Deploying a data-driven model in a real environment is challenging due to the fact that the distribution of the input data is usually not the same as that of the training data. The distribution change can considerably degrade the reliability and robustness of the model output. In this study, we develop a chance constrained back-mapping approach to transform the features of input data with a varying distribution into the distribution of the training dataset, based on the Kantorovich transportation. Since the Kantorovich method cannot guarantee a truthful backmapping of samples with highly variable size and distribution, we introduce a constraint to restrict the transformation by ensuring a prospect certainty of high accuracy of the model output. In addition, to reflect the data variability, the upper bound of the constraint is considered as a random parameter whose distribution is estimated online using the rolling average method. Furthermore, chance-constrained optimization is used to satisfy this constraint in a probabilistic manner. Therefore, the proposed approach enssures a high certainty of high accuracy of the trained model when deployed in a variable environment. Both analytical and experimental verification of the approach are carried out. The results of synthetic and real datasets demonstrate a clear improvement in terms of accuracy and robustness compared with the existing methods.

Potential for subsynchronous interactions (SSI) involving inverter-based resources is often screened for using small-signal impedance scans. However, these impedance scans are often slow to compute and it is often desired to study a wide range of operating conditions. This paper presents modeling practices and a new scanning approach for performing impedance-based SSI screening studies that can reduce the required computation time by orders of magnitude with negligible effect on accuracy. Relevant principles are illustrated using a type3 wind turbine model, using two different reactive power control strategies that have significantly different SSI behavior. The discussion of modeling practices includes per-unitization, mechanical dynamics, timestep selection, PLL design, and negative-sequence behavior and covers both time-saving simplifications and pitfalls or unsuitable simplifications to avoid. The new scanning approach involves identifying sets of input frequencies that do not interfere with one another’s outputs and using crest factor minimization to develop multisine test signals.

Probabilistic Neural Contextualization introduces a groundbreaking approach that bridges probabilistic reasoning with neural architectures to redefine contextual knowledge retrieval in computational models. By integrating dynamic contextualization mechanisms, it achieves enhanced adaptability and precision in managing uncertainty across diverse linguistic domains. The novel architecture incorporates advanced probabilistic inference layers, ensuring that outputs are consistently aligned with the complex demands of varied contextual scenarios. Comprehensive evaluations demonstrated marked improvements in key performance metrics, including perplexity and F1-score, validating the model's robustness and scalability. Experimental results further revealed its computational efficiency, maintaining performance consistency even under significant data variability and noise. Through seamless integration with open-source frameworks, the proposed methodology demonstrates its practical viability for large-scale applications, addressing critical challenges in knowledge-driven processing tasks. The study highlights a transformative advancement in contextual modeling, paving the way for more reliable, efficient, and intelligent computational systems capable of operating in complex and dynamic environments.

5G and beyond provides connectivity for a variety of heterogeneous, often mission critical services, placing stringent performance requirements on these systems. Providing satisfactory Quality-of-Experience (QoE) for diverse, coexisting applications prompts the network operators to enforce application-aware, efficient resource allocation schemes that can increase user-satisfaction, system utilization, and decrease the operational costs. For these purposes, QoS Flows and network slicing have been identified as key enablers. Those concepts move away from economy of scale, towards a fine-grained slice and flow handling with customized resource control for each application, application type, or slice. Implications of this change on the scaling of network resources and how to optimally allocate the available resources have been largely ignored so far. Furthermore, while capacity has been recognized as a key resource, selecting the appropriate queue size, granularity of the resource allocation scheme, and their relations with the number of clients are often neglected in the process of resource dimensioning. To address these shortcomings, we perform an in-depth evaluation of the effects that impact factors have on the overall QoE and system utilization using the OMNeT++ simulator. We show the optimization potential for QoE and resource utilization, and further formulate guidelines for efficient and QoE-aware resource allocation.  

We propose Supervised Learning Preference Optimization (SLPO), an approach to aligning language models that relies solely on the classic supervised learning loss function, cross-entropy. Unlike Reinforcement Learning from Human Feedback (RLHF), SLPO directly adjusts probability mass for chosen, rejected, and other sequences relative to a reference model, eliminating the need for KL divergence regularization. SLPO also avoids the extensive reparameterization of model outputs into scores and the Bradley-Terry model required by Direct Preference Optimization (DPO). As a result, SLPO is a purely supervised learning approach to alignment, free of reinforcement learning concepts. We further demonstrate how to efficiently implement a targeted probability mass for the intractably large set of sequences that are neither explicitly chosen nor rejected.

This paper focuses on Ambimorphic prototype research in the field of information engineering. Firstly, it elaborates the research background, purpose and significance, and analyzes the concept of Ambimorphic prototype and related theoretical foundations. It then introduces in detail the application of public key-private key simulation in information security and communication systems, as well as the design and implementation of telephone semantic tools, including requirements analysis, architecture design and key technologies. Through experiments, performance tests and result analyses are conducted on both, including individual tests and comprehensive application tests. Finally, it summarizes the research results, limitations, and looks forward to future research directions, providing new ideas and methods for the development of information engineering.

Maintaining contextual coherence in extended text generation remains a significant challenge in computational linguistics. The introduction of the Stochastic Hierarchical Embedding (SHE) mechanism offers a novel approach to this issue, incorporating stochastic variability within hierarchical embeddings to enhance adaptability and coherence in language models. This study details the integration of SHE into a contemporary open-source Large Language Model (LLM), providing a comprehensive mathematical framework and outlining the modifications required for effective implementation. Experimental evaluations demonstrate that SHE significantly improves contextual coherence and prompt convergence, as evidenced by higher Contextual Coherence Scores (CCS) and Prompt Convergence Rates (PCR). Additionally, the SHE-augmented LLM exhibits enhanced generalization capabilities across diverse linguistic tasks and increased robustness to input noise. These findings suggest that SHE presents a promising advancement in the development of more coherent and adaptable LLMs.

由于太赫兹无线通信拥有广阔的未开发频谱，它正在成为未来 6G 网络的关键技术。使用模拟相控天线阵列对于缓解太赫兹信号的显著衰减特别有效，从而确保高质量的通信。通过采用精心设计的移相器，这些阵列可以实现波束成形，将信号增益集中在空间中以补偿信号损失。然而，在超宽带情况下，相控阵系统的性能会受到影响，因为移相器通常是为中心频率设计的，但应用于整个频带。当子载波频率与中心频率不同时，这将导致标准波束成形方法中出现明显的波束偏差和散焦。在本文中，我们提出了模拟波束成形方法来增强视距太赫兹超宽带相控发射机阵列系统的性能。利用空间啁啾特性，我们开发了移相器设计方案，以在整个频带上实现更均匀的等效信道增益，以适应各种应用场景。我们通过模拟结果证明了所提出方法的有效性和优势，与最先进的模拟波束形成方案相比，结果显示出显着的改进，包括提高吞吐量和降低误码率。

Integrating quantum mechanics into cybersecurity frameworks offers transformative opportunities for addressing sophisticated digital threats. The Quantum-Infused Multi-Dimensional Signal Convergence (QIMDSC) framework introduced a pioneering methodology that leverages quantum principles for detecting highly dynamic and obfuscated ransomware behaviors. By encoding behavioral patterns into enriched quantum states and analyzing signal convergence through multidimensional mathematical constructs, the framework achieved significant advances in detection accuracy and computational efficiency. Comprehensive experiments demonstrated its ability to minimize false positives while maintaining high recall rates across diverse ransomware families, including LockBit, Hive, and BlackCat. Comparative analyses demonstrated the framework's superiority over traditional machine learning-based and hybrid approaches, particularly in identifying zero-day ransomware variants. The introduction of probabilistic wavefunction modeling further refined the detection mechanism, enabling the isolation of subtle deviations within high-dimensional feature spaces. Real-time anomaly detection capabilities were validated through extensive simulations, where the framework consistently performed well under varying network and system loads. Its ability to adapt to encryption depth and respond effectively to large-scale ransomware activity emphasizes its robustness in dynamic environments. The scalability of the architecture and its reliance on modular design ensured applicability across critical infrastructure, enterprise networks, and resource-constrained systems. Performance evaluations revealed that the framework excelled under conditions of high file system activity and network variability, demonstrating exceptional resilience to operational noise. The innovative approach bridges theoretical quantum mechanics with practical cybersecurity applications, marking a paradigm shift in threat detection methodologies. Insights derived from this research highlight the potential of quantum-inspired techniques for addressing emerging challenges in modern cybersecurity landscapes.

The concept of resolution is fundamental to any sensing or measurement system, as it establishes a reference for the intrinsic detection limit of any acquisition process. Although this concept originally emerged in the context of DC measurements, it should be more clearly defined in a generic acquisition system where the signal is inherently stochastic. This paper revisits the sensing and measurement process through the lens of Information Theory, where resolution is considered in relation to the distributions of random variables. Additionally, it will introduce a new parameter, the "number of resolution levels", which is valuable because it encapsulates the amount of information conveyed by a measurement system. The results demonstrate how this framework can address key design questions, such as determining the optimal discretization for an A/D converter given input-referred noise from a transducer, and how to optimize the system from a resolution perspective. Theoretical results will be compared with numerical simulations of an analog noisy interface coupled with an A/D converter.

The deployment of IoT platforms for SmartCity applications is demanding solutions to assure security and integrity levels. In this context, physical unclonable functions (PUF) may overcome the security drawbacks of storing the security key in a non-volatile memory. The existence of SRAM in embedded systems has driven the implementation of PUF solutions using memory circuit as entropy sources. The use of a non-specific memory design requires the need of identify the adequate cells useful for PUF applications. This work proposes a new methodology to distinguish the adequate cells based on mismatch factor calculation. The mismatch analysis using physical and performance parameters shows the viability of the selection methodology and computes the robustness of the responses in terms of probability of error in the start-up value.

This paper analyzes the performance of Alamouti coded short-packet non-orthogonal multiple access (NOMA) systems with hybrid multicast and unicast transmission over Nakagami-m fading, where only the statistical channel state information is available at the transmitter, and the multicast and unicast signals are intended for all users and a particular user, respectively. Due to some practical limitations, channel estimation errors (CEE) caused by a linear minimum mean-square error estimator and residual hardware impairments (RHI) caused by imperfect hardware implementation are taken into account. We first derive the approximate closed-form expressions for the average block error rate (BLER) and the corresponding asymptotic expressions at all users. Using such expressions, we analyze the diversity performance including conventional diversity order and finite-SNR diversity order. After this, we quantify the relationship among the common blocklenth, power allocation, and pilot sequence length under users' reliability constraints. Finally, numerical and simulation results verify our theoretical analysis and show that i) CEE and RHI greatly affects the transmission blocklength, ii) RHI leads to the error floor phenomenon at high SNRs and the finite-SNR diversity order is the effective performance metric in the presence of RHI, iii) there exist the optimal power allocation coefficients and pilot sequence length to minimize the transmission blocklength in the considered hybrid multicast-unicast system, and iv) the NOMA scheme is superior to the OMA counterpart by achieving low-latency transmission.

The rapid evolution of software development practices, coupled with the increasing prominence of open-source software (OSS) projects, has positioned versioning systems as invaluable repositories of information for analysis. This paper delves into the factors that shape software development activities by examining documented methodologies and tools used to mine and analyze OSS repositories. Through a detailed review of approaches addressing developer contributions, code modifications, and collaborative workflows, we identify key influences on productivity, code quality, and team dynamics. Our exploration includes an analysis of analytics techniques and tools, such as CVSSearch and CVSAnalY, that facilitate a deeper understanding of developer behaviour and project evolution. Furthermore, we discuss the implications of these insights for enhancing software engineering practices across both OSS and proprietary environments. This paper provides a conceptual framework that connects theoretical insights with practical approaches, offering actionable strategies to improve processes, foster collaboration, and support learning in software development. Ultimately, the findings hold potential value not only for practitioners aiming to optimize workflows but also for educators leveraging OSS environments to teach and evaluate software engineering concepts.

In Deep Neural Networks (DNN), the depth-wise separable convolution has often replaced the standard 2D convolution having much fewer parameters and operations. Another common technique to squeeze DNNs is heterogeneous quantization, which uses a different bitwidth for each layer. In this context we propose for the first time a novel Reconfigurable Depth-wise convolution Module (RDM), which uses multipliers that can be reconfigured to support 1, 2 or 4 operations at the same time at increasingly lower precision of the operands. We leveraged High Level Synthesis to produce five RDM variants with different channels parallelism to cover a wide range of DNNs. The comparisons with a nonconfigurable Standard Depth-wise convolution module (SDM) on a CMOS FDSOI 28-nm technology show a significant latency reduction for a given silicon area for the low-precision configurations.

Railway wireless communications are critical for transmitting train control and dispatch commands, where high reliability and low latency are essential to ensure operational safety. However, as railway systems become increasingly electrified and more complex, the exposure to electromagnetic interference (EMI) also grows, potentially causing service disruptions and compromising safety. Intentional EMI (IEMI), which is deliberately and often maliciously generated, further increases the vulnerability of these critical communication networks. Realtime detection and classification of EMI and IEMI therefore become increasingly important. This paper presents composite models that reflect realistic railway scenarios. It proposes an adaptive classification approach for EMI and IEMI using a deep learning algorithm based on bidirectional long-short-term memory (BiLSTM) networks and attention mechanisms. By employing time-series feature extraction to analyze both time and frequency information at fine resolution, the proposed method demonstrates a classification accuracy of 94.98%. Simulation results outperform existing techniques with a 3% improvement in accuracy, showcasing its adaptability across four typical railway scenarios at train speeds of up to 500 km/h. Moreover, the online monitoring phase performs real-time detection in just 7.43 ms, meeting the stringent latency requirements for railway systems. Validation using real-world data further confirms the practical applicability of the proposed methods under actual operating conditions.

Target detection using cameras or automotive radar to identify traffic or prevent collisions is an important issue in Autonomous Vehicles (AV) research. Traditional Constant False Alarm Rate (CFAR) methods are commonly employed. Although these methods are suitable for lightweight hardware, improving the target detection process often leads to losing real-time performance. The method proposed in this letter improves detection accuracy. It reduces response time by modifying the position of guard cells in the first stage and employing harmonic averaging (inverse of the sum of the inverse of data) while eliminating data sorting in the second stage. Moreover, this approach exhibits better performance in the presence of interfering targets. Since the proposed method is more applicable to the Range-Doppler map, it has been named RD-CFAR. The proposed method also enhances target detection in Synthetic Aperture Radar (SAR) images.

This paper addresses the modulation, design, and ZVS range extension of a single-stage current-fed fullbridge AC-DC converter with an active clamp circuit. A modulation scheme is proposed to achieve zero voltage turn-ON and zero current turn-OFF with reduced current stress as compared to the traditional phase-shifted modulation scheme. The impact of converter parameters, parasitic elements, and loading is considered while analyzing the switching transitions to understand the ZVS bounds of the converter. It is demonstrated that the ZVS range can be extended by a suitable choice of the dead time. The analysis is validated using switching circuit simulations that incorporate semiconductor models. Furthermore, experimental results from a 1.2 kW hardware prototype operating at 94.5 % efficiency at full load are presented to validate the findings.

Accurate detection of gait events, such as heel strike (HS) and toe off (TO), is critical for the implementation of many lower limb exoskeleton control strategies. While underfoot force sensors are commonly used, their limitations have inspired the development of algorithms to detect these events from kinematic information obtained from inertial measurement units (IMUs) placed on limb segments and encoders at the joints. This work presents an adaptive, user-independent approach to gait event detection, using kinematic information from the embedded sensors of an exoskeleton. These included hip and knee joint encoders and a thigh-worn IMU; additionally, event detection only considered ipsilateral data. The algorithm was evaluated in real-time with seven healthy subjects walking in the Indego Explorer exoskeleton on an instrumented treadmill at varying speeds and slopes. The experiment yielded mean gait phase errors of 0.38% ± 1.82% for HS and 0.54% ± 1.14% for TO, at an accuracy of 99.93% over a total of 4104 steps. The results of this validation suggest that the presented algorithm is a viable solution to inertial-based gait event detection for the application of lower limb exoskeleton control. Future directions for this work include evaluating the algorithm for overground walking and for a controller providing gait assistance.