Compared to opportunistic multichannel ALOHA, the proposed method displays a reward enhancement of roughly 10% for a single user and approximately 30% for multiple users. Beyond that, we examine the complex structure of the algorithm and the influence of parameters within the DRL framework during training.
The swift evolution of machine learning has empowered companies to develop sophisticated models that provide predictive or classification services to their clientele, dispensing with the requirement for substantial resources. Various related protective measures exist to shield the privacy of models and user information. Still, these initiatives demand costly communication solutions and are not secure against quantum attacks. To resolve this issue, a new and secure protocol for integer comparison, incorporating fully homomorphic encryption, was conceived. Further, a client-server classification protocol for evaluating decision trees was proposed, built upon this newly developed secure integer comparison protocol. Compared to prior efforts, our classification protocol is remarkably economical in terms of communication, completing the classification task with just a single exchange with the user. Furthermore, a fully homomorphic lattice scheme, which is resistant to quantum attacks, forms the basis of the protocol, in contrast to traditional schemes. Concluding the investigation, an experimental comparison between our protocol and the traditional method was undertaken using three datasets. According to the experimental results, the communication cost of our system was 20% less than the communication cost of the traditional system.
A data assimilation (DA) system in this paper incorporated a unified passive and active microwave observation operator, which is an enhanced, physically-based, discrete emission-scattering model, into the Community Land Model (CLM). The assimilation of Soil Moisture Active and Passive (SMAP) brightness temperature TBp (polarization being either horizontal or vertical) for soil property extraction and combined soil property-soil moisture estimation was performed with the local ensemble transform Kalman filter (LETKF) algorithm, which is the default for the system. Data from in-situ observations at the Maqu site supported this study. The findings reveal a marked improvement in estimating the soil properties of the topmost layer, as compared to the measurements, and of the entire soil profile. For the retrieved clay fraction, comparing background and top layer measurements, both TBH assimilation procedures produced a decrease in root mean square errors (RMSE) exceeding 48%. Both TBV assimilations result in a 36% reduction of RMSE in the sand fraction and a 28% reduction in the clay fraction. However, the DA's calculated values for soil moisture and land surface fluxes still exhibit deviations from the measured values. Precisely determined soil properties, though retrieved, still fall short of improving those projections. The CLM model's structures, particularly its fixed PTF components, present uncertainties that must be addressed.
The wild data set is leveraged in this paper for a facial expression recognition (FER) approach. This paper principally addresses two important areas of concern, occlusion and intra-similarity problems. Facial analysis employing the attention mechanism targets the most significant areas within facial images for specific expressions. The triplet loss function compensates for the intra-similarity problem, which frequently impedes the collection of identical expressions across different faces. The proposed approach for FER demonstrates robustness against occlusions. It leverages a spatial transformer network (STN) combined with an attention mechanism to extract the facial regions most crucial for recognizing expressions like anger, contempt, disgust, fear, joy, sadness, and surprise. mediation model To improve recognition accuracy, the STN model is linked to a triplet loss function, exceeding existing methods which leverage cross-entropy or other approaches using exclusively deep neural networks or classical techniques. The triplet loss module enhances classification by effectively counteracting the restrictions imposed by the intra-similarity problem. The experimental findings support the proposed FER method, achieving higher accuracy than existing approaches, such as in situations with occlusions. A quantitative evaluation of FER results indicates over 209% improved accuracy compared to previous CK+ data, and an additional 048% enhancement compared to the results achieved using a modified ResNet model on FER2013.
Due to the consistent progress in internet technology and the widespread adoption of cryptographic methods, the cloud has emerged as the preeminent platform for data sharing. Typically, encrypted data are sent to cloud storage servers. For regulated and facilitated access to encrypted outsourced data, access control methods are applicable. Controlling access to encrypted data across organizational boundaries, such as in healthcare or inter-organizational data sharing, is facilitated by the promising technique of multi-authority attribute-based encryption. XMU-MP-1 Flexibility in sharing data with individuals, both recognized and unidentified, is something a data owner might need. Internal employees, identified as known or closed-domain users, stand in contrast to external entities, such as outside agencies and third-party users, representing unknown or open-domain users. For closed-domain users, the data owner assumes the role of key issuer; in contrast, for open-domain users, established attribute authorities carry out the task of key issuance. In cloud-based data-sharing systems, safeguarding privacy is a critical necessity. The SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing, is proposed in this work. Policy privacy is preserved by only disclosing the names of policy attributes, encompassing users in both open and closed domains. The attributes' values remain concealed. In contrast to existing analogous schemes, our approach offers simultaneous support for multi-authority setups, expressive access policies, enhanced privacy, and superior scalability. NBVbe medium A reasonable decryption cost is indicated by our performance analysis. The scheme is additionally proven to be adaptively secure, operating according to the standard model's precepts.
In recent research, compressive sensing (CS) methods have been explored as a novel compression paradigm. The approach utilizes the sensing matrix throughout the measurement and reconstruction processes for reconstructing the compressed signal. To ensure efficiency in medical imaging (MI), computer science (CS) is deployed to optimize sampling, compression, transmission, and storage procedures for large volumes of medical image data. Although the CS of MI has been the focus of many investigations, its interplay with color space has not been studied previously in the literature. This article's novel CS of MI methodology, designed to meet these requirements, utilizes hue-saturation-value (HSV), spread spectrum Fourier sampling (SSFS), and sparsity averaging with reweighted analysis (SARA). For a compressed signal, we propose an HSV loop that carries out the SSFS procedure. Following the preceding steps, HSV-SARA is suggested for the reconstruction of the MI data point from the compressed signal data. The research examines multiple color medical imaging techniques, specifically colonoscopies, brain and eye MRIs, and wireless capsule endoscopy images. Evaluations were carried out to establish the superior performance of HSV-SARA against benchmark methodologies, focusing on signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The proposed CS method demonstrated that a color MI, possessing a resolution of 256×256 pixels, could be compressed at a rate of 0.01 using the experimental approach, and achieved a significant enhancement in both SNR (by 1517%) and SSIM (by 253%). Improving medical device image acquisition is a potential benefit of the HSV-SARA proposal, which addresses color medical image compression and sampling.
The current paper scrutinizes the prevalent methods in nonlinear analysis of fluxgate excitation circuits, outlining their shortcomings and emphasizing the pivotal significance of nonlinear analysis for these circuits. Considering the non-linearity of the excitation circuit, this paper presents the use of the core-measured hysteresis curve for mathematical analysis and a nonlinear model, encompassing the core-winding interaction and the effect of the previous magnetic field, for simulation analysis. Experiments prove the applicability of mathematical calculations and simulations to the nonlinear investigation of fluxgate excitation circuit designs. The results reveal that the simulation surpasses a mathematical calculation by a factor of four in the subject area. The excitation current and voltage waveform results, both simulated and experimental, under varying circuit parameters and structures, show a high degree of correlation, differing by no more than 1 milliampere in current. This supports the effectiveness of the non-linear excitation analysis.
A micro-electromechanical systems (MEMS) vibratory gyroscope benefits from the digital interface application-specific integrated circuit (ASIC) introduced in this paper. The interface ASIC's driving circuit employs an automatic gain control (AGC) module, eschewing a phase-locked loop, to achieve self-excited vibration, thereby bestowing robust performance upon the gyroscope system. The co-simulation of the gyroscope's mechanically sensitive structure and its interface circuit necessitates the equivalent electrical model analysis and modeling of the mechanically sensitive gyro structure, achieved via Verilog-A. From the design scheme of the MEMS gyroscope interface circuit, a system-level simulation model, using SIMULINK, was generated. This model integrated the mechanically sensitive structure and measurement and control circuit.