A mixer-first receiver frontend with resistive-feedback baseband is proposed. The baseband combination of G m and transimpedance amplifier (TIA) is designed to cover a wide frequency range for high data rate applications. The N-path filtering at the RF side and enhanced filtering at the BB side inhibit out-of-band blocker interferences. The receiver is fabricated in a 65 nm CMOS. Measurement results display an NF of 2.3 dB and a conversion gain of 33.5 dB at 2 GHz f LO . The in-band and out-of-band IIP3 are −7.5 dBm and 19 dBm respectively. The receiver core draws 34 mW in the signal path and occupies an active area of 0.31 mm ² .

Group activity analysis has attracted remarkable attention recently due to the widespread applications in security, entertainment and military. This paper targets at learning group activity representations with self-supervision, which differs from the majorities relying heavily on manually annotated labels. Moreover, existing Self-Supervised Learning (SSL) methods for videos are sub-optimal to generate such representations because of the complex context dynamics in group activities. In this paper, an end-to-end framework termed Contextualized Relation Predictive Model (Con-RPM) is proposed for self-supervised group activity representation learning with predictive coding. It involves the Serial-Parallel Transformer Encoder (SPTrans-Encoder) to model the context of spatial interactions and temporal variations, and the Hybrid Context Transformer Decoder (HConTrans-Decoder) to predict the future spatio-temporal relations guided by holistic scene context. Additionally, to improve the discriminability and consistency of prediction, we introduce a united loss integrating group-wise and person-wise contrastive losses in frame-level as well as the adversarial loss in global sequence-level. Consequently, our Con-RPM learns robust group representations via describing temporal evolutions of individual relationships and scene semantics explicitly. Extensive experimental results on downstream tasks indicate the effectiveness and generalization of our model in self-supervised learning, and present state-of-the-art performance on the Volleyball, Collective Activity, VolleyTactic, and Choi's New datasets.

The research goal of this paper is to learn the group activity representations in a self-supervised fashion instead of through the use of conventional methods that rely on manually annotated labels. It is essential for this task to better describe the complex group states and their future transitions. To this end, we propose a long-short state predictive Transformer (LSSPT), which mines the meaningful spatiotemporal features of group activities by predicting the future group states with long- and short-term historical state dynamics. LSSPT consists of an encoder that models diverse spatiotemporal state representations in the observation, together with a decoder that exploits rich dynamic patterns by attending to both the short-term spatial context and long-term history state evolutions to predict future group states. Furthermore, we consider the distinguishability and consistency of the predicted states and introduce a joint learning mechanism to optimize the models, enabling LSSPT to describe more reliable state transitions. Finally, extensive experiments are carried out to evaluate the learned representation on downstream tasks on the Volleyball, Collective Activity and VolleyTactic datasets, which showcases the method’s state-of-the-art performance over the existing self-supervised learning approaches.

Periocular recognition is regarded as an alternative trait for biometric recognition that can effectively solve the identification problem under large occlusions. However, few datasets are tailored for periocular recognition. For most compromises, iris datasets at near-infrared wavelengths, miss information about the eyebrows or eyelids. In this paper, a challenging dataset for real scenarios named CASIA-PR-V1 with evaluation protocols is released for periocular recognition. It is collected from multiple types of mobile devices with different resolutions or wavelengths. A rich set of attributes, e.g., ethnicities, is tagged to support fine-grained classification tasks. Moreover, we consider a wide range of noisy data in unconstrained environment, especially for glasses. Superior to its counterparts, this periocular dataset is highly valuable for studying cross-device and cross-spectral periocular recognition with occlusions, as well as fine-grained attribute classification. Additionally, a multiscale disentangled model is proposed to extract discriminating representations for periocular recognition with severe occlusions. Extensive experiments are conducted on CASIA-PR-V1, and the results indicate the superiority of our model for unconstraint periocular recognition. Please visit http://biometrics.idealtest.org for more details about our dataset.