Nowadays, one of the challenges of communications systems is to enhance spectrum utilization and data rate without robustness loses. This situation is more perceptive in digital television applications, since there is...
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programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential chall...
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Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Quer...
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Multipliers are essential for emerging technologies, as they are vital arithmetic circuits in many energy-efficient applications, such as digital signal processing and machine learning applications. Approximate multip...
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ISBN:
(数字)9798331522124
ISBN:
(纸本)9798331522131
Multipliers are essential for emerging technologies, as they are vital arithmetic circuits in many energy-efficient applications, such as digital signal processing and machine learning applications. Approximate multipliers (AxM) became an optimal option for applications in ASIC systems with error tolerance. This paper proposes to develop a run-time reconfigurable AxM with four approximation levels in a single circuit, evaluating accuracy and circuit metrics (e.g., circuit area, timing, and power consumption) based on the leading one-bit-based approximate (LoBA) multiplier. The results achieve a circuit area reduction of 52% less area and up to 27% less power consumption when compared with equivalent architecture based on LoBA state-of-art. Applying the proposed RLoBA to a normalized least mean square (NLMS) adaptive filter case study, we obtain 22.95% less power consumption using dynamic approximate level selection than the precise multiplier while maintaining the same accuracy level.
This paper presents a novel architecture for an approximate multiplier (AxM) based on Leading One-Bit Approximation (LoBA), aimed at enhancing error-resilient applications through a quality-configurable multipliers (Q...
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ISBN:
(数字)9798350356830
ISBN:
(纸本)9798350356847
This paper presents a novel architecture for an approximate multiplier (AxM) based on Leading One-Bit Approximation (LoBA), aimed at enhancing error-resilient applications through a quality-configurable multipliers (QCMs) approach. The proposed DR-LoBA design is a statically and dynamically reconfigurable LoBA multiplier that offers flexibility and adaptability to varying application requirements. Featuring 16 approximation levels, it achieves significant power savings of 13.2% to 72% compared to precise multipliers with the same bit-width when tested with random inputs. On average, DR-LoBA delivers 27% greater precision when compared to state-of-art truncation-based reconfigurable multiplier across all precision levels. When performed in an actual application using the Filtered-x Least Mean Square (FXLMS) filter in active noise cancellation, our DR-LoBA multiplier reduces power consumption by 7.6% to 42.7% by varying the precision during the process while maintaining a noise reduction level of just 0.11 to 1.94dB lower than the full-precision system.
Approximate computing attempts to maximize area and energy savings for a trade-off between quality and efficiency. This paper investigates the approximate squarer and divider units in the calibration procedure for rad...
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Approximate computing attempts to maximize area and energy savings for a trade-off between quality and efficiency. This paper investigates the approximate squarer and divider units in the calibration procedure for radio astronomy called StEFCal (Statistically Efficient and Fast Calibration). The StEFCal circuit uses two squarer units from the literature, i.e., radix-4 (AxRSU) and SquASH, and iterative-based Newton-Raphson (NR) and Goldschmid (GLD) dividers. We demonstrate the efficiency of StEFCal using the approximate arithmetic operators from the Pareto-optimal that show the area- and power-quality trade-off. The results show that using the AxRSU combined with the NR divider improves the Mean Square Error (MSE) by 4.24%, with 58x more energy saving than the state-of-the-art.
This paper presents MULTISTYLE, a multi-agent centralized heuristic search planner that incorporates distinct agent playstyles to generate solution plans where characters express individual preferences while cooperati...
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The learning game “Present of the Future” has shown great potential as an effective tool for promoting futures literacy and speculative design in various educational and professional contexts. Through interactive an...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
The learning game “Present of the Future” has shown great potential as an effective tool for promoting futures literacy and speculative design in various educational and professional contexts. Through interactive and collaborative scenarios, participants are encouraged to explore future possibilities and develop creative solutions to contemporary challenges. Despite its promising impact, the game still lacks a structured and systematic process for evaluating the user experience on three levels: the immediate reaction to the game experience, the retention of learning in the short and long term, and collaboration in creating future artifacts. We propose a detailed experiment and an evaluative questionnaire composed of open and closed questions to fill this gap and provide a foundation for critically analyzing these dimensions. Although the experiment has not yet been conducted, the research establishes a robust theoretical and methodological framework that will allow, in future stages, the collection and analysis of quantitative and qualitative data to complement and enrich this study. Thus, we expect that the future application of these practices will contribute to a deeper understanding of the pedagogical and collaborative effects of the game.
With the advancement of Artificial Intelligence (AI) technologies, the world has been increasingly reshaped across multiple domains - healthcare, finance, education, and creative fields. This expansion transforms indi...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
With the advancement of Artificial Intelligence (AI) technologies, the world has been increasingly reshaped across multiple domains - healthcare, finance, education, and creative fields. This expansion transforms individual tasks and facilitates complex collaborations where humans and machines work to- gether in unprecedented ways. Through machine learning and, more specifically, large language models (LLMs), AI now plays a crucial role in enhancing productivity. Recent technological advancements have significantly boosted the text generation capabilities of LLMs, enabling them to simulate personas that can represent groups or specific well-known individuals. By simulating expert perspectives, LLMs provide insights to guide human decision-making across various collaborative frameworks in Computer-Supported Cooperative Work (CSCW). Personas, as simulated identities, have proven helpful in group brainstorming sessions, consensus-building exercises, and virtual advisory boards, which help represent diverse viewpoints and facilitate group decisions. Among these CSCW methods, the Delphi method is a well-established approach for reaching consensus among experts through iterative feedback and structured discussions. This work introduces a framework to manage and evaluate LLM-generated personas simulating expert input in Delphi studies. Our contributions include a framework designed for evaluating LLM personas in Delphi studies using a text similarity validation technique comparing real and simulated expert opinions and an evaluation of a Delphi about Brazilian higher education, assessing the LLMs' reliability in real-world scenarios. Findings show LLMs can effectively replicate expert perspectives, enhancing AI integration in specialized decision-making.
In an effort to continue to help provide various and thriving experiences to engineering undergraduates and help increase retention, a mid-size university uses a high impact practice of using peer teachers in the clas...
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