INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Dethroning GPS: Low-Power Accurate 5G Positioning Systems Using Machine Learning

Abstract: Over the last years positioning systems have become increasingly pervasive, covering most of the planet’s surface. Although they are accurate enough for a large number of uses, their precision, power consumption, and hardware requirements establish the limits for their adoption in mobile devices. In this paper, the energy consumption of a proposed deep learning-based millimeter wave positioning method is assessed, being subsequently compared to the state-of-the-art on accurate outdoor positioning systems. Requiring as low as 0.4 mJ per position fix, when compared to the most recent assisted-GPS implementations the proposed method has energy efficiency gains of $\mathbf {47\times }$ and $\mathbf {85\times }$ for continuous and sporadic position fixes (respectively), while also having slightly lower estimation errors. Therefore, the proposed method significantly reduces the energy required for precise positioning in the presence of millimeter wave networks, enabling the design of more efficient and accurate positioning-enabled mobile devices.

Efficient hybrid DCT-domain algorithm for video spatial downscaling

Abstract: A highly efficient video downscaling algorithm for any arbitrary integer scaling factor performed in a hybrid pixel transform domain is proposed. This algorithm receives the encoded DCT coefficient blocks of the input video sequence and efficiently computes the DCT coefficients of the scaled video stream. The involved steps are properly tailored so that all operations are performed using the encoding standard block structure, independently of the adopted scaling factor. As a result, the proposed algorithm offers a significant optimization of the computational cost without compromising the output video quality, by taking into account the scaling mechanism and by restricting the involved operations in order to avoid useless computations. In order to meet any system needs, an optional and possible combination of the presented algorithm with high-order AC frequency DCT coefficients discarding techniques is also proposed, providing a flexible and often required complexity scalability feature and giving rise to an adaptable tradeoff between the involved scalable computational cost and the resulting video quality and bit rate. Experimental results have shown that the proposed algorithm provides significant advantages over the usual DCT decimation approaches, both in terms of the involved computational cost, the output video quality, and the resulting bit rate. Such advantages are even more significant for scaling factors other than integer powers of 2 and may lead to quite high PSNR gains.

Using anticipation to create believable behaviour

Abstract: Although anticipation is an important part of creating believable behaviour, it has had but a secondary role in the field of life-like characters. In this paper, we show how a simple anticipatory mechanism can be used to control the behaviour of a synthetic character implemented as a software agent, without disrupting the user's suspension of disbelief. We describe the emotivector, an anticipatory mechanism coupled with a sensor, that: (1) uses the history of the sensor to anticipate the next sensor state; (2) interprets the mismatch between the prediction and the sensed value, by computing its attention grabbing potential and associating a basic qualitative sensation with the signal; (3) sends its interpretation along with the signal. When a signal from the sensor reaches the processing module of the agent, it carries recommendations such as: "you should seriously take this signal into consideration, as it is much better than we had expected" or "Just forget about this one, it is as bad as we predicted". We delineate several strategies to manage several emotivectors at once and show how one of these strategies (meta-anticipation) transparently introduces the concept of uncertainty. Finally, we describe an experiment in which an emotivector-controlled synthetic character interacts with the user in the context of a word-puzzle game and present the evaluation supporting the adequacy of our approach.

RNS Reverse Converters for Moduli Sets With Dynamic Ranges up to $(8n+1)$ -bit

Abstract: In the last years, investigation on residue number systems (RNS) has targeted parallelism and larger dynamic ranges. In this paper, we start from the moduli set {2n,2n-1,2n+1,2n-2(n+1)/2+1,2n+2(n+1)/2+1} , with an equivalent 5n -bit dynamic range, and propose horizontal and vertical extensions in order to improve the parallelism and increase the dynamic range. The vertical extensions increase the value of the power-of-2 modulus in the five-moduli set. With the horizontal extensions, new six channel sets are allowed by introducing the 2n+1+1 or 2n-1+1 moduli. This paper proposes methods to design memoryless reverse converters for the proposed moduli sets with large dynamic ranges, up to (8n+1)-bit. Due to the complexity of the reverse conversion, both the Chinese Remainder Theorem and the Mixed Radix Conversion are applied in the proposed methods to derive efficient reverse converters. Experimental results suggest that the proposed vertical extensions allow to reduce the area-delay-product up to 1.34 times in comparison with the related state-of-the-art. The horizontal extensions allow larger and more balanced moduli sets, resulting in an improvement of the RNS arithmetic computation, at the cost of lower reverse conversion performance.