Search Results (80)

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Regeneration of Pinus brutia (Turkish red pine) after clear-cutting is showing failures in some low-elevation Mediterranean stands, raising questions about long-used silvicultural prescriptions. Because site limitations arise from the combined effects of climate, terrain, and surface thermal conditions that vary over short distances, diagnosing where problems may occur is challenging at operational scales. In this study, we first evaluate the study area (Antalya, Türkiye, 0–400 m elevation band) using open, long-term climatic indicators, along with terrain and surface thermal remote sensing variables, to describe recent environmental conditions relevant to germination and early seedling survival. We then build a transparent environmental-analog screening product that summarizes the degraded reference site as an environmental signature and computes pixel-wise similarity across the landscape at 100 m resolution. The resulting map reports three actionable tiers (≥95th, 90–95th, 85–90th percentiles), delineating compact clusters of very-high analogs surrounded by broader high/elevated belts. Interpreted strictly as a screening layer (not a predictive model), it supports compartment-scale triage: ≥95th areas are first candidates for field checks and adjusted prescriptions, while lower tiers guide targeted site preparation and monitoring. The novelty and importance are practical: widely available Earth observation data are converted into a reproducible, auditable tool that reduces dependence on complex predictive models and large calibration samples, while still requiring careful local interpretation and ground-truthing to inform P. brutia regeneration planning. Full article

Stochastic geometry provides a powerful analytical framework for evaluating interference-limited cellular networks with randomly deployed base stations (BSs). While prior studies have examined limited channel state information at the transmitter (CSIT) and low-resolution analog-to-digital converters (ADCs) separately, their joint impact in multi-user multiple-input multiple-output (MIMO) systems remains largely unexplored. This paper investigates a downlink cellular network in which BSs are distributed according to a homogeneous Poisson point process (PPP), employing zero-forcing beamforming (ZFBF) with limited feedback, and receivers are equipped with one-bit ADCs. We derive a tractable approximation for the achievable spectral efficiency that explicitly accounts for both the quantization error from limited feedback and the receiver distortion caused by coarse ADCs. Based on this approximation, we determine the optimal feedback rate that maximizes the net spectral efficiency. Our analysis reveals that the optimal number of feedback bits scales logarithmically with the channel coherence time but its absolute value decreases due to coarse quantization. Simulation results validate the accuracy of the proposed approximation and confirm the predicted scaling behavior, demonstrating its effectiveness for interference-limited multi-user MIMO networks. Full article

In view of the great need for quality assurance in radiotherapy, this paper proposes a stitching-based detector (SBD) technique and a set of intelligent algorithms that can reconstruct the information of projected particle beams. The reconstructed information includes the intensity, sigma value, and location of the maximum intensity of the beam under test. To verify the effectiveness of the proposed technique and algorithms, this research study adopts the pencil beam scanning (PBS) form of proton beam therapy (PBT) as an example. Through the SBD technique, it is possible to utilize 128 × 128 ionization chambers, which constitute an ionization plate of 25.6 cm², with an acceptable number of 4096 analog-to-digital converters (ADCs) and a resolution of 0.25 mm. Through simulation, the proposed SBD technique and intelligent algorithms are proven to exhibit satisfactory and practical performance. By using two kinds of maximum intensity definitions, sigma values ranging from 10 to 120, and two definitions in an erroneous case, the maximum error rate is found to be 3.95%, which is satisfactorily low. Through analysis, this research study discovers that most errors occur near the symmetrical and peripheral boundaries. Furthermore, lower sigma values tend to aggravate the error rate because the beam becomes more like an ideal particle, which leads to greater imprecision caused by symmetrical sensor structures as its sigma is reduced. However, because proton beams are normally not projected onto the border region of the sensed area, the error rate in practice can be expected to be even lower. Although this research study adopts PBS PBT as an example, the proposed SBD technique and intelligent algorithms are applicable to any type of particle beam reconstruction in the field of radiotherapy, as long as the particles under analysis follow a Gaussian distribution. Full article

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case $12\%$ margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems. Full article

An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection from many wide-bandwidth signals within a system, as low-bandwidth information such as DC output voltage is of limited value for decision-making and failure prediction. Analog compression, data profiling, and anomaly detection methods enabled by the unique analog frontend are presented. The system significantly reduces the demand for high computational power, fast communication, and large storage space required for the task. A real-time compression ratio exceeding 100:1 was achieved by the experimental analog frontend, digitizing the analog signal at a rate of 135 MS/s with a 10-bit resolution. The motivation, existing solutions, performance metrics, and advantages of the analog frontend are demonstrated, along with the details of the circuit operation principle. The process of data collection, its intelligent processing using the analog frontend, and anomaly detection are simulated to validate the theoretical hypotheses. For experimental validation, a laboratory setup that includes a dedicated analog frontend prototype and step-down DC-DC converter was built and evaluated to demonstrate the robust performance in sampling and monitoring wide-bandwidth signals and smart data processing using analog frontend for quick decision-making. Full article

New probability models of inherently embodied cognition derived from the asymptotic limit theorems of information and control theories show, where the Weber–Fechner, Stevens, Hick–Hyman, and Pieron’s psychophysics laws—and analogous processes of sensory data rate compression—operate, that sufficient arousal will engender the classic Yerkes–Dodson effect responses for ‘easy’ and ‘difficult’ challenges, depending on the level of ‘noise’ impeding the cognition rate. A ‘hallucination’ mode is found to arise at low arousal, and, in the face of sufficient noise, a ‘panic’ mode at high arousal. Systems that are ‘ductile’ in a formal sense, however, are not afflicted by such hallucination, although panic remains for difficult challenges. Similar dynamics that surround organized conflict on ‘Clausewitz landscapes’ of fog, friction, and deadly adversarial intent have long been studied. We find a central mechanism for cognitive failure under increasing stress across a very broad range of modalities to be enough—usually badly needed—compression of sensory/intelligence and internal information transmission rates. It seems possible, with some effort, to convert the probability models developed here into robust statistical tools for the study and limited control of critical real-time, real-world embodied cognitive phenomena associated with cellular, neural, individual, machine, and institutional systems and their many composites. Full article

Stochastic geometry has emerged as a powerful tool for modeling cellular networks, especially in dense deployment scenarios where inter-cell interference is significant. Previous studies have extensively analyzed multi-antenna systems with partial channel state information at the transmitter (CSIT) using stochastic geometry models. However, most of these works assume the use of infinite-resolution analog-to-digital converters (ADCs) at the receivers. Recent advances in low-resolution ADCs, such as one-bit ADCs, offer an energy-efficient alternative for millimeter-wave systems, but the interplay between limited feedback and one-bit ADCs remains underexplored in such networks. This paper addresses this gap by analyzing the optimal feedback rate that maximizes net spectral efficiency in dense cellular networks, modeled using stochastic geometry, with both limited feedback and one-bit ADC receivers. We introduce an approximation of the achievable spectral efficiency to derive a differentiable expression of the optimal feedback rate. The results show that while the scaling behavior of the optimal feedback rate with respect to the channel coherence time remains unaffected by the ADC’s resolution, the absolute values are significantly lower for one-bit ADCs compared to infinite-resolution ADCs. Simulation results confirm the accuracy of our theoretical approximations and demonstrate the impact of ADC resolution on feedback rate optimization. Full article

Inspired by classical polar codes, whose coding rate can asymptotically achieve the Shannon capacity, researchers are trying to find their analogs in the quantum information field, which are called quantum polar codes. However, no one has designed a quantum polar coding scheme that applies to quantum computing yet. There are two intuitions in previous research. The first is that directly converting classical polar coding circuits to quantum ones will produce the polarization phenomenon of a pure quantum channel, which has been proved in our previous work. The second is that based on this quantum polarization phenomenon, one can design a quantum polar coding scheme that applies to quantum computing. There are several previous work following the second intuition, none of which has been verified by experiments. In this paper, we follow the second intuition and propose a more reasonable quantum polar stabilizer code construction algorithm than any previous ones by using the theory of stabilizer codes. Unfortunately, simulation experiments show that even the stabilizer codes obtained from this more reasonable construction algorithm do not work, which implies that the second intuition leads to a dead end. Based on the analysis of why the second intuition does not work, we provide a possible future direction for designing quantum stabilizer codes with a high coding rate by borrowing the idea of classical polar codes. Following this direction, we find a class of quantum stabilizer codes with a coding rate of 0.5, which can correct two of the Pauli errors. Full article

Among many other applications, electronic converters can be used with sensors with analogue outputs (DC voltage). This article presents an analogue computation converter with two DC voltages at the inputs (one input changes the frequency of the output signal, another input changes the amplitude of the output signal) that provide a periodic sinusoidal signal (with variable frequency and amplitude) at the output. On the basis of the analogue computation converter is a nonhomogeneous second-order linear ordinary differential equation which is solved analogically. The analogue computation converter consists of analogue multipliers and operational amplifiers, composed of seven function circuits: two analogue multiplication circuits, two analogue addition circuits, one non-inverting amplifier, and two integration circuits (with RC time constants). At the output of an oscillator is a sinusoidal signal which depends on the DC voltages applied on two inputs (0 ÷ 10 V): at one input, a DC voltage is applied to linearly change the sinusoidal frequency output (up to tens of kHz, according to two time constants), and at the other input, a DC voltage is applied to linearly change the amplitude of the oscillator output signal (up to 10 V). It can be used with sensors which have a DC output voltage and must be converted to a sine wave signal with variable frequency and amplitude with the aim of transmitting information over longer distances through wires. This article presents the detailed theory of the functioning, simulations, and experiments of the analogue computation converter. Full article

In recent years, the widespread adaptation of Unmanned Aerial Vehicles (UAVs), commonly known as drones, among the public has led to significant security concerns, prompting intense research into drones’ classification methodologies. The swift and accurate classification of drones poses a considerable challenge due to their diminutive size and rapid movements. To address this challenge, this paper introduces (i) a novel drone classification approach utilizing deep convolution and deep transfer learning techniques. The model incorporates bypass connections and Leaky ReLU activation functions to mitigate the ‘vanishing gradient problem’ and the ‘dying ReLU problem’, respectively, associated with deep networks and is trained on a diverse dataset. This study employs (ii) a custom dataset comprising both audio and visual data of drones as well as analogous objects like an airplane, birds, a helicopter, etc., to enhance classification accuracy. The integration of audio–visual information facilitates more precise drone classification. Furthermore, (iii) a new Finite Impulse Response (FIR) low-pass filter is proposed to convert audio signals into spectrogram images, reducing susceptibility to noise and interference. The proposed model signifies a transformative advancement in convolutional neural networks’ design, illustrating the compatibility of efficacy and efficiency without compromising on complexity and learnable properties. A notable performance was demonstrated by the proposed model, with an accuracy of 100% achieved on the test images using only four million learnable parameters. In contrast, the Resnet50 and Inception-V3 models exhibit 90% accuracy each on the same test set, despite the employment of 23.50 million and 21.80 million learnable parameters, respectively. Full article

High-resolution multi-channel digitizers are used extensively for precision low voltage measurements in numerous applications and allow the simultaneous measurement of voltage magnitude ratio and phase difference between two different waveforms in power system applications. Delta–sigma-based analog-to-digital conversion enables the use of sampling frequencies in the range of megahertz, which provides accurate measurement bandwidths for transformed high-frequency, high-voltage signals. With the increased use of power electronic converters contributing to high-frequency harmonic emissions in power systems, there is a growing interest in developing calibration systems to measure voltage ratio and phase difference of distorted fundamental frequency waveforms consisting of superimposed, high-frequency harmonics. However, information regarding the accuracy of the high-resolution digitizers in the measurement of distorted voltage waveforms is limited as characterization is typically performed under sinusoidal voltage waveform conditions. This paper presents the details of the accuracy characterization of a 24-bit resolution digitizer under both sinusoidal and distorted waveform conditions for measuring complex voltage ratio and phase error for frequencies up to 10 kHz. The detailed experimental results and the measurement uncertainty evaluations show that increased voltage ratio and phase difference errors should be allocated when these high-resolution digitizers are used to measure distorted voltage waveforms. The estimated expanded uncertainties of complex voltage ratio measurement and phase error measurement for harmonic frequencies up to 10 kHz are ±260 ppm and ±100 µrad, respectively. Full article

This research explores the forefront of all-optical data processing systems through the utilization of carrier reservoir semiconductor optical amplifiers (CR-SOAs). Recent advancements have showcased the successful design and implementation of CR-SOA-based combinational systems, incorporating pivotal elements like half adders, half subtractors, digital-to-analog converters, latches, header recognition, and header processors. These breakthroughs signify a significant stride towards the realization of faster and more efficient optical logic systems. This study delves into the distinctive characteristics of CR-SOA-based Mach–Zehnder interferometer (MZI) functioning as an XOR gate, emphasizing their transformative potential in information processing. By integrating them into the architecture of an all-optical 4-bit parity generator and checker, the research underscores the practicality of CR-SOA technology in all-optical processing, offering unprecedented speeds and facilitating enhanced data processing capabilities at a remarkable speed of 120 Gb/s return-to-zero pulses. In evaluating the performance of the proposed scheme, the research employs the quality factor metric. This assessment not only yields quantitative insights into the efficacy of CR-SOA-based logic systems but also establishes a critical benchmark for their practical implementation. The study further explores the impact of key data signals and CR-SOA parameters on this metric. The outcomes demonstrate the ability of the CR-SOA-based MZI to cascade and form more intricate logic circuits, thereby highlighting the versatility and potential of this innovative approach in advancing the landscape of all-optical data processing. Full article

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target. Full article