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This research centers on the application of the discrete-time Taylor–Fourier transform (DTTFT) algorithmic implementation for phasor estimation on a field-programmable gate array board. The system employs a finite impulse response structure of a digital Taylor–Fourier filter to extract amplitude and phase information. The hardware description utilizes a multiply accumulator architecture with only forty embedded 9-bit multiplier elements, achieving an 18-bit input–output resolution. Performance assessment involves signal analysis through FPGA-in-the-loop simulation in MATLAB/Simulink. Findings demonstrate that the DTTFT-based phasor estimator can be effectively characterized using VHDL code and implemented on an Intel D2-115 board. [ABSTRACT FROM AUTHOR]

In coherent optics transmission systems, the digital signal processor (DSP) application-specific integrated circuit (ASIC) is the most power-hungry part of the optical transceiver. Already in the edge of transistor technology, to achieve the power budget, we must look for opportunities to further optimize the designs. This letter explores a dynamic duty cycle for reducing the consumption in the pipeline of such DSP ASICs. We exploit the characteristics of estimator algorithms to introduce a dynamic duty cycle, reducing the mean consumption of designs originally constrained only for worst-case scenarios. We present the methodology to implement duty cycle control using the carrier frequency offset estimator (CFE) algorithm as case study, achieving in simulation level from 22% to 74% power consumption reduction in this algorithm, varying on-chip operation conditions. [ABSTRACT FROM AUTHOR]

Noisy intermediate-scale quantum (NISQ) computers face significant reliability challenges because they are vulnerable to quantum noise, which severely limits their fidelity in quantum applications. In particular, deep circuits with a large number of quantum gates are susceptible to errors as they are more likely to lose their states in a deep circuit. In this paper, we propose a quantum circuit for square root operation that generates correct results on NISQ devices. The square root operation is used in many applications such as complex number computations, computer graphics, etc. While there have been limited studies on quantum square root circuits, none of them can be implemented on NISQ devices. In this work, we simplify the structure of an exact quantum square root circuit and reduce the number of quantum gates. The proposed design reduces the complexity of the square root circuit while maintaining the same level of precision in outputs. In addition, we exploit approximate computing to simplify the circuit further and run it on a real quantum computer. Approximate computing is used in classical computers to enhance the power and/or performance in exchange for accuracy. We exploit approximate computing for a different purpose, which is reducing the depth as well as the number of quantum gates in the square root circuit. To validate the effectiveness of our approach, we conduct experiments on an IBM quantum computer, where our circuit produces meaningful results. Furthermore, we present examples of error-resilient applications to demonstrate the validity of our approximate circuit.

The growing number of data privacy breaches and associated financial losses have driven the demand for private database queries. Clients typically submit queries that involve both search and computation operations, such as counting students under a certain age or calculating the BMI of employees above a specific age. Existing protocols often face limitations due to reliance on specific-purpose encryption schemes or multiple communication rounds between clients and servers. In this work, we present a unified framework utilizing fully homomorphic encryption techniques to efficiently and privately process queries with search and computation operations. Our contributions include a homomorphic encryption-based private comparison algorithm, called the layered comparison algorithm, which achieves a 2.6-6.6X performance improvement compared to algorithms from prior work; a fast Fourier transform-based preprocessing method enabling accurate large integer arithmetic operations in the encrypted domain; and a scalable database encoding method. Evaluation results demonstrate the practicality of our system, as it processes an aggregated query for a 1k-row encrypted database in approximately 4.53 seconds. [ABSTRACT FROM AUTHOR]

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. [ABSTRACT FROM AUTHOR]