior of the NoC architecture exhibits significant spatial-temporal variation, posing a considerable challenge for NoC reconfiguration. In this paper, we propose a traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing to adapt to the varying traffic flows with a high flexibility. First, a modified input port is introduced to support buffer sharing between adjacent ports. Specifically, the modified input port can be dynamically reconfigured to react to on-demand traffic. Second, it is ascertained that a centralized output-oriented buffer management works well with the reconfigurable input ports. Finally, this reconfiguration method can be implemented with a low overhead hardware design without imposing a great burden on the system implementation. The experimental results show that compared to other proposals, the proposed NoC architecture can greatly reduce the packet latency and improve the saturation throughput, without incurring significant area and power overhead.