Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices

We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.Comment: Will be appeared in OFC 202

Improved Polarization Tracking in the Presence of PDL

We propose a novel tracking algorithm for optical channels suffering from fast state of polarization (SOP) rotations and polarization-dependent loss (PDL). Unlike gradient descent-based algorithms that require step size adjustment when the channel conditions change, our algorithm performs similarly or better without parameter tuning

Optimal Condition of DME Production Through Syngas Hydrogenation in Dual Membrane Reactor

Typically, supporting the dimethyl ether synthesis reactor by hydrogen and steam permselective membrane modules and optimization of operating conditions are practical solutions to shift the equilibrium conversion of reactions toward dimethyl ether synthesis and CO2 conversion. In this regard, the aim of this research is to calculate the desired condition of hydrogen and steam Selective membrane reactors to improve dimethyl ether productivity. At first, the mass and energy conservation laws are applied to the membrane supported reactor to develop a heterogeneous model. After model validation, an optimization problem is programmed to calculate the optimal value of manipulated variables considering the limitations and constraints of the problem. Then, the main parameters of conventional and optimized membrane supported reactor including carbon monoxide and carbon dioxide conversion, dimethyl ether productivity, and temperature profiles are presented at steady-state conditions. The results of the simulation prove that dimethyl ether productivity is 0.0211 and 0.0262 mole s-1 in conventional and optimized membrane reactors, respectively. In general, operating at optimal conditions increases DME production by up to 24.2%

DESIGN AND SIMULATION OF HIGH PRECISION SECOND-ORDER SIGMA-DELTA MODULATOR FOR BLUETOOTH APPLICATIONS

A second-order sigma-delta modulator is presented in this paper which, according to the considered standards, is suitable for bluetooth applications. The oversampling ratio and signal bandwidth of the proposed modulator is 128 and 1 MHz, respectively. The Signal to Noise and Distortion Ratio (SNDR) of the proposed structure is achieved 74 dB, equivalent to 12-bit accuracy, which is desirable precision for the aforementioned application. Utilizing highperformance blocks to implement the system at the circuit level, the sigma-delta modulator has obtained an overall desirable performance more specifically in terms of minimizing the power consumption. The modulator is simulated in 180 nm CMOS TSMC technology at cadence software applying 1.8 V supply voltage. The power consumption is obtained as low as 1.9 mW very suitable for portable modern ultra-low power applications

RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.Comment: Mohammad Heydari Rad, Farhan Farsi, and Shayan Bali have made equal contributions to this wor

Economics Sanction and Barley Price Regime Change in Iran

In Iran, barley is considered the second-largest cultivated crop. However, more than 40% of Iran's requirements are imported from the international market. Due to the importance of barley in providing livestock feed and food security, its price variation is a critical issue for Iranian governments. Therefore, in this study, the influence of different determinants of domestic barley price, such as international price, real effective exchange rate variation, price volatility of barley, Russian-Ukrainian armed conflict, and the existence of economic sanctions, has been investigated by applying the Markov-Switching model. The main results indicated that in both states, the real effective exchange rate was the primary determinant of the domestic price. Moreover, the impact of international price in first state is much more powerful than the second state. Also, the results revealed that the persistence of US economic sanctions amplified barley prices in both regimes. According to these findings, the government should eliminate interventions in the barley market by utilizing the preferential exchange rate for importing barley. Moreover, pursuing a political agenda to create a stable political condition and lift economic sanctions should be considered the priority for the government to mitigate the barley price upsurge

Resource Allocation-Based PAPR Analysis in Uplink SCMA-OFDM Systems

Sparse code multiple access (SCMA) is a non-orthogonal multiple access (NOMA) uplink solution that overloads resource elements (RE's) with more than one user. Given the success of orthogonal frequency division multiplexing (OFDM) systems, SCMA will likely be deployed as a multiple access scheme over OFDM, called an SCMA-OFDM system. One of the major challenges with OFDM systems is the high peak-to-average power ratio (PAPR) problem, which is typically studied through the PAPR statistics for a system with a large number of independently modulated sub-carriers (SCs). In the context of SCMA systems, the PAPR problem has been studied before through the SCMA codebook design for certain narrowband scenarios, applicable more for low-rate users. However, we show that for high-rate users in wideband systems, it is more meaningful to study the PAPR statistics. In this paper, we highlight some novel aspects to the PAPR statistics for SCMA-OFDM systems that is different from the vast body of existing PAPR literature in the context of traditional OFDM systems. The main difference lies in the fact that the SCs are not independently modulated in SCMA-OFDM systems. Instead, the SCMA codebook uses multi-dimensional constellations, leading to a statistical dependency between the data carrying SCs. Further, the SCMA codebook dictates that an UL user can only transmit on a subset of the available SCs. We highlight the joint effect of the two major factors that influence the PAPR statistics-the phase bias in the multi-dimensional constellation design along with the resource allocation strategy. The choice of modulation scheme and SC allocation strategy are static configuration options, thus allowing for PAPR reduction opportunities in SCMA-OFDM systems through the setting of static configuration parameters. Compared to the class of PAPR reduction techniques in the OFDM literature that rely on multiple signalling and probabilistic techniques, these gains come with no computational overhead. In this paper, we also examine these PAPR reduction techniques and their applicability to SCMA-OFDM systems