Moment quantization of inhomogeneous spin ensembles

Robust excitation of a large spin ensemble is a long-standing problem in the field of quantum information science and engineering and presents a grand challenge in quantum control. A formal theoretical treatment of this task is to formulate it as an ensemble control problem defined on an infinite-dimensional space. In this paper, we present a distinct perspective to understand and control quantum ensemble systems. Instead of directly analyzing spin ensemble systems defined on a Hilbert space, we transform them to a space where the systems have reduced dimensions with distinctive network structures through the introduction of moment representations. In particular, we illustrate the idea of moment quantization for a spin ensemble and illuminate how this technique leads to a dynamically equivalent control system of moments. This equivalence enables the control of spin ensembles through the control of their moment systems, which in turn creates a new control analysis and design paradigm for quantum ensemble systems based on the use of truncated moment systems.     

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature.     

Difference angle quantization index modulation scheme for image watermarking

In this paper, we propose a new angle quantization index modulation (AQIM) method, called the difference AQIM (DAQIM) method. The proposed method aims to improve the watermarking performance against gain attacks. Unlike the original AQIM method (Ourique et al., Angle QIM: a novel watermark embedding scheme robust against amplitude scaling distortions, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, 2005, pp. 797–800), the DAQIM method quantizes the difference of the two angles instead of the angles themselves. The main advantage of the DAQIM method is to disperse the interference to the watermark signal from one angle to more angles. Thus, the watermark has a higher ability to resist attacks. We perform a theoretical analysis of the document-to-watermark ratio (DWR) based on our proposed method. We show that our proposed method can obtain a lower embedding distortion compared to the AQIM and the gradient direction watermarking (GDWM) (Nezhadarya et al., IEEE Trans. Inf. Forensics Secur., 6(4), 2011, 1200–1213), methods under the same robustness and payload conditions. The experimental results demonstrate that our proposed method outperforms common existing methods in terms of the robustness against various attacks such as the JPEG quantization noise, additive white Gaussian noise (AWGN), cropping effect and mean filtering.     

STAR-Lite: A light-weight scalable self-taught learning framework for older adults’ activity recognition

Recognizing activities for older adults is challenging as we observe a variety of activity patterns caused due to aging (e.g., limited dexterity, limb control, slower response time) or/and underlying health conditions (e.g., dementia). However, existing literature with deep learning methods has successfully recognized activities when the dataset contains high-quality annotations and is captured in a controlled environment. On the contrary, data captured in a real-world environment, especially with older adults exhibiting memory-related symptoms, varying psychological and mental health status, reliance on caregivers to perform daily activities, and unavailability of domain-specific annotators, makes obtaining quality data with annotations challenging; leaving us with limited labeled data and abundant unlabeled data. In this paper, we hypothesize that projecting the labeled data representations comprising a specific set of activities onto a new representation space characterized by the unlabeled data comprising activities beyond the limited activities in the labeled dataset would help us rely less on the annotated data to improve activity detection performance. Motivated by this, we propose STAR-Lite, a self-taught learning framework that involves a pre-training framework to prepare the new representation space considering activities beyond the initial labels in the labeled dataset. STAR-Lite projects the labeled data representations on the new representation space characterized by unlabeled data labels and learns higher-level representations of the labeled dataset while optimizing inter- and intra- class distances without explicitly using a computation hungry similarity-based approach. We demonstrate that our proposed approach, STAR-Lite (a) improves activity recognition performance in a supervised setting and (b) is feasible for real-world deployment. To enhance the feasibility of deploying STAR-Lite on devices with limited memory resources, we explore model compression techniques such as pruning and quantization and propose a novel layer-wise pruning-rate optimization technique that effectively compresses the network while preserving the model performance. The evaluation was performed using the Alzheimer’s Activity Recognition dataset (AAR) captured from 25 individuals living in a retirement community center with IRB approval (#Y18NR12035) using an in-house SenseBox infrastructure while concurrently assessing the clinical evaluation of the participants for dementia, and independent living. Our extensive evaluation reveals that STAR-Lite can detect activities with an F1-score of 85.12% despite 62% reduction in model size and 5% improvement of execution time on a resource constrained device.     

H∞ observer-based event-triggered sliding mode control for a class of discrete-time nonlinear networked systems with quantizations

This paper investigates the problem of H_∞ observer-based event-triggered sliding mode control (SMC) for a class of uncertain discrete-time Lipschitz nonlinear networked systems with quantizations occurring in both input and output channels. The event-triggered strategy is used to save the limited network bandwidth. Then, based on the zero-order-hold (ZOH) measurement, a state observer is designed to reconstruct the system state, which facilitates the design of the discrete-time sliding surface. Considering the effects of quantizations, networked-induced constraints and event-triggered scheme, the nonlinear state error dynamics and sliding mode dynamics are converted into a unified linear parameter varying (LPV) time-delay system with the aid of a reformulated Lipschitz property. By using the Lyapunov-Krasovskii functional and free weighting matrix, a new sufficient condition is derived to guarantee the robust asymptotic stability of the resulting closed-loop system with prescribed H_∞ performance. And then the observer gain, event-triggering parameter and sliding mode parameter are co-designed. Furthermore, a novel SMC law is synthesized to force the trajectories of the observer system onto a pre-specified sliding mode region in a finite time. Finally, a single-link flexible joint robot example is utilized to demonstrate the effectiveness of the proposed method.     

冀南山区传统村落主街界面形态量化研究

针对传统村落保护与更新过程中 其街巷界面形态出现同质化、雷同化、商品 化等问题,基于冀南山区内与等高线平行、垂 直、斜交三种类型的传统村落主街界面形态 数据,采用分形理论、数理统计学等相关分 析方法,量化描述了冀南山区传统村落主街 底界面与侧界面的形态规律及其组构方式, 总结出与一定量化参数区间所对应的传统村 落主街界面形态特征。     

Networked control systems for multi-input plants based on polar logarithmic quantization

This paper investigates the use of polar logarithmic quantization (PLQ) for multi-input systems, and the corresponding design issues for the underlying networked control system (NCS). It is shown that the PLQ induces sector bounded nonlinear uncertainties in multiplicative and relative forms for vector-valued analog signals, similar to those in the scalar case. For the two-input NCS, optimal quantization is obtained through minimization of the quantization error that is quantified explicitly. The results are extended to more than two-input NCSs with an upper bound derived for the quantization error. Feedback stabilization and control of the NCS are also investigated under the PLQ at the plant input under state feedback. The coarsest quantization density (CQD) is studied and obtained. Results in this paper are illustrated by a numerical example.     

Multi-leader coordination algorithm for networks with switching topology and quantized information

This paper analyzes the stability and convergence properties of a proportional–integral protocol for coordination of a network of agents with dynamic information flow and quantized information exchange. In the setup adopted, each agent is only required to exchange its coordination state with its neighboring agents, and the desired reference rate is only available to a group of leaders. We show that the integral term of the protocol allows the agents to learn the reference rate, rather than have it available a priori, and also provides disturbance rejection capabilities. The paper addresses the case where the graph that captures the underlying network topology is not connected during some interval of time or even fails to be connected at all times.     

Quantized based image watermarking in an independent domain

This paper presents a performance study for two well known quantized based watermarking schemes Scalar Costa Scheme (SCS), and Trellis Coded Quantization scheme (TCQ) in an independent domain. For our study, the independent domain is obtained by the combination of the cited schemes with Independent Component Analysis (ICA). The Independent Component Analysis is used while inserting and extracting the message. Thus a performance compromise improvement of the original SCS and TCQ schemes in terms of robustness, capacity, and security is shown. Then the obtained schemes performances are compared to the Spread Transform (ST) based scheme well known for its proven good robustness properties. Our results show that, using watermarking with side information in independent domain permits to improve the global SCS and TCQ schemes performances. For example, in the case of SCS, by studying the Bit Error Rate (BER) in function of the watermark to noise ratio (WNR), the robustness has been improved by 20 dB (1 decade) when WNR is equal to 0 dB. Moreover, the capacity study shows for the same WNR=0 dB, the amount of information to be transmitted without error for a given noise level of the proposed SCS combined with ICA scheme (SCS-ICA) is much higher than that of the existing schemes. Finally, the study of the probability density functions (PDF) of the original and marked signals has shown that the Cachin-security level of the TCQ in an independent domain scheme (TCQ-ICA) is the best one compared to the other known schemes.     

Robustness of quantized control systems with mismatch between coder/decoder initializations

This paper analyzes the stability of linear systems with quantized feedback in the presence of mismatch between the initial conditions at the coder and decoder. Under the assumption of the perfect channel, we show that using the scheme proposed in [Liberzon, D., & Neši?, D. (2007). Input-to-state stabilization of linear systems with quantized state measurements. Institute of Electrical and Electronic Engineers Transaction on Automatic Control, 52, 767-781] it is possible to achieve stability with exponential convergence of linear systems with quantized feedback when the coder and decoder are initialized at different initial conditions.