Optical frequency combs (OFCs) are extensively used in spectroscopy, range finding, metrology, and optical communications. In this paper, we propose a novel technique to achieve a flat OFC by serially cascading two frequency modulators (FMs) followed by a single-drive Mach–Zehnder modulator (MZM). The modulators are driven by a sinusoidal RF signal of frequencies $f_m$, $\frac{f_m}{2}$, and 2 $f_m$ GHz, respectively. With our proposed approach ($f_m$), an optical spectrum of 71 subcarriers spaced at 4 GHz is realized within a power fluctuation of ∼2 dB. The proposed method is also tested for $f_m$ = 16 GHz, showing that this approach can work in all scenarios with lower power fluctuations. In addition, we also studied the impact of the phase of the RF signal on the power variation of the OFC spectrum. A theoretical investigation of the ultra-flat spectrum generated by cascaded FMs and MZM is conducted, and the results of simulations support the findings. The simulation results demonstrate good performance, allowing for the application of our proposed approach in next-generation optical networks.

In this article, we proposed two schemes for generating a broadband optical frequency comb (OFC) source using dual laser sources. In the first proposed technique, the OFC is generated using a cascaded single-drive Mach-Zehnder modulator (MZM) and dual-drive Mach-Zehnder modulator (DD-MZM). Here, the two CW light sources are modulated by each of the MZM, respectively. The first single-drive MZM generates the subcarriers, while The second DD-MZM carries out intensity modulation on the output spectrum generated by the MZM, ensuring that the power fluctuations of both even and odd sidebands are adjusted to an equal level. The second approach involves replacing the single-drive MZM with a phase modulator (PM). The center frequencies of laser 1 and laser 2 must be carefully set to achieve a non-overlapping or gap-free comb spectrum. The frequency of the RF signal driving the modulators determines the spacing between the laser frequencies. In order to illustrate this aspect, we have utilized three distinct RF signal frequencies: 10 GHz, 16 GHz, and 20 GHz. Furthermore, we have examined the constraints that impact the estimation of the center frequency of laser 2, aiming for a non-overlapping and gap-free comb spectrum. By considering these constraints, we can ensure the successful generation of a uniform and stable comb spectrum with the desired characteristics in our proposed approach. The theoretical and primary analysis of cascaded single-drive MZM and DD-MZM, as well as cascaded PM and DD-MZM, is also investigated, and simulation results confirm the findings. By employing our proposed approach, OFC achieves a significant number of comb lines while exhibiting a power deviation of ~ 2 dB in comparison to single laser configurations.

Hybrid magnetic tunnel junction/complementary metal oxide semiconductor (MTJ/CMOS) circuits based on in-memory-computation (IMC) architecture is considered as the next-generation candidate for the digital integrated circuits. However, the energy consumption during the MTJ write process is a matter of concern in these hybrid circuits. In this regard, we have developed a novel write circuit for the contemporary three-terminal perpendicular-MTJs that works on the voltage-gated spin orbit torque (VG+SOT) switching mechanism to store the information in hybrid circuits for IMC architecture. Investigation of the novel write circuit reveals a remarkable reduction in the total energy consumption (and energy delay product) of 92.59% (95.81) and 92.28% (42.03%) than the conventional spin transfer torque (STT) and spin-Hall effect assisted STT (SHE+STT) write circuits, respectively. Further, we have developed all the hybrid logic gates followed by nonvolatile full adders (NV-FAs) using VG+SOT, STT, and SHE+STT MTJs. Simulation results show that with the VG+SOT NOR-OR, NAND-AND, XNOR-XOR, and NV-FA circuits, the reduction in the total power dissipation is 5.35% (4.27%), 5.62% (3.2%), 3.51% (2.02%), and 4.46% (2.93%) compared to STT (SHE+STT) MTJs respectively.

Mental health disorders are becoming a common concern among adults and children alike.With the growth of cloud computing and the rising concerns over data privacy, integrating machine learning (ML) with secure cloud infrastructure for mental health prediction helps in early detection and timely intervention. In this paper, we propose a novel approach of integrating a voting-based ensemble learning with AES-256-encrypted cloud infrastructure on AWS for a secure and scalable mental health prediction. The ensemble model combines five popular machine learning classifiers: Logistic Regression, Naïve Bayes, SupportVector Machine (SVM), XGBoost, and Random Forest. The proposed model was constructed using these classifiers as its base learners. The system was trained on a dataset of 7,731 Reddit posts related to mental health. We have employed AWS S3 storage and AES-256 encryption for data security. To evaluate the effectiveness of the proposed model, we compared its performance against individual classifiers using a range of evaluation metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that the ensemble model consistently outperforms the individual classifiers, achieving superior performance across all metrics. While Reddit posts may not reflect the language or tone used on other platforms—such as clinical notes, journals, or conversation transcripts—thereby limiting the overall generalizability of the model, it demonstrates high accuracy, scalability, and security, making it a viable choice for deployment in real-world clinical settings for early mental health screening.

The expansion of Zigbee-enabled IoT networks has generated significant security issues, especially around threat detection and secure key management. Using RBF and blockchain technology, this study shows a smart hybrid framework to find threats early and distribute keys safely on IoT networks enabled by Zigbee. This methodology incorporates Radial Basis Function (RBF) networks for prompt threat detection and a blockchain-based trust framework for decentralized and tamper-proof key distribution. It guarantees safe network access, comprehensive authentication, and effective key updates, reducing risks associated with IoT-related DoS attacks and Man in the Middle Attacks. The Trust-Based Security Provider (TBSP) enhances security by administering critical credentials across diverse networks. Comprehensive simulations and performance assessments illustrate the effectiveness of the framework in increasing threat detection precision, minimizing key distribution delay, and bolstering overall network security. The findings confirm its efficacy in safeguarding IoT settings from new risks while ensuring scalability and resource efficiency. We proposed an RBF-based threat detection framework for network keys using the ZBDS2023 dataset and the J48 decision tree algorithm. In conclusion, we demonstrate the security and efficiency of our proposed work.

In this paper, we compute the optimal slot width which provides a good trade-off between the blocking probability and network cost, aiming to achieve a spectrally and cost-efficient solution for Elastic Optical Networks (EONs). Here we have determined the blocking performance by varying the slot width from 50 GHz down to 1.5625 GHz, followed by network cost estimation considering various network elements, namely transponders and node equipment. The analysis reveals that reducing the slot width from 50 GHz to 6.25 GHz leads to a substantial reduction in blocking probability, approximately 70%, with an estimated normalized cost increment of approximately 0.6. Further reductions to 3.125 GHz and 1.5625 GHz offer only minor additional improvements in blocking probability, while incurring normalized cost increments that are disproportionate to the minor additional performance gains, rising to approximately 0.75 and 1.0 (relative to the 50 GHz baseline). These results indicate that narrowing the slot width beyond 6.25 GHz leads to diminishing returns in performance at a substantially higher cost. Based on this trade-off analysis, 6.25 GHz is identified as the optimal slot width, providing the best balance between blocking performance improvement and network cost efficiency.