Search Results (15)

With the increasing demand for high throughput and ultra-dense small cell deployment in the next-generation communication networks, spectrum resources are becoming increasingly strained. At the same time, the security risks posed by eavesdropping remain a significant concern, particularly due to the broadcast-access property of optical fronthaul networks. To address these challenges, we propose a high-security, high-spectrum efficiency radio-over-fiber (RoF) system in this paper, which leverages compressive sensing (CS)-based algorithms and chaotic encryption. An 8 Gbit/s RoF system is experimentally demonstrated, with 10 km optical fiber transmission and 20 GHz radio frequency (RF) transmission. In our experiment, spectrum efficiency is enhanced by compressing transmission data and reducing the quantization bit requirements, while security is maintained with minimal degradation in signal quality. The system could recover the signal correctly after dequantization with 6-bit fronthaul quantization, achieving a structural similarity index (SSIM) of 0.952 for the legitimate receiver (Bob) at a compression ratio of 0.75. In contrast, the SSIM for the unauthorized receiver (Eve) is only 0.073, highlighting the effectiveness of the proposed security approach. Full article

This study presents a noise-resilient masked-face detection framework optimized for the NVIDIA Jetson AGX Orin, which improves detection precision by approximately 30% under severe Gaussian noise (variance 0.10) while reducing denoising latency by over 42% and increasing end-to-end throughput by more than 30%. The proposed system integrates a lightweight DnCNN-based denoising stage with the YOLOv11 detector, employing Quantize-Dequantize (QDQ)-based INT8 post-training quantization and a parallel CPU–GPU execution pipeline to maximize edge efficiency. The experimental results demonstrate that denoising preprocessing substantially restores detection accuracy under low signal quality. Furthermore, comparative evaluations confirm that 8-bit quantization achieves a favorable accuracy–efficiency trade-off with only minor precision degradation relative to 16-bit inference, proving the framework’s robustness and practicality for real-time, resource-constrained edge AI applications. Full article

The quantizer–dequantizer method is employed. Using the construction of probability distributions describing density operators of a quantum system states, the connection between the Feynman path integral and the time evolution of the density operator (Landau density matrix) as well as the wave function of the stateconsidered. For single–mode systems with continuous variables, a tomographic propagator is introduced in the probability representation of quantum mechanics. An explicit expression for the probability in terms of the Green function of the Schrödinger equation is obtained. Equations for the Green functions defined by arbitrary integrals of motion are derived. Examples of probability distributions describing the evolution of state of a free particle, as well as states of systems with integrals of motion that depend on time (oscillator type) are discussed. Full article

Battery parameter identification is a key challenge for battery management systems, as parameterizing lithium-ion batteries is resource-intensive. Electrical circuit models (ECMs) provide an alternative, but their parameters change with physical conditions and battery age, necessitating regular parameter identification. This paper presents two modular algorithms to improve data quality and enable fast, robust parameter identification. First, the dequantizer algorithm restores the time series generating the noisy, quantized data using the inverse normal distribution function. Then, the Linear Continuous-Time Regression (LCTR) algorithm extracts exponential parameters from first-order or overdamped second-order systems, deducing ECM parameters and guaranteeing optimality with respect to RMSE. The parameters have low sensitivity to measurement noise since they are continuous-time. Sensitivity analyses confirm the algorithms’ suitability for battery management across various Gaussian measurement noise, accuracy, time constants and state-of-charge (SoC), using evaluation metrics like root-mean-square-error (RMSE) (<2 mV), relative time constant errors, and steady-state error. If the coarseness of rounding is not extreme, the steady-state is restored within a fraction of a millivolt. While a slight overestimation in the lower time constants occurs for overdamped systems, the algorithms outperform the conventional benchmark for first-order systems. Their robustness is further validated in real-life applications, highlighting their potential to enhance commercial battery management systems. Full article

Federated learning enables devices to train models collaboratively while protecting data privacy. However, the computing power, memory, and communication capabilities of IoT devices are limited, making it difficult to train large-scale models on these devices. To train large models on resource-constrained devices, federated split learning allows for parallel training of multiple devices by dividing the model into different devices. However, under this framework, the client is heavily dependent on the server’s computing resources, and a large number of model parameters must be transmitted during communication, which leads to low training efficiency. In addition, due to the heterogeneous distribution among clients, it is difficult for the trained global model to apply to all clients. To address these challenges, this paper designs a sparse gradient collaborative federated learning model for heterogeneous data on resource-constrained devices. First, the sparse gradient strategy is designed by introducing the position Mask to reduce the traffic. To minimize accuracy loss, the dequantization strategy is applied to restore the original dense gradient tensor. Second, the influence of each client on the global model is measured by Euclidean distance, and based on this, the aggregation weight is assigned to each client, and an adaptive weight strategy is developed. Finally, the sparse gradient quantization method is combined with an adaptive weighting strategy, and a collaborative federated learning algorithm is designed for heterogeneous data distribution. Extensive experiments demonstrate that the proposed algorithm achieves high classification efficiency, effectively addressing the challenges posed by data heterogeneity. Full article

We derive the probability representation of even and odd cat states of two and three qubits. These states are even and odd superpositions of spin-1/2 eigenstates corresponding to two opposite directions along the z axis. The probability representation of even and odd cat states of an oscillating spin-1/2 particle is also discussed. The exact formulas for entangled probability distributions describing density matrices of all these states are obtained. Full article

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp. Full article

Although optimizing deep neural networks is becoming crucial for deploying the networks on edge AI devices, it faces increasing challenges due to scarce hardware resources in modern IoT and mobile devices. This study proposes a quantization method that can quantize all internal computations and parameters in the memory modification. Unlike most previous methods that primarily focused on relatively simple CNN models for image classification, the proposed method, Unified Scaling-Based Pure-Integer Quantization (USPIQ), can handle more complex CNN models for object detection. USPIQ aims to provide a systematic approach to convert all floating-point operations to pure-integer operations in every model layer. It can significantly reduce the computational overhead and make it more suitable for low-power neural network accelerator hardware consisting of pure-integer datapaths and small memory aimed at low-power consumption and small chip size. The proposed method optimally calibrates the scale parameters for each layer using a subset of unlabeled representative images. Furthermore, we introduce a notion of the Unified Scale Factor (USF), which combines the conventional two-step scaling processes (quantization and dequantization) into a single process for each layer. As a result, it improves the inference speed and the accuracy of the resulting quantized model. Our experiment on YOLOv5 models demonstrates that USPIQ can significantly reduce the on-chip memory for parameters and activation data by ~75% and 43.68%, respectively, compared with the floating-point model. These reductions have been achieved with a minimal loss in mAP@0.5—at most 0.61%. In addition, our proposed USPIQ exhibits a significant improvement in the inference speed compared to ONNX Run-Time quantization, achieving a speedup of 1.64 to 2.84 times. We also demonstrate that USPIQ outperforms the previous methods in terms of accuracy and hardware reduction for 8-bit quantization of all YOLOv5 versions. Full article

A short description of the notion of states of quantum systems in terms of conventional probability distribution function is presented. The notion and the structure of entangled probability distributions are clarified. The evolution of even and odd Schrödinger cat states of the inverted oscillator is obtained in the center-of-mass tomographic probability description of the two-mode oscillator. Evolution equations describing the time dependence of probability distributions identified with quantum system states are discussed. The connection with the Schrödinger equation and the von Neumann equation is clarified. Full article

The review of new formulation of conventional quantum mechanics where the quantum states are identified with probability distributions is presented. The invertible map of density operators and wave functions onto the probability distributions describing the quantum states in quantum mechanics is constructed both for systems with continuous variables and systems with discrete variables by using the Born’s rule and recently suggested method of dequantizer–quantizer operators. Examples of discussed probability representations of qubits (spin-$1/2$, two-level atoms), harmonic oscillator and free particle are studied in detail. Schrödinger and von Neumann equations, as well as equations for the evolution of open systems, are written in the form of linear classical–like equations for the probability distributions determining the quantum system states. Relations to phase–space representation of quantum states (Wigner functions) with quantum tomography and classical mechanics are elucidated. Full article

We review the method of quantizers and dequantizers to construct an invertible map of the density operators onto functions including probability distributions and discuss in detail examples of qubit and qutrit states. The biphoton states existing in the process of parametric down-conversion are studied in the probability representation of quantum mechanics. Full article

In view of the probabilistic quantizer–dequantizer operators introduced, the qubit states (spin-1/2 particle states, two-level atom states) realizing the irreducible representation of the $SU\left(2\right)$ symmetry group are identified with probability distributions (including the conditional ones) of classical-like dichotomic random variables. The dichotomic random variables are spin-1/2 particle projections $m=\pm 1/2$ onto three perpendicular directions in the space. The invertible maps of qubit density operators onto fair probability distributions are constructed. In the suggested probability representation of quantum states, the Schrödinger and von Neumann equations for the state vectors and density operators are presented in explicit forms of the linear classical-like kinetic equations for the probability distributions of random variables. The star-product and quantizer–dequantizer formalisms are used to study the qubit properties; such formalisms are discussed for photon tomographic probability distribution and its correspondence to the Heisenberg–Weyl symmetry properties. Full article

The probability representation of quantum mechanics where the system states are identified with fair probability distributions is reviewed for systems with continuous variables (the example of the oscillator) and discrete variables (the example of the qubit). The relation for the evolution of the probability distributions which determine quantum states with the Feynman path integral is found. The time-dependent phase of the wave function is related to the time-dependent probability distribution which determines the density matrix. The formal classical-like random variables associated with quantum observables for qubit systems are considered, and the connection of the statistics of the quantum observables with the classical statistics of the random variables is discussed. Full article

Discretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aimed at approximating the high bit depth of the original signal. Our method models a region with a bended plate model, which leads to the biharmonic equation. This method addresses several new aspects: the reconstruction of non-continuous regions when foreground objects split the area into separate regions; the incorporation of confidence about pixel levels, making the model tunable; and the method gives a physics-inspired way to handle local maximal/minimal regions. The solution of the biharmonic equation yields a smooth high-order signal approximation and handles the local maxima/minima problems. Full article