Geometry-aware online motion retargeting is crucial for real-time character animation in gaming and virtual reality. However, existing methods often rely on complex optimization procedures or deep neural networks, which constrain their applicability in real-time scenarios. Moreover, they offer limited control over fine-grained motion details involved in character interactions, resulting in less realistic outcomes. To overcome these limitations, we propose a novel optimization framework for ultrafast, lightweight motion retargeting with joint-level control (i.e., controls over joint position, bone orientation, etc,). Our approach introduces a semantic-aware objective grounded in a spherical geometry representation, coupled with a bone-length-preserving algorithm that iteratively solves this objective. This formulation preserves spatial relationships among spheres, thereby maintaining motion semantics, mitigating interpenetration, and ensuring contact. It is lightweight and computationally efficient, making it particularly suitable for time-critical real-time deployment scenarios. Additionally, we incorporate a heuristic optimization strategy that enables rapid convergence and precise joint-level control. We evaluate our method against state-of-the-art approaches on the Mixamo dataset, and experimental results demonstrate that it achieves comparable performance while delivering an order-of-magnitude speedup.

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality.

This article presents a constant voltage control scheme to improve the transfer efficiency and output voltage accuracy for single-stage AC-DC converters. The proposed scheme also has the advantage of low cost because of the simple application and few peripheral devices. To realize above features, the peak current and frequency curves with high efficiency are selected by analyzing the power losses of the applied topology. A voltage self-compensation circuit is designed to improve the output accuracy, especially the output voltage below 5V. Furthermore, a multi-stage startup circuit is also proposed to reduce the output voltage overshoot. To verify the feasibility of the proposed constant voltage control scheme, a BUCK controller adopting the proposed control scheme has been designed and fabricated with$0.18~\mu \text{m}$BCD process. Using the proposed controller, the peak efficiency of the prototype can reach as high as 66.9%, 78.9% and 82.8% under 115Vac input and 3.3V@300mA, 5V@300mA and 12V@300mA respectively. The load regulation is counted to be within ±1%.

In recent years, micro-nano device characteristics like ferroelectrics and resistive switching are being used to build important security primitives such as Physical Unclonable Function (PUF). The micro-nano device-based hardware security primitives, although with higher security, energy efficiency, and integration density, suffer from serious reliability issues caused by process scaling. To mitigate this issue, this paper introduces a reconfigurable weak PUF based on spin-transfer torque magnetoresistive random-access memory (STT-MRAM), which adopts the crossing switches implemented with simple demultiplexes (DEMUXs) to improve the flexibility and reliability. Moreover, two algorithms,neighboring bit linesandtop-$n$n, are proposed to enlarge the gap between two parallel reading currents, thus further enhancing the reliability of PUF responses. Experimental results demonstrate that the proposed PUF scheme achieves good uniqueness (50.64 percent), uniformity (50.02 percent), and bit-aliasing ($\approx$49.80%). Particularly, the proposed method significantly improves the PUF reliability, achieving low bit error rate (BER$\leq$2.13%) within the range of -20$^\circ$C to 90$^\circ$C.

Physical unclonable functions (PUFs) have been adopted in many resource-constrained Internet of Things (IoT) applications to provide effective and lightweight solutions for device authentication. However, an attacker can collect challenge–response pairs (CRPs) of a strong PUF, to build a machine learning (ML) model and mimic its behavior, i.e., predicting the responses of unseen challenges with high accuracy. Although several PUFs have been proposed to resist such modeling attacks, they incur high hardware overhead. Developing a PUF primitive with low hardware cost and high resistance to ML attacks is thus a crucial task. In this article, we propose the first response–feedback-based lightweight anti-ML-attack PUF (FLAM-PUF). It is only composed of one arbiter PUF (APUF) and one Galois linear-feedback shift register (LFSR), with some basic logic gates, reducing more than 62% hardware cost compared with the state-of-the-art robust strong PUFs. Specifically, FLAM-PUF leverages a cost-effective feedback loop structure to dynamically control and update the LFSR configuration. FLAM-PUF has two main characteristics: 1) it feeds back a 1-bit response in every cycle to intentionally poison the data of the CRP set for training. To resist ML-based modeling attacks, the 1-bit response can randomly update one coefficient of the feedback polynomial to implant more complex correlations into the model built by attackers and 2) it takes advantage of an$n-$bit response feedback-controlled reconfigurable Galois LFSR to enlarge the original challenge space of the APUF. Extensive experimental results show that the proposed FLAM-PUF achieves near-optimal uniformity, uniqueness, and reliability. Our scheme works well under standard attack models with public crucial initial information. In particular, the prediction accuracy of modeling attacks against FLAM-PUF is nearly 50% under the four widely used ML algorithms, i.e., support vector machines (SVMs), logistic regression (LR), covariance matrix adaptation evolution strategy (CMA-ES), and deep neural networks (DNNs), indicating excellent resistance against these ML attacks.

In the existing V-BLAST system with adaptive modulation, spectral efficiency decreases dramatically at lower SNR due to the invalid subchannnel which does not meet the target BER. To resolve this problem, a power reallocation algorithm is proposed in this paper. The post-detection SNR of the valid subchannel can be improved by reallocating the power quotas of the invalid channel to valid ones. Simulation results show that by means of the proposed method the spectral efficiency increases obviously, especially when the SNR is low.

An improved second-order power control (ISOPC) algorithm for code division multiple access (CDMA) systems is proposed in order to improve the convergence speed in distributed carrier-to-interference ratio balance algorithm. By adopting soft optimum coefficient, the ISOPC algorithm uses current and previous power levels for power updates. Convergence analysis is performed and the condition of ISOPC convergence is provided for feasible systems. The simulation indicates that when soft optimum coefficient equal to 1.2, the ISOPC algorithm has faster convergence speed compared to the second-order power control (SOPC) algorithm.