The phenomena of iced line galloping in overhead transmission lines, caused by wind or asymmetric icing, can directly result in structural damage, windage yaw discharge of conductor, and metal damage, posing significant risks to the operation of power systems. However, the existing prediction methods for iced line galloping are difficult to achieve accurate predictions due to the lack of a large amount of iced line galloping data that matches real-world conditions. To address these issues, this paper studies the overhead iced transmission line galloping response prediction. First, the models of finite element, aerodynamic coefficient, and aerodynamic excitation for the iced conductor are constructed. The dynamic response of the conductor is simulated using finite element software to obtain a dataset of conductor galloping under different parameters. Secondly, a particle swarm optimization-conditional generative adversarial network (PSO-CGAN) based iced transmission line galloping prediction model is proposed, where the weight parameters of loss function in CGAN are optimized by PSO. The model takes initial wind attack angle, wind speed, and span as inputs to output prediction results of iced transmission line galloping. Then, based on the dynamics and galloping features of the conductor, the effects of different initial wind attack angles, wind speeds, and icing thickness on galloping are analyzed. Finally, the superior performance of the proposed model is verified through simulations.

Visible-infrared person re-identification (VI-ReID) aims to achieve cross-modality matching between the visible and infrared modalities, thus enabling usage in all-day monitoring scenarios. Existing VI-ReID methods have indeed achieved promising performance by considering the global information for identity-related discriminative learning. However, they often overlook the importance of local information, which can contribute significantly to learning identity-specific discriminative cues. Moreover, the substantial modality gap typically poses challenges during the model training process. In response to the aforementioned issues, we propose a VI-ReID method called partial enhancement and channel aggregation (PECA) and make efforts in the following three aspects. Firstly, to capture local information, we introduce the global-local similarity learning (GSL) module, which compels the encoder to focus on fine-grained details by increasing the similarity between global and local features within various feature spaces. Secondly, to address the modality gap, we propose an inter-modality channel aggregation learning (ICAL) approach, which progressively guides the learning of modality-invariant features. ICAL not only progressively alleviates modality gap but also augments the training data. Additionally, we introduce a novel instance-modality contrastive loss, which facilitates the learning of modality-invariant and identity-related features at both the instance and modality levels. Extensive experiments on the SYSU-MM01 and RegDB datasets have shown that PECA outperforms state-of-the-art methods.

The bicube is derived from the hypercube, and it provides a topology for interconnection networks of parallel systems. The bicube can interconnect the same number of nodes with the same degree as the hypercube while its diameter is almost half of that of the hypercube. In addition, the bicube preserves the property of node symmetry. Hence, the bicube attracts much attention. In this paper, we focus on the bicube with faulty nodes and propose three fault-tolerant routing methods to find a fault-free path between any pair of non-faulty nodes in it.

Big data processing is a set of techniques or programming models, which can be deployed on both the cloud servers or edge nodes, to access large-scale data and extract useful information for supporting and providing decisions. Meanwhile, several typical domains of human activity in smart society, such as social networks, medical diagnosis, recommendation systems, transportation, and Internet of Things (IoT), often manage a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. As one of the convincing solutions to carry out analytics for big data, graph processing is especially applicable for these application domains. However, either the intra-device or the inter-device data processing in the edge-cloud architecture is truly prone to be attacked by the malicious Trojans covertly embedded in the counterfeit processing systems developed by some third-party vendors in numerous practical scenarios, leading to identity theft, misjudgment, privacy disclosure, and so on. In this paper, for the first time to our knowledge, we specially build a novel attack model for ubiquitous graph processing in detail, which also has easy scalability for other applications in big data processing, and discuss some common existing mitigations accordingly. Multiple activation mechanisms of Trojans designed in our attack model effectively make the attacks imperceptible to users. Evaluations indicate that the proposed Trojans are highly competitive in stealthiness with trivial extra latency.

Federated Learning (FL) facilitates deep learning model training across distributed networks while ensuring data privacy. When deployed on edge devices, network pruning becomes essential due to the constraints of computational resources. However, traditional FL pruning methods face bias issues arising from the varied distribution of local data, which poses a significant challenge. To address this, we propose DDPruneFL, an innovative FL pruning framework that utilizes Discriminative Data (DD). Specifically, we utilize minimally pre-trained local models, allowing each client to extract semantic concepts as DD, which then inform an iterative pruning process. As a result, DDPruneFL significantly outperforms existing methods on four benchmark datasets, adeptly handling both IID and non-IID distributions and Client Selection scenarios. This model achieves state-of-the-art (SOTA) performance in this field. Moreover, our studies comprehensively validate the effectiveness of DD. Furthermore, a detailed computational complexity analysis focused on Floating-point Operations (FLOPs) is also conducted. The FLOPs analysis reveals that DDPruneFL significantly improves performance during inference while only marginally increasing training costs. Additionally, it exhibits a cost advantage in inference when compared to other pruning FL methods of the same type, further emphasizing its cost-effectiveness and practicality.

Annealing machines use an Ising model to represent combinatorial optimization problems (COPs) and minimize the energy of the model with spin-flip sequences. Pseudo temperature is a key hyperparameter to control the search performance of annealing machines. In general, the temperature is statically scheduled such that it is gradually decreased from a sufficiently high to a sufficiently low values. However, the search process during high and low temperatures in solving constrained COPs does not improve the solution quality as expected, which requires repeated preliminary annealing for pre-tuning. This paper proposes a flip-count-based dynamic temperature control (FDTC) method to make the preliminary annealing unnecessary. FDTC checks whether the current temperature is effective by evaluating the average number of flipped spins in a series of steps. The simulation results for traveling salesman problems and quadratic assignment problems demonstrate that FDTC can obtain comparable or higher solution quality than the static temperature scheduling pre-tuned for every COP.

The performance improvement by CMOS circuit technology is reaching its limits. Many researchers have been studying computing technologies that use emerging devices to challenge such critical issues. Nanophotonic technology is a promising candidate for tackling the issue due to its ultra-low latency, high bandwidth, and low power characteristics. Although previous research develops hardware accelerators by exploiting nanophotonic circuits for AI inference applications, there has never been considered for the acceleration of training that requires complex Floating-Point (FP) operations. In particular, the design balance between optical and electrical circuits has a critical impact on the latency, energy, and accuracy of the arithmetic system, and thus requires careful consideration of the optimal design. In this study, we design three types of Opto-Electrical Floating-point Multipliers (OEFMs): accuracy-oriented (Ao-OEFM), latency-oriented (Lo-OEFM), and energy-oriented (Eo-OEFM). Based on our evaluation, we confirm that Ao-OEFM has high noise resistance, and Lo-OEFM and Eo-OEFM still have sufficient calculation accuracy. Compared to conventional electrical circuits, Lo-OEFM achieves an 87% reduction in latency, and Eo-OEFM reduces energy consumption by 42%.

This study discusses the behavior of resonant tunneling diode (RTD) oscillators when a transmission line (TL) stub is added. The TL stub acts as a delayed feedback unit, resulting in unstable and complex oscillation behavior. Circuit simulation showed that the circuits generate various waveforms, including chaos, by changing the stub length. Experimental demonstration of the simulation results was performed using circuits fabricated with hybrid integration techniques using an InGaAs/AlAs RTD. These complex signals have potential for various applications in the THz frequency range. On the other hand, this finding is significant for the design of THz oscillators using an RTD, since even a small metal pattern can cause such a feedback effect in the THz frequency range. In particular, interconnect wiring patterns can cause this effect because reflection due to impedance mismatch is unavoidable.

Due to the limited lifespan of Micro-Electro-Mechanical Systems (MEMS), their components need to be replaced regularly. For intelligent devices such as electronic noses, updating an intelligent gas sensor system requires establishing a new classifier model for the newly inserted gas sensor probes because of the poor consistency between the signals collected by the new and original systems. The traditional method involves retraining the new model by collecting adequate data of the gas sensor array under strict laboratory conditions, which is time-consuming and resource-intensive. To simplify and expedite this process, a federated learning method called FedGSSU is proposed for gas sensor system updating. Two datasets were used to verify the effectiveness of the proposed framework. The experimental results show that FedGSSU can effectively utilize the original classifier model to obtain a new classifier model while only replacing the gas sensor array. The consistency between the new gas sensor system and the original one reaches up to 90.17%, and the test accuracy is increased by 4 percentage points compared to the traditional method. While replacing sensors with FedGSSU will reduce recognition accuracy slightly, it is more acceptable in scenarios where high accuracy is not required than re-calibrating sensors and re-training the classifier.

This study introduces a pattern-matching method to enhance the efficiency and accuracy of physical verification of cell libraries. The pattern-matching method swiftly compares layouts of all I/O units within a specific area, identifying significantly different I/O units. Utilizing random sampling or full permutation can improve the efficiency of verification of I/O cell libraries. All permutations within an 11-unit I/O unit library can produce 39,916,800 I/O units (11!), far exceeding the capacity of current IC layout software. However, the proposed algorithm generates the layout file within 1 second and significantly reduces the DRC verification time from infinite duration to 63 seconds executing 415 DRC rules. This approach effectively improves the potential to detect layer density errors in I/O libraries. While conventional processes detect layer density and DRC issues only when adjacent I/O cells are placed due to layout size and machine constraints, in this work, the proposed algorithm selectively generates multiple distinct combinations of I/O cells for verification, crucial for improving the accuracy of physical design.

This paper proposes two VLSI implementation approaches for periods estimation hardware of periodic signals. Digital signal processing is one of the important technologies, and to estimate periods of signals are widely used in many areas such as IoT, predictive maintenance, anomaly detection, health monitoring, and so on. This paper focuses on accumulation for real-time serial-to-parallel converter (ARS) which is a simple parameter estimation method for periodic signals. ARS is simple algorithm to estimate periods of periodic signals without complex instructions such as multiplier and division. However, this algorithm is implemented only on software, suitable hardware implementation methods are not clear. Therefore, this paper proposes two VLSI implementation methods called ARS-DFF and ARS-MEM. ARS-DFF is simple and fast implementation method, but hardware scale is large. ARS-MEM reduces hardware scale by introducing an SRAM macro cell. This paper also designs both approaches using SystemVerilog and evaluates VLSI implementation. According to our evaluation results, both proposed methods can reduce the power consumption to less than 1/1000 compared to the implementation on a microprocessor.

A dual-band decoupling strategy via artificial transmission line (TL) for closely spaced two-element multiple-input multiple-output (MIMO) antenna is proposed, which consists of two composite right-/left-handed TLs for dual-band phase shifting and a cross-shaped TL for susceptance elimination to counteract the real and imaginary part of the mutual coupling coefficient S21 at dual frequency bands, respectively. The decoupling principle and detailed design process of the dual-band decoupling scheme are presented. To validate the dual-band decoupling technique, a closely spaced dual-band MIMO antenna for 5G (sub-6G frequency band) utilization is designed, fabricated, and tested. The experimental results agree well with the simulation ones. A dual-band of 3.40 GHz-3.59 GHz and 4.79 GHz-4.99 GHz (S11&S22 < -10 dB, S12&S21 < -20 dB) has been achieved, and the mutual coupling coefficient S21 is significantly reduced 21 dB and 16.1 dB at 3.5 GHz and 4.9 GHz, respectively. In addition, the proposed dual-band decoupling scheme is antenna independent, and it is very suitable for other tightly coupled dual-band MIMO antennas.

This paper presents a scatterer information estimation method for both E- and H-polarizations based on a time-domain saddle-point technique (TD-SPT). The method utilizes numerical data of the response waveforms of the reflected geometric optical ray (RGO) series, which constitute the backward transient scattering field components when a line source and an observation point are at the same location. A scatterer selected in the paper is a two-dimensional (2-D) coated cylinder. The three types of scatterer information are the relative permittivity of a coating medium layer and its thickness, and the outer radius of a coated cylinder. Specifically, the scatterer information estimation formulas are derived by applying the TD-SPT represented in RGO series to the amplitude intensity ratios (AIRs) of adjacent RGO components. By focusing on the analytical results that the AIRs are independent of polarization, we analytically clarify that all the estimation formulas derived here denote polarization independence. The estimates are obtained by substituting numerical data of the peaks of the response waveforms of the RGO components and their arrival times, as well as numerical parameters of a pulse source, into the estimation formulas and performing iterative calculations. We derive approximations to the estimation errors that are useful in quantitatively evaluating the errors of the estimates. The effectiveness of the scatterer information estimation method is substantiated by comparing the estimates with the set values. The polarization independence of the estimation formulas is validated numerically by contrasting the estimates for E- and H-polarizations. The estimation errors are discussed using the approximations to the errors of the estimates when a line source and an observation point are at the same location. Thereafter, the discrepancies that arise between the estimation errors when a line source and an observation point are at different locations are discussed. The methods to control the estimation accuracy and the computational time are also discussed.

The combination of peak-to-average power ratio (PAPR) reduction and predistortion (PD) techniques effectively reduces the nonlinear distortion of a transmission signal caused by power amplification and improves power efficiency. In this paper, assuming downlink amplify-and-forward (AF)-type relaying of multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) signals, we propose a joint method that combines a PD technique with our previously reported PAPR reduction method utilizing the null space of a MIMO channel. In the proposed method, the reported PAPR reduction method reduces the PAPR at a relay station (RS) as well as that at a base station (BS) by using only signal processing at the BS. The PD process at the BS and RS further reduces the nonlinear distortion caused by nonlinear power amplification. Computer simulation results show that the proposed method enhances the effectiveness of PD at the BS and RS and achieves further coverage enhancement compared to conventional methods.

Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.

In cloud computing environments with virtual machines (VMs), we propose a VM placement (VMP) method based on traffic estimation to balance loads due to traffic volumes within physical hosts (PHs) and passing through physical network interface cards (NICs). We refer to a VM or a NIC in a cloud environment as node, and define a flow as a pair of nodes. To balance loads for both PHs and NICs, it is necessary to measure flow traffic volumes because each VM may connect to other VMs in different PHs. However, this is not a cost-effective way to measure flow traffic volumes because the number of flows increases with O(N2) for the number N of nodes. To solve this problem, we propose a VMP method using a compressed sensing (CS)-based traffic estimator. In the proposed method, the relationship between flow traffic volumes and node traffic volumes is formulated by a system of underdetermined linear equations. The flow traffic volumes are estimated with CS from the measured node traffic volumes. From the estimated flow traffic volumes, each VM is assigned to the optimal host for load balancing by solving a mixed-integer optimization problem.

Four-wave mixing (FWM) is a crucial impairment factor in optical wavelength-division-multiplexing (WDM) transmission systems over dispersion-shifted fibers. This paper presents an FWM suppression scheme that places dispersive elements (DEs) such as dispersion compensation fibers at optically repeating points in transmission lines. In a DE, the relative phase of the transmitted signal lights and the FWM light generated in the previous spans is shifted. Consequently, the FWM lights generated in each span are summed in random phases and the total FWM power at the end of the transmission lines is reduced from that in straight transmission lines with no DEs. We conduct proof-of-principle experiments to confirm the mechanism of the FWM reduction. Calculation for evaluating the FWM reduction ratio in a WDM transmission system is also presented.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) effectively provides a high spectral efficiency and coding gain for digital coherent systems over additive white Gaussian noise (AWGN) and optical fiber channels. We previously proposed combining probabilistic amplitude shaping (PAS) with BICM-ID to further improve the system performance. However, the BICM-ID performance depends on the binary labeling scheme used for the constellation points. In this study, we evaluated the effect of binary labeling schemes on the performance of the PAS with BICM-ID system. Numerical simulations showed that the PAS with BICM-ID system employing a suitable binary labeling scheme offers a significant coding gain over both the AWGN and optical fiber channels. The system is also robust against performance degradation caused by the optical Kerr effect in the optical fiber channel. We used an extrinsic information transfer (EXIT) chart to analyze the suitability of binary labeling schemes and the effect of bit interleavers. The results showed that a binary labeling scheme is suitable if the slope of the demodulator’s EXIT curve is close to the slope of the decoder’s EXIT curve. The EXIT chart analysis also showed that inserting bit interleavers mitigates the performance degradation during iterative decoding. In addition, we used bitwise mutual information to evaluate the SNR penalty due to shaping gap and coding gap, and coding gain offered by iterative decoding of BICM-ID.

Federated cloud, as a promising technology, can improve the computing capacity for autonomous driving in the vehicle-road-cloud collaborative system. However, the allocation of federated clouds should consider the environmental changes based on the real-time impact of vehicle terminal location. To improve computational efficiency while ensuring the effectiveness of federated clouds, this paper proposes a one-sided matching reverse auction based on the federated clouds (OSFC) method for scheduling autonomous driving sensors in a vehicle-road-cloud collaborative environment. This method dynamically allocates communication resources according to the actual situation of the vehicle terminals in real time. Numerical simulations show that our proposed OSFC method significantly improves computational efficiency while ensuring the effectiveness of federated clouds compared with state-of-the-art work.

This paper proposes overloaded MIMO spatial multiplexing that can increase the number of spatially multiplexed signal streams despite of the number of antennas on a terminal and that on a receiver. We propose extension of the channel matrix for the spatial multiplexing to achieve the superb multiplexing performance. Precoding based on the extended channel matrix plays a crucial role in carrying out such spatial multiplexing. We consider three types of QR-decomposition techniques for the proposed spatial multiplexing to improve the transmission performance. The transmission performance of the proposed spatial multiplexing is evaluated by computer simulation. The simulation reveals that the proposed overloaded MIMO spatial multiplexing can implement 6 stream-spatial multiplexing in a 2×2 MIMO system, i.e., the overloading ratio of 3.0. The superior transmission performance is achieved by the proposed overloaded MIMO spatial multiplexing with one of the QR-decomposition techniques.