Young’s double slit (and Arago spot) experiments revived the wave theory of light. The standard interpretation is that the light behaves as waves before and after passing through the double slit. Feynman called the double slit experiment the only mystery in quantum mechanics. In this article, we postulate and experimentally confirm the precise rules of convex lenses. Based on those Postulates, we study the evolution of the patterns of single slit, double slit, triple slit, cross-double slit, disc ring, 1D-grating and 2D-grating experiments. The experiments show, for the first time, the novel universal phenomena: (1) Within a certain macroscopic distance from the diaphragm (Zone-1), light behaves as photons and produces a non-interference pattern, while near the screen (the far field, Zone-3), the photons produce an interference pattern, while between Zone-1 and Zone-3 (Zone-2), the photons produce an non-interference pattern; namely the nature and characteristics of the pattern are distance dependent; (2) The non-interference pattern evolves to the interference pattern gradually in the same experiment; (3) The convex lens stops the evolution of the patterns. The experiments provide profound phenomena/data for further theoretical development of optics and for understanding the nature of light. We suggest that a complete theory should be able to interpret the non-interference pattern in Zone-1, the non-interference pattern in Zone-2, and the interference pattern in Zone-3; and the evolution from one pattern to another.

We introduce a neural network model for regression in which prediction uncertainty is quantified by Gaussian random fuzzy numbers (GRFNs), a newly introduced family of random fuzzy subsets of the real line that generalizes both Gaussian random variables and Gaussian possibility distributions. The output GRFN is constructed by combining GRFNs induced by prototypes using a combination operator that generalizes Dempster’s rule of Evidence Theory. The  three output units indicate the most plausible  value of the response variable, variability around this value, and epistemic uncertainty. The network is trained by minimizing a loss function that generalizes the negative log-likelihood. Comparative experiments show that this method is competitive, both in terms of prediction accuracy and calibration error, with state-of-the-art techniques such as random forests or deep learning with Monte Carlo dropout. In addition, the model outputs a predictive belief function that can be shown to be calibrated, in the sense that it allows us to compute conservative prediction intervals with specified belief degree.

Understanding the relationship between structure and sentiment is essential in highlighting future operations with online social networks. More specifically, within popular conversation on Twitter. This paper provides a development on the relationship between the two variables: structure, defined as the composition of a directed network, and sentiment, a quantified value of the positive/negative connotations of a conversation. We highlight thread sentiment to be inversely proportional to the strength and connectivity of a network. The second portion of this paper highlights differences in query types, specifically how the aforementioned behavior differs within four key query types. This paper focuses on topical, event-based, geographic, and individual queries as orientations which have differing behavior. Using cross-query analysis, we see that the relationship between structure and sentiment, though still inversely proportional, differs greatly across query types. We find this relationship to be the most clear within the individual queries and the least prevalent within the event-based queries. This paper provides a sociological progression in our understanding of opinion and networks, while providing a methodological advancement for future studies on similar subjects.

The last decade witnessed the proliferation of automated content analysis in communication research. However, existing computational tools have been taken up unevenly, with powerful deep learning algorithms such as transformers rarely applied as compared to lexicon-based dictionaries. To enable social scientists to adopt modern computational methods for valid and reliable sentiment analysis of English text, we propose an open and free web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates diverse tools and offers validation metrics, empowering users with limited computational knowledge and resources to reap the benefit of state-of-the-art computational methods. Two cases demonstrate the functionality and usability of TLSA. The performance of different tools varied to a large extent based on the dataset, supporting the importance of validating various sentiment tools in a specific context.

Engineering education is constantly evolving to keep up with the latest technological developments and meet the changing needs of the engineering industry. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. ChatGPT has the potential to offer personalized and effective learning experiences by providing students with customized feedback and explanations, as well as creating realistic virtual simulations for hands-on learning. However, it is important to also consider the limitations of this technology. ChatGPT and other generative AI systems are only as good as their training data and may perpetuate biases or even generate and spread misinformation. Additionally, the use of generative AI in education raises ethical concerns such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. While the current state of generative AI technology represented by ChatGPT is impressive but flawed, it is only a preview of what is to come. It is important for engineering educators to understand the implications of this technology and study how to adapt the engineering education ecosystem to ensure that the next generation of engineers can take advantage of the benefits offered by generative AI while minimizing any negative consequences.

Concerning the Perspective 3-Point (P3P) Problem, Grunert’s system of three quadratic equations has a repeated solution if and only if the cubic polynomial introduced by Finsterwalder has a repeated root. This polynomial is here shown to be obtainable from a particularly simple cubic polynomial with complex coefficients via a simple Möbius transformation. This provides surprising geometric insight into the P3P problem. In particular, (1) the discriminant of Finsterwalder’s polynomial can be written using the formula for the standard deltoid curve, and (2) this discriminant vanishes on a surface that approaches a deltoid shape when the camera is moved infinitely far from the control points in a direction perpendicular to the control points plane (the “limit case”). These two facts have been previously reported, but obscure reasoning was required to establish them. In contrast, the present article uses the newly discovered cubic polynomial to easily produce the first fact, which then provides a basis for better understanding the second fact. A detailed geometric description of the P3P solution points in the “limit case” is also provided.

Interference based microwave photonic bandpass filters (MPBF) form an important component of modern RF signal processing. The presence of multiple optical components in an RF photonic system e.g. fiber amplifiers, waveguides, cause a degradation in the MPBF profile and the out-of-band rejection due to the dispersion induced phase shift. Here, we compare the performance, using simulations and experiments, of two interference based MPBF configurations: (i) double-sideband phase modulation (DSB-PM) and (ii) double-sideband intensity modulation (DSB-IM), which exploit stimulated Brillouin scattering (SBS) for sideband processing, under different dispersion conditions. While the profile and out-of-band rejection for the DSB-PM based MPBF degrade drastically under the effect of dispersion, DSB-IM based MPBF achieves an out-of-band rejection of $>$ 40 dB and maintain the 3 dB and 20 dB bandwidths of 16.7 $\pm$ 0.4 MHz and 104.5 $\pm$ 5.4 MHz, respectively, under different dispersion conditions. The DSB-IM based MPBF exploits the bias dependent phase of the modulation sidebands of a z-cut intensity modulator to mitigate the dispersion induced impairment. The use of an intensity modulator for dispersion compensation makes our approach dynamic and compatible with integrated microwave photonic systems.

This paper investigates covert communication in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) systems, assuming for the sake of being more practical that the successive interference cancellation (SIC) is defective. To assess the covertness performance of the considered system, we first derive closed-form expressions for the detection error probability, the optimal detection threshold and the average minimum detection error probability of the warden, as well as the outage probability of the NOMA user pair. Then an optimization problem to maximize the effective covert rate (ECR) is proposed. According to the results of the simulation, it is possible to increase the maximum ECR of the considered system by varying the amount of components of the STAR-RIS.

By extending metric space to expression in hash of directed graph, spatiotemporal observation could be described in matrix quantized state. (Working paper, more contents would be added soon)

The Levelized Cost of Electricity (LCOE) is a widely used economic parameter that determines a power plant’s unit cost of energy over its lifetime. In fact, it facilitates economic decisions and quantitative comparisons between different energy generation technologies. Previous methods for calculating the LCOE were based on fixed singular input values that do not capture the uncertainty associated with determining the financial feasibility of a PV project. Instead, we propose a dynamic model that takes into account important demographic, energy and policy data that includes interest rates, inflation rates and energy yield. All these parameters will undoubtedly vary during a photovoltaic (PV) system’s lifetime and will help determine a more accurate LCOE value. Furthermore, comparisons between different ML algorithms revealed that the ARIMA model gave an accuracy of 93.8% for predicting the consumer price of electricity. Moreover, we validated our proposed model with two case studies from the United States and the Philippines. Results from these case studies revealed that LCOE values for the State of California can be almost 30% different (5.03 cent/kWh for singular values in comparison to 7.09 cent/kWh using our ML model), which can distort the risk or economic feasibility of a PV power plant. Additionally, our ML model predicts the ROI of a grid-connected PV plant in the Philippines to be 5.37 years instead of 4.23 years.

In this article, I/We report a novel eye-movement assessment method using a digital camera to measure eye conjugacy in healthy individuals while performing a neurological examination. This is clinically significant because this approach overcomes the limitations of complex and expensive setups (e.g., infrared cameras) that often make it impractical to scale up and translate to clinical use. Moreover, this approach removes the need for a calibration procedure which has caused prior studies to exclude participants, potentially introducing selection bias and limiting generalizability. Our study suggests that this technology could be deployed for clinical use in the clinic or pre-hospital setting, including telemedicine or emergency medical services (EMS) encounters to detect neurological injury or diseases that cause neuro-ocular deficits, like stroke.

M-ary Aggregate Spread Pulse Modulation (M-ASPM) is the physical layer (PHY) modulation technique that is well suited for use in low-power wide-area networks (LPWANs). Notably, M-ASPM combines high energy-per-bit efficiency, robustness, resistance to interference, and a number of other favorable technical characteristics, with the spread-spectrum ability to maintain the capacity of an uplink-focused network while extending its range. However, when all M-ASPM nodes transmit with the same average power, implementation of such capacity-preserving range extension may become impractical in complicated propagation environments with greatly varying path losses. Favorably, the efficiency of M-ASPM with constant-envelope pulses can be maintained effectively the same as the efficiency of transmitting a continuous constant-envelope waveform. Then the transmit power of different nodes can be adjusted, without sacrificing the transmission efficiency, to compensate for differences in the path attenuation. This enables us to significantly simplify planning and management of the network. In addition, such a variable-power approach generally increases the network capacity and the average energy efficiency of the nodes, as compared with the arrangement of the nodes with a constant transmit power. In this paper, we outline a practical approach to implementing such an energy-efficient M-ASPM power control, that can be used for scaling LPWANs with realistic desired and/or actual areal distributions of the uplink nodes under diverse propagation conditions.

We propose a simple yet highly efficient and robust active learning (AL) framework for image classification. Most of the existing AL strategies are either not scalable with increasing acquisition batch sizes or not robust to noise. They select samples greedily without considering the acquisition state of previous iteration. Further, very little focus has been given to the selection of the initial seed set for active learning. In this work, we propose a new framework that combines simulated annealing within AL to select those samples which improve their acquisition cost in the previous iteration. A convex combination of a diversity measure and an uncertainty measure is used as the acquisition cost. The diversity measure ensures consistent prediction of samples lying farthest from the decision boundaries and, eventually, an unbiased estimation of uncertainty. We demonstrate the efficiency and robustness of our proposed framework over the current state of the art AL strategies using Bayesian CNNs.

This paper surveys different arrival and service curve estimation methods for basic linear functions from experimental data. We experiment with methods we found in literature and discuss the benefit and deficit of these methods. We propose two own developed simple methods inspired by literature, to estimate rate-latency service curves matching to our use case to analyze a soft real-time streaming system with Network Calculus. We furthermore apply three methods to a novel field for performance evaluation with network calculus: hardware-in-the-loop test benches. The performance evaluation of these test systems and the two estimation methods are our contribution to the field of network calculus applicated to soft real-time streaming systems. The performance metric playback buffer and the minimum needed pre-buffer time are dimensioned with the help of adapted and mathematically proven network calculus solutions for streaming devices from our last paper. Linear network calculus elements are generated in three different ways based on measurements of software processing latencies and network latencies. In the next step, playback buffer backlog and delay bounds are calculated based upon the three service curve estimation methods by applying network calculus to the measurements. These calculated bounds are compared to each other and discussed and furthermore verified by a discrete-event simulation model with trace-driven input and with distribution fits of the processing latencies. The simulation model uses a variation of the workload to prove the system, while being operated in soft real-time operating point, with the mean arrival-rate near the mean service-rate and above the minimum service-rate. We recommend using the delay metrics by NC with a safety-factor to design the pre-buffer time.

Algorithm-aware (AlgAw) qubit mapping aims at directly providing the solutions to qubit mapping of algorithms with regular structures based on the algorithm’s features. Although exact methods can provide high quality solutions, their compilation time grows exponentially with circuit size. To improve scalability, we propose the AlgAw qubit mapping approach. The main idea is to first identify subcircuits in an algorithm to be mapped, then analyze optimal solutions for small scale subcircuits found by exact methods, extend these solutions to large scale subcircuits, reconstruct the entire circuit and assign parameters, and finally map pretranspiled circuits onto the target quantum devices. Applying AlgAw to the quantum approximate optimization algorithm (QAOA) on linear and T-shaped subtopologies produces optimal and scalable solutions for arbitrary numbers of qubits and depths, which is critical to the algorithm’s performance on near-term quantum devices. Compared to other methods including Qiskit, Tket, and SWAP-network, AlgAw produces the least number of CNOT gates and the lowest circuit depth for the pretranspiled circuits. Furthermore, AlgAw takes only a few seconds to construct a resulting circuit having a hundred qubits. The benchmarking results on IBM quantum devices show that AlgAw qubit mapping yields better performance than others. AlgAw is applicable to other variational quantum algorithms (VQAs) and can also be applied to other algorithms.

Current solutions for distance estimation using the IEEE 802.15.4 TSCH standard mainly focus on improving localization performance, but in turn reduce communication performance. In this paper, we propose a novel improvement to the TSCH protocol that enables distance estimation between two devices without compromising the ongoing communication. We further improve the current state-of-the art phase-based distance estimation algorithm by combining conclusions from several research works into a single solution, that reduces energy consumption while maintaining the accuracy and sensitivity of the distance estimation results. We implemented the proposed method in the Contiki-NG OS and created a real-world measurement campaign to study the impact of the proposed solution on communication and energy consumption, which is of great concern in low-power IoT networks.

Machine learning (ML) solutions are being applied in many areas of our daily lives, but they often require high-quality, balanced datasets in order to perform well. However, datasets for real-world problems are often imbalanced, requiring the use of special-purpose ML algorithms or synthetic data to address the class imbalance. Traditional techniques such as Synthetic Minority Oversampling Technique (SMOTE) and generative models such as Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN) are commonly used to generate minority class samples. Evaluating the quality of synthetic samples can be challenging, and researchers often rely on improved classifier performance as justification for their use. However, simply performing well on a test set is not sufficient to ensure the trustworthiness of a model, and further analysis of model predictions is necessary. To address this, we trained multiple classifiers on synthetic data generated by various methods and analyzed their predictions using SHAP, an explainable AI technique. Our in-depth analysis showed that these classifiers used different features for making predictions and placed different levels of importance on commonly used features. Therefore, we conclude that classification models trained on synthetic data must be carefully analyzed by human experts before being deployed in the real world.

Satellite images captured by the sensor do not contain the reflectance values returned from the earth due to absorption and scattering caused by the atmospheric elements. Atmospheric correction (AC) retrieves the actual reflectance values, called surface reflectance (SR). Existing models are based on physics-based radiative transfer codes that rely on precomputed lookup tables, require many atmospheric parameters, and involve high computation costs. A few of these parameters are difficult to calculate and must be estimated. Deep learning models can be an excellent approximator to such physics-based AC models. In the proposed paper, we design, develop and analyze a deep learning end-to-end model. It is trained with a seasonally and spatially rich dataset to perform AC of Landsat 8 satellite images without explicitly considering atmospheric parameters. Experiments on model predictions are carried out to validate the effectiveness using Landsat data and ground measurements provided by RadCalNet. The results are encouraging because the model predicts SR values with good accuracy and establishes a high correlation with the reference data.