Indian Institute of Information Technology Guwahati

Selective chemical fixation of CO2 represents a promising approach to mitigate escalating atmospheric CO2 concentration and synthesizing essential commodity chemicals to boost the circular economy. The cycloaddition of CO2 for producing α‐alkylidene cyclic carbonates is an attractive research topic as it uses cheap CO2 as C1 feedstock in a 100% atom economy reaction. Hence, we designed an amine‐functionalized, nitrogen‐rich COF (SNW‐1@NH2) to strongly graft Cu(I) ions within its pores. The synergistic effect of both SNW‐1@NH2 and Cu(I) ion boosted the catalytic activity towards precious metal‐free CO2 cycloaddition with propargylic alcohol to produce α‐alkylidene cyclic carbonate at room temperature. Furthermore, broad substrate scope and high recyclability of the catalyst with retention of catalytic activity and structural integrity were observed. Contrarily, SNW‐1@NH2 was further employed for catalytic Knoevenagel condensation reaction between benzaldehyde and cyanoacetamide. SNW‐1@NH2 COF yielded desired products in Knoevenagel condensation with broad substrate scope and high recyclability without affecting its structural integrity and functionality. Additionally, deep mechanistic investigations were executed to describe plausible catalytic pathways for both catalysis reactions. We hope that this investigation towards developing nitrogen‐rich porous soft materials and expanding its applicability can offer alternative solutions to multiple severe environmental concerns.

Toom-Cook multiplication algorithm is one of the most efficient method compared to other traditional large-integer multiplication algorithms. However, in most cases, implementation of this algorithm in hardware is practically avoided due to presence of exact-divisions at intermediate steps of computation. Thus this paper proposes ways to overcome the limitation by designing a hardware-optimized, division-free Toom-4 multiplication algorithm. This is done by eliminating the effects of division due to odd factors in the denominator using a scaling factor, at the interpolation step of the algorithm. Further optimization is done by replacing the direct point-wise multiplication with an optimized Schoolbook multiplication. The overall performance of the proposed architecture is estimated using Area-Time-Product (ATP) and hardware implementation is done using Virtex-7 FPGA device in Xilinx-ISE platform. Practically, the proposed design performs better in terms of speed and resource utilization for higher input bits compared to other state-of-the-art designs; for example for 512 input bits, the percentage ATP of the proposed design is 75.077%, 98.822% and 57.316% superior performance compared to Traditional Schoolbook Multiplication, Karatsuba (Toom-2) multiplication and Toom-4 multiplication respectively.

In this paper, we deduce a summation transformation formula for Gaussian hypergeometric series. Using this, we express certain twisted Kloosterman sheaf sums in terms of special values of Gaussian hypergeometric series. This generalizes a result of Dummit et al. related to a conjecture of Evans.

A combination of a thulium-doped fiber amplifier (TDFA) and an erbium-doped fiber amplifier (EDFA) in a sequential configuration has been identified to design a hybrid amplifier. The proposed amplifier module delivers a flat and high gain. This method is potentially an effective strategy to fully utilize the S + C band (1,460 nm–1,565 nm) for amplifying fiber optic signals. The proposed TDF-EDF hybrid amplifier has an optimum average flat-band gain of 24.27 dB from 1,460 nm to 1,516 nm with a maximum gain of 47.07 dB at 1,528 nm. The parametric optimization has been performed considering the optical pumping configuration with corresponding power, input-signal power, amplifier length, ion density with its radius, and the hybrid amplifier’s numerical aperture (NA). The nonlinear clustering-ion effects known as the ion-ion interaction mechanism (IM) have been aimed to distort the signal gain with broadband flat-gain profile minimally. Apparently, the optimized parameter reduced the gain deterioration by the IM effect to 1.12 % with homogeneous up-conversion (HUC) and 8.14 % with the combined effect of HUC and pair-induced quenching (PIQ) corresponding to the maximum gain. In the flat-band region, the HUC and HUC + PIQ combination have the respective gain deteriorating contributions of 0.67 % and 5.24 %. For the input signal power of −40 dBm, the gain flatness of the hybrid amplifier is obtained as 3.27 dB for 1,460 nm–1,516 nm regions without IM effects. However, with HUC and HUC + PIQ, gain flatness has been reduced to 3.08 dB and 2.19 dB, respectively. Furthermore, the gain variation ratio (GVR) of 0.05 (preferably GVR < ${< }$ 0.4) is obtained from 1,472 nm to 1,504 nm wavelength region indicating a flatter gain response.

Content addressable memory (CAM) is essential for applications that demand rapid search capabilities. Its ability to perform high-speed search within a single clock cycle requires CAM cell to incorporate an additional comparison unit, which contributes approximately 30%- 40% more transistors than storage unit. As a result, the CAM arrays occupy more space than traditional memory systems such as static random access memory (SRAM), and the enhanced performance comes at a significant increase in power consumption. We propose a CAM cell design that utilizes one of the access transistors as a half-side comparator, allowing for a single bit-line (BL) configuration instead of complementary BLs. This innovative approach not only decreases the number of transistors and the memory core area but also cuts down half of the BL switching activities, leading to a more efficient CAM design. Based on 180-nm 1.8-V CMOS technology, our 64×32-bit CAM utilizing the single bit-line CAM cell (SBCC) achieves 59% better energy-delay performance compared to a conventional design and 10% better than a previous full swing cell based CAM design, while using 1.25 × fewer transistors.

This article explores vertex-transitive maps on the torus, with a focus on polyhedral maps, which are embeddings of graphs on surfaces where face intersections are limited to vertices or edges. A map is said to be semi-equivelar if, up to cyclic permutations and reversed order, any two face-cycles are equivalent. There are eleven types of semi-equivelar maps on the torus. A map is termed vertex-transitive if its automorphism group acts transitively on the vertex set. This study builds on previous works that classified and enumerated various types of equivelar and semi-equivelar maps on the torus. Among these, only four types are vertex-transitive, while for the remaining seven types, both vertex-transitive and non-vertex-transitive examples are known. We extend the understanding of semi-equivelar maps by providing a detailed enumeration of vertex-transitive maps of specific types, namely $[3^2, 4^1, 3^1, 4^1], [4^1, 8^2], [3^1, 6^1, 3^1, 6^1], [3^1, 12^2], [3^1, 4^1, 6^1, 4^1], [4^1, 6^1, 12^1],$ and $[3^4, 6^1]$. We determine the conditions under which vertex-transitive maps exist for any given number of vertices. The results show that for each type, there exist integer values of n (the number of vertices) such that vertex-transitive maps can be constructed. This work provides a comprehensive classification of both vertex-transitive and non-vertex-transitive semi-equivelar maps on the torus, contributing significantly to the ongoing study of polyhedral map classification and symmetry on surfaces.

Indoor localization methods based on the Wi-Fireceived signal strength indicator (RSSI) ranging technology are sensitive to noise fluctuations and signal attenuations, which could lead to a significant localization error. Therefore, this study proposes an improved indoor localization algorithm using a deep randomized neural network (RandNN) with Wi-Fi-RSSI. We have conducted a real experiment testbed for Wi-Fi RSSI data collection from a complex indoor environment. An improved adaptive unscented Kalman filter (IAUKF) method is used to minimize noise fluctuations and signal attenuations in the raw Wi-Fi RSSI data collection. Moreover, we have investigated the deep RandNN in which the weights and biases of the input hyperparameter are initially randomized to obtain the best localization performance. For convenience, the presented localization model is known as RandNN-IAUKF. Furthermore, real experiments were conducted in a room surrounding working stations, walls, patriation separations, etc., to maximize the complexity of wireless signal propagation. The performance of the presented RandNN-IAUKF algorithm is assessed and compared with other well-known conventional localization approaches. Overall, the experimental results showed that the presented RandNN-IAUKF algorithm provides significant 95% and 67% location estimation errors only at 0.79 m and 1.31 m, respectively, outperforming conventional algorithms by approximately 30% under the same test environment

The current study incorporates a modified version of Kouri's (1983) portfolio balance rational expectations model to determine and forecast the bilateral exchange rate between India and the US for the period 1996:Q2-2019:Q3. The assumption of rational expectations enables us to analyse the factors representing current and capital account as macroeconomic determinants of exchange rates. The most significant contribution of the current study is however the inclusion of the microstructure theory within Kouri's (1983) framework, which permits us to determine the role of micro factors and macro factors, in influencing exchange rate. The use of the novel econometric tool, the Nonlinear Auto-Regressive Distributed Lag (NARDL) model, combined with various post-estimation tests, allows us to conclude in favour of asymmetric relationship between some of the exogenous variables and the exchange rate, with both micro and macro factors being important determinants of the exchange rate in the short-run. While evaluating the forecasting accuracy of the modified Kouri's (1983) model, we compare it to the Random Walk Model (RWM) across three forecast horizons: 6-months, 1-year, and 2-years. The results show that the modified Kouri (1983) model outperforms the RWM for all the forecast horizons considered.

This paper offers a new approach to validation for IP address reputation (IPR) using dynamic signatures achieved through the application of machine learning (ML) to conduct an accurate detection of malicious IPs. The proposed solution connects to the Amazon Web Services (AWS) Web Application Firewall (WAF) to improve security by deploying artificial intelligence (AI) models and natural language (NL) processing for automation of information extraction from IP-related information saved in the AWS object storages. All these lead to minimization of the number of false positives, real-time feedback, and optimization of IP reputation checking. The experimental outcome proves the capability of the proposed system to mitigate the IP address of the attacker and improve the protection of web applications against cybercrimes. This new approach is much advanced and a major leap ahead of other conventional IP reputation techniques, which offer a good protection shield against spam, DDoS, and other such evils.

The current study conducts a comparative analysis of Portfolio-Balance Models (PBM) developed by Branson, Kouri and Dornbusch to assess the role of expectations and time horizons in determining and forecasting the India–US exchange rate over the period 1996:Q2–2019:Q3. Notably, it improves the original models by integrating microstructure theory into their framework. The Autoregressive Distributed Lag Error-Correction Model (ARDL-ECM) is used to investigate both short run and long run behaviour of the models. Additionally, the study assesses out-of-sample forecasting accuracy of the modified models against the Random Walk Model (RWM) using the root mean square error metric. The estimation results reveal that models based on rational expectations are better than the static expectations model. Notably, the microeconomic determinant is counterintuitively significant only in the long run across all models. Furthermore, these modified models demonstrate superior out-of-sample forecasting abilities compared to RWM for alternative forecasting horizons. However, forecasting results over a 6-month period is better with short run models. Over 1-year and 2-year horizons, rational expectations models outperform the static expectations model. This study challenges the Meese–Rogoff puzzle, ensuring that PBM, when modified to incorporate microstructure theory, is valid and yields superior forecasting results compared to RWM. JEL Codes: F31, F32, C22

In this paper, we consider the parametric inference for the family of inverted exponentiated distributions under a joint adaptive progressive Type-II censoring scheme. The problem of estimation is considered for this family with common scale and different shape parameters. We obtain maximum likelihood estimators of unknown model parameters. In sequel asymptotic intervals are also constructed. Further, Bayes estimators are derived under squared error loss function and corresponding credible intervals are obtained as well. To support the findings, we perform simulation studies and analyze a real data set to demonstrate the effectiveness of proposed estimation methods.

In this work, we investigate a full-duplex network assisted by an intelligent reflecting surface (IRS) involving both downlink (DL) and uplink (UL) users in the same frequency band with best user selection. The IRS is partitioned into two zones to cater to each of DL and UL users. Our analysis incorporates factors such as residual self-interference, co-channel interference, and hardware imperfections, considering a direct link over generalized Nakagami-m fading channels. We derive analytical expressions for outage probability (OP), system outage probability (SOP), and ergodic rate. We also investigate the joint allocation of element splitting between the zones, user power allocations, and reflection amplitude with SOP minimization and ergodic sum rate maximization as objectives. Extensive Monte-Carlo simulations are conducted to validate the analytical expressions.