This paper introduces a novel approach for 3D Electromagnetic Tomography (EMT), designing a 3D EMT measurement system for volumetric conductivity reconstruction and defect detection, enabled by an innovative hybrid that combines a sensitivity-based algorithm with a Transformer–UNet model. Traditional non-iterative algorithms such as linear back projection (LBP), Tikhonov Regularization (TR), and Singular Value Decomposition (SVD) face limitations such as reduced accuracy in complex scenarios and susceptibility to noise. To overcome these issues, we propose a TR algorithm enhanced by bi-Laplacian regularization, significantly improving reconstruction quality. Extensive simulations and experimental validations demonstrate that the proposed method outperforms conventional algorithms, achieving higher quantitative gains. Furthermore, integrating deep learning techniques, specifically the proposed method, enables precise reconstruction by effectively combining raw electromagnetic signals with the prior reconstructed results based on the proposed reconstructed algorithm, thereby significantly improving robustness, accuracy, and detail preservation. Experimental verification confirms the practical efficacy and robustness of this hybrid model for industrial applications. This framework differs from prior purely data-driven reconstructions by explicitly coupling a sensitivity-based EMT solver with a Transformer–UNet trained with a supervised image-domain loss, where a physics-based pre-reconstruction is provided as an additional prior, enabling real-time monitoring with offline shape-accurate refinement. • A 3D EMT framework is proposed for conductivity and contour reconstruction. • A triple-regularized scheme improves volumetric imaging and defect depiction. • A dual-branch network enhances reconstruction accuracy and robustness.

Pulsed eddy current (PEC) has been widely employed in the metal industry for estimating the electromagnetic parameter and thickness of steel plates, owing to its unique advantages, such as rapid response, strong penetration ability, and richer information, in time domain and frequency domain. The slope of the last phase signal on a logarithmic scale is a crucial indicator in PEC testing. A simplified analytical expression for estimating the slope of the last phase signal is presented for the first time in this article. The slope can be computed based on the thickness, electrical conductivity, and magnetic permeability of the tested specimen. The simultaneous nonlinear mapping from measured signals to the thickness, electrical conductivity, and magnetic permeability of the tested specimen is constructed by the deep learning model based on the Transformer network, which is physics-informed through the deriveD simplified analytical expression, incorporating the last phase signal slope on a logarithmic scale to enhance test accuracy and speed. Numerical simulations and experiments were conducted to assess the proposed method for estimating electromagnetic parameters and thickness across different plate materials. The method provides real-time, accurate parameters estimation of the steel plate, and all estimated parameters have a relative error below 3%.

We study the (1:s+1) success rule for controlling the population size of the (1,λ)-EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large s if the fitness landscape is too easy. They conjectured that this problem is worst for the OneMax benchmark, since in some well-established sense OneMax is known to be the easiest fitness landscape. In this paper, we disprove this conjecture. We show that there exist s and ɛ such that the self-adjusting (1,λ)-EA with the (1:s+1)-rule optimizes OneMax efficiently when started with ɛn zero-bits, but does not find the optimum in polynomial time on Dynamic BinVal. Hence, we show that there are landscapes where the problem of the (1:s+1)-rule for controlling the population size of the (1,λ)-EA is more severe than for OneMax. The key insight is that, while OneMax is the easiest function for decreasing the distance to the optimum, it is not the easiest fitness landscape with respect to finding fitness-improving steps.

We study the (1,λ)-EA with mutation rate c/n for c≤1, where the population size is adaptively controlled with the (1:s+1)-success rule. Recently, Hevia Fajardo and Sudholt have shown that this setup with c=1 is efficient on OneMax for s<1, but inefficient if s≥18. Surprisingly, the hardest part is not close to the optimum, but rather at linear distance. We show that this behavior is not specific to OneMax. If s is small, then the algorithm is efficient on all monotone functions, and if s is large, then it needs super-polynomial time on all monotone functions. In the former case, for c<1 we show a O(n) upper bound for the number of generations and O(nlog⁡n) for the number of function evaluations, and for c=1 we show O(nlog⁡n) generations and O(n2log⁡log⁡n) evaluations. We also show formally that optimization is always fast, regardless of s, if the algorithm starts in proximity of the optimum. All results also hold in a dynamic environment where the fitness function changes in each generation. An extended abstract, containing only the results without proofs, has been published at the PPSN conference [1].

In this work, we thoroughly analyze the rate-region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users' channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate. Furthermore, we provide practical transceiver designs to approach the capacity-region. The memory induced by asynchronous transmission enables the use of the trellis-based detection methods which improves the performance. In addition, we propose a transceiver design, based on channel diagonalization to exploit the frequency-selectivity introduced by timing offsets. The proposed transceiver design, joint with the turbo principle, enables us to achieve a rate pair that is not achievable by the synchronous transmission.

In this paper, we analyze the benefits of intentionally adding timing mismatch in the downlink transmit beamforming for wireless transmission. Transmit beamforming enables the so-called space-division multiple access (SDMA), where multiple spatially separated users are served simultaneously. The optimal beamforming vectors can be found to minimize the average transmit power under each user's Quality-of-Service (QoS) constraint. We show that intentionally adding timing offsets between the transmitted signals can significantly reduce the average transmission power compared with the conventional optimal beamforming method while providing the same QoSs for users. The frequency-selectivity in communication channels provides the opportunity to exploit intelligent design for performance improvement. The frequency-selectivity is limited in environments with line-of-sight links or little scattering. In such environments, we propose the use of intentional time delays to induce frequency-selectivity that can be exploited. We provide three different methods exploiting the artificially induced frequency-selectivity which improve the performance with a computational complexity similar to that of the optimal synchronous beamforming. We derive the expressions for the achievable rates using the proposed methods and then provide efficient algorithms to solve the minimum power optimization. We show analytically and numerically that our proposed methods can provide the same QoS while serving more users, utilizing a fewer number of transmit antennas and using reduced power compared with the conventional beamforming methods.

Recent studies have demonstrated the superiority of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in cooperative communication networks. In this paper, we propose a novel half-duplex cooperative asynchronous NOMA (C-ANOMA) framework with user relaying, where a timing mismatch is intentionally added in the broadcast signal. We derive the expressions for the throughputs of the strong user (acts as relay) which employs the block-wise successive interference cancellation (SIC) and the weak user which combines the symbol-asynchronous signal with the interference-free signal. We analytically prove that in the C-ANOMA systems with a sufficiently large block length, the strong user attains the same throughput to decode its own message while both users can achieve a higher throughput to decode the weak user's message compared with those in the cooperative NOMA (C-NOMA) systems. Besides, we obtain the optimal timing mismatch when the block length goes to infinity. Furthermore, to exploit the trade-off between the power consumption of the base station and that of the relay user, we solve a weighted sum power minimization problem under quality of services (QoS) constraints. Numerical results show that the C-ANOMA system can consume less power compared with the C-NOMA system to satisfy the same QoS requirements.

Pseudo-Boolean monotone functions are unimodal functions which are trivial to optimize for some hillclimbers, but are challenging for a surprising number of evolutionary algorithms. A general trend is that evolutionary algorithms are efficient if parameters like the mutation rate are set conservatively, but may need exponential time otherwise. In particular, it was known that the (1 + 1)-EA and the (1 + λ)-EA can optimize every monotone function in pseudolinear time if the mutation rate is c/n for some c < 1, but that they need exponential time for some monotone functions for c > 2.2. The second part of the statement was also known for the (µ + 1)-EA.

In this work, we investigate the characteristics of spectrally efficient frequency division multiplexing (SEFDM). We prove that as the number of sub-carriers goes to infinity, the system model becomes rank-deficient and the number of zero eigenvalues is proportional to the frequency compression factor. We propose to transmit the superimposed symbols through the non-zero eigenvalues using proper power allocation. At the receiver side, we apply the block-wise zero forcing with successive interference cancellation (ZF-SIC) detection method and an approximation of the symbol error rate performance is provided. We compare the performance of the proposed method with that of the orthogonal frequency division multiplexing (OFDM) method in additive white Gaussian noise (AWGN) and frequency selective channels.

Recent studies have demonstrated that asynchronous non- orthogonal multiple access (ANOMA) outperforms conventional (synchronous) NOMA under the condition of perfect channel state information (CSI). In this paper, we investigate a downlink ANOMA system with imperfect CSI. It is analytically proved that the ANOMA system with a relatively large frame length outperforms the NOMA system in terms of the outage probability. To this end, we derive the analytical expressions for the individual throughput of each user and simplify them in the asymptotic case of infinite frame length. Besides, we show that with channel estimation error, the optimal timing mismatch converges to half of a single symbol length as the frame length goes to infinity.

In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.

Recent studies have numerically demonstrated the possible advantages of the asynchronous non-orthogonal multiple access (ANOMA) over the conventional synchronous non-orthogonal multiple access (NOMA). The ANOMA makes use of the oversampling technique by intentionally introducing a timing mismatch between symbols of different users. Focusing on a two-user uplink system, for the first time, we analytically prove that the ANOMA with a sufficiently large frame length can always outperform the NOMA in terms of the sum throughput. To this end, we derive the expression for the sum throughput of the ANOMA as a function of signal-to-noise ratio, frame length, and normalized timing mismatch. Based on the derived expression, we find that users should transmit at full powers to maximize the sum throughput. In addition, we obtain the optimal timing mismatch as the frame length goes to infinity. Moreover, we comprehensively study the impact of timing error on the ANOMA throughput performance. Two types of timing error, i.e., the synchronization timing error and the coordination timing error, are considered. We derive the throughput loss incurred by both types of timing error and find that the synchronization timing error has a greater impact on the throughput performance compared with the coordination timing error.

Recent studies have shown that asynchronous non- orthogonal multiple access (ANOMA) outperforms conventional synchronous non-orthogonal multiple access (NOMA) by taking advantage of artificial timing mismatch with oversampling. For the first time, we comprehensively study the impact of timing errors on the performance of uplink ANOMA systems in this paper. We consider two types of timing errors, which are the synchronization timing error and the coordination timing error. We analyze how the timing errors affect ANOMA systems, and derive the throughput loss of ANOMA systems incurred by both the synchronization timing error and the coordination timing error. An interesting finding is that the synchronization timing error has a larger impact on the throughput performance of ANOMA systems compared with the coordination timing error.

We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes, where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario, where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmit antennas, a short-term power constraint P , and target data rate p. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1bits per channel state and via training R2transmit antennas on average, where R1and R2are independent oft, and depend only on p and P . In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme.

Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit the operations of a database. However, debugging transactions is hard for several reasons: 1) after the execution of a transaction, its input is no longer available for debugging, 2) internal states of a transaction are typically not accessible, and 3) the execution of a transaction may be affected by concurrently running transactions. We present a debugger for transactions that enables non-invasive, postmortem debugging of transactions with provenance tracking and supports what-if scenarios (changes to transaction code or data). Using reenactment , a declarative replay technique we have developed, a transaction is replayed over the state of the DB seen by its original execution including all its interactions with concurrently executed transactions from the history. Importantly, our approach uses the temporal database and audit logging capabilities available in many DBMS and does not require any modifications to the underlying database system nor transactional workload.

Pilot contamination has been regarded as the bottleneck in time division duplexing (TDD) multi- cell massive multiple-input multiple-output (MIMO) systems. The pilot contamination problem cannot be addressed with large-scale antenna arrays. We provide a novel asynchronous channel training scheme to reduce the impact of pilot contamination without the cooperation of base stations. The scheme takes advantage of sampling diversity by inducing intentional timing mismatch. Then, the optimal linear minimum mean square error (LMMSE) estimator is designed to minimize the channel matrix estimation error. Finally, simulation results demonstrate that our scheme can provide significant performance improvement compared with the conventional synchronous systems that suffer from pilot contamination.

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.