Abstract

This paper focuses on optimal beamforming to maximize the mean signal-to-noise ratio (SNR) for a reconfigurable intelligent surface (RIS)-aided MISO downlink system under correlated Rician fading. The beamforming problem becomes non-convex because of the unit modulus constraint of passive RIS elements. To tackle this, we propose a semidefinite relaxation-based iterative algorithm for obtaining statistically optimal transmit beamforming vector and RIS-phase shift matrix. Further, we analyze the outage probability (OP) and ergodic capacity (EC) to measure the performance of the proposed beamforming scheme. Just like the existing works, the OP and EC evaluations rely on the numerical computation of the iterative algorithm, which does not clearly reveal the functional dependence of system performance on key parameters. Therefore, we derive closed-form expressions for the optimal beamforming vector

Abstract

The direction of arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper we propose two trajectory models, namely the polynomial and bandlimited trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, we adopt two gridless algorithms: i) Sliding Frank-Wolfe (SFW), which solves the Beurling LASSO problem and ii) Newtonized Orthogonal Matching Pursuit (NOMP), which improves over OMP using cyclic refinement. Furthermore, we extend our analysis to include wideband processing. The simulation results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency.

Abstract

Carbon monoxide (CO) and Nitrogen dioxide (NO 2 ) are major air pollutants that have the potential to affect human health adversely. There is a lack of useful information regarding the spatial distribution and temporal variability of CO and NO 2 emissions in major metropolitan areas. The primary goal of this research is to provide a geospatial data methodology for detecting emission spikes of CO and NO 2 in polluted urban environments employing portable low-cost sensors. We propose that ephemeral identification of harmful gas concentrations can be achieved using different IoT device types mounted on a mobile platform. We propose that persistent identification of the CO and NO 2 emission spikes can be attained by driving through the city on different days. We applied this approach to Hyderabad, India, by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating

Abstract

This paper presents a custom design and analysis of a CMOS-compatible, color-segregating photodiode design for Lab-on-a-Chip applications. The design is based on minimal post-processing of a CMOS photodiode circuit to achieve improved segregation of red, green, and blue wavelengths. Lower wavelengths are blocked by depositing blocking materials (polysilicon, silicon nitride) of different thicknesses. Diodes compatible with the process are used for collecting the electron-hole pairs. We have used the 180 nm TSMC process for our design and analysis, but it is applicable to other processes as well.

Abstract

A proportional to-absolute-temperature (PTAT) voltage generating circuit connected between a power supply voltage source and a ground for providing a PTAT voltage at an output terminal of the PTAT voltage generating circuit to act as a temperature sensor is provided. The PTAT voltage generating circuit includes a plurality of PMOS transistors. The plurality of PMOS transistors generates a second PTAT voltage by multiplying a first PTAT voltage by a factor equal to a ratio of a first equivalent resistance (R2) and a second resistance (R1) of a first PMOS transistor (M4). The first equivalent resistance (R2) is obtained from a series combination of the plurality of PMOS transistors. The first PTAT voltage is generated by determining a difference between a base-emitter voltage of a first PNP transistor (T1) and the second PNP transistor

Abstract

In this work, the role of gold nanoparticles in enhancing the sensitivity of Radio Frequency based biomolecule detection is assessed. A facile, efficient and rapid microstrip patch antenna based sensor, resonating at 61.747 GHz is utilized for the analysis. Gold nanoparticles are placed in the vicinity of biomolecules, and the extent of improvement is studied under various conditions. In particular, different morphological variations and arrangements of gold particles are investigated. The results indicate that, in general, the presence of gold nanoparticles contributes towards sensitivity improvement. However, the amount of enhancement strongly depends upon the morphological features and arrangements of gold particles and varies within 5-65% for the scenarios studied in this case.

Abstract

A solvent-specific novel Integrated Biosensing Network Analyser (IBNA) for the detection of materials with varying dielectric constant (i.e., permittivity) is reported in this paper. The proposed IBNA works on the concept that any change in permittivity of the Material Under Test (MUT) results in a change in capacitance of the RF Bio Liquid cavity’s resonant frequency measured by the Measurement Unit. An interdigitated capacitor-based RF bioliquid cavity resonating at 2.3 GHz performs the sensing operation, while a Dual Band Six-Port Reflectometer (DBSPR) with an operating frequency of 1.1 GHz and 2.3 GHz facilitates the measurement of the s-parameters and hence the extraction of permittivities. The DBSPR operating at 1.2 GHz and 2.3 GHz required development of a dual-band coupler which exhibits |S 11 | of -30 dB and -25 dB and bandwidth of 573 MHz and 644 MHz at the chosen design frequencies. It also

Abstract

This paper presents a real-time algorithm for computing the optimal sequence and motion plans for a fixed-base manipulator to pick and place a set of given objects. The optimality is defined in terms of the total execution time of the sequence or its proxy, the arc-length in the joint-space. The fundamental complexity stems from the fact that the optimality metric depends on the joint motion, but the task specification is in the end-effector space. Moreover, mapping between a pair of end-effector positions to the shortest arc-length joint trajectory is not analytic; instead, it entails solving a complex trajectory optimization problem. Existing works ignore this complex mapping and use the Euclidean distance in the end-effector space to compute the sequence. In this paper, we overcome the reliance on the Euclidean distance heuristic by introducing a novel data-driven technique to estimate the optimal arc-length cost in joint …

Abstract

Recent advances and applications of language technology and artificial intelligence have enabled much success across multiple domains like law, medical and mental health. AI-based Language Models, like Judgement Prediction, have recently been proposed for the legal sector. However, these models are strife with encoded social biases picked up from the training data. While bias and fairness have been studied across NLP, most studies primarily locate themselves within a Western context. In this work, we present an initial investigation of fairness from the Indian perspective in the legal domain. We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents. We evaluate the fairness gap using demographic parity and show that a decision tree model trained for the bail prediction task has an overall fairness disparity of 0.237 between input features associated with Hindus and Muslims. Additionally, we highlight the need for further research and studies in the avenues of fairness/bias in applying AI in the legal sector with a specific focus on the Indian context.

Abstract

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author’s post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit “r/AmItheAsshole” as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach