Electrocardiogram (ECG), as a crucial fine-grained cardiac feature, has been successfully recovered from radar signals in the literature, but the performance heavily relies on the high-quality radar signal and numerous radar-ECG pairs for training, restricting the applications in new scenarios due to data scarcity. Therefore, this work focuses on radar-based ECG recovery in new scenarios with limited data and proposes a cardio-focusing and-tracking (CFT) algorithm to precisely track the cardiac location to ensure an efficient acquisition of high quality radar signals. Furthermore, a transfer learning model (RFcardi) is proposed to extract cardio-related information from the radar signal without ECG ground truth based on the intrinsic sparsity of cardiac features, and only a few synchronous radar ECG pairs are required to fine-tune the pre-trained model for ECG recovery. The experimental results reveal that the proposed CFT can dynamically identify the cardiac location, and the RFcardi model can effectively generate faithful ECG recoveries after using a small number of radar-ECG pairs for training. The code and dataset will be made available after publication.

Time series modeling serves as the cornerstone of real-world applications, such as weather forecasting and transportation management. Recently, Mamba has become a promising model that combines near-linear computational complexity with high prediction accuracy in time series modeling, while facing challenges such as insufficient modeling of nonlinear dependencies in attention and restricted receptive fields caused by convolutions. To overcome these limitations, this paper introduces an innovative framework, Attention Mamba, featuring a novel Adaptive Pooling block that accelerates attention computation and incorporates global information, effectively overcoming the constraints of limited receptive fields. Furthermore, Attention Mamba integrates a bidirectional Mamba block, efficiently capturing long-short features and transforming inputs into the Value representations for attention mechanisms. Extensive experiments conducted on diverse datasets underscore the effectiveness of Attention Mamba in extracting nonlinear dependencies and enhancing receptive fields, establishing superior performance among leading counterparts. Our codes will be available on GitHub.

Motivated by traffic analysis attacks based on the packet sizes and timing information in the Internet of Things (IoT) networks, we establish a rigorous event-level differential privacy (DP) model on infinite packet streams. We propose a traffic shaper satisfying a first-come-first-served queuing discipline that outputs traffic dependent on the input using a DP mechanism. We show that in special cases the proposed mechanism recovers existing shapers which standardize the output independently from the input. To find the optimal shapers for given levels of privacy and transmission efficiency, we formulate the constrained problem of minimizing the expected delay per packet and propose using the expected queue size across time as a proxy. We further show that the constrained minimization is a convex program. We demonstrate the effect of shapers on both synthetic data and packet traces from actual IoT devices. The experimental results reveal inherent privacy-overhead tradeoffs: more shaping overhead provides better privacy protection. Under the same privacy level, there is a tradeoff between dummy traffic and delay. When shaping heavier or less bursty traffic, all shapers become more overhead-efficient. We also show that increased traffic from more IoT devices makes guaranteeing event-level privacy easier. The DP shaper offers tunable privacy that is invariant with the change in the input traffic distribution and has an advantage in handling burstiness over traffic-independent shapers. This approach accommodates heterogeneous network conditions and user demands in privacy and overhead.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. We design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralyzed side, so that the blink of the both eyes become symmetric. Our contributions are: First, we propose an eye-blink detection mechanism based on support vector machine (SVM), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99 percent. Our eye-image library for training the model is published online for further related studies, which contains more than 30,000 eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. We conduct experiments in a hospital to obtain the design basis and verify effectiveness of our device.

Recently proposed Wi-Fi indoor localization systems utilizing channel state information (CSI) derived from the received signal achieve admirable accuracy. This paper presents an information-theoretical analysis in the received waveform level to explore the fundamental limits of the approach. In particular, we perform frequency domain Cramer-Rao bound (CRB) analysis for location estimation with CSI. Our analysis resolves the high-rank Fisher information matrix challenge, and establishes intrinsic connection between parameters to be estimated for localization and the received waveform information observable with the CSI retrieving toolkit. We also analyze the influence of the asynchronization between the transmitter and the receiver to the performance bound of the CSI approach. Moreover, we shed light on the insight into the design of the CSI localization systems. In particular, we show that the CSI approach presents varying performance for localizing targets in different distances and directions with respect to the CSI retrieving anchor point (AP), and the geometric distribution of AP antennas could fundamentally influence the localization performance. Comprehensive experimental results are demonstrated to validate our theoretical analysis.

Motivated by privacy issues in the Internet of Things (IoT), we generalize a previously proposed privacy-preserving packet obfuscation scheme to guarantee differential privacy. We propose a locally differentially private packet obfuscation mechanism as a defense against packet-size side-channel attacks in IoT networks. We formulate the problem as an optimization over a conditional probability distribution (channel) between the original and obfuscated packet sizes and show that the optimal set of obfuscated packet sizes is a strict subset of the set of original packet sizes. We study the optimal mechanisms for minimizing the (average or min-max) bandwidth overhead subject to a privacy constraint by solving the corresponding (linear or convex) program. We demonstrate our methods on synthetic and real data to illustrate privacy-bandwidth tradeoffs in different settings. Systems with many bandwidth-intensive devices can easily mask low-bandwidth devices. For data collected from actual smart home IoT devices, we show how the packet size distributions become increasingly indistinguishable as the level of privacy protection increases. The proposed mechanism highlights the possibility for bandwidth-constrained users to optimally tune their privacy preferences and trade off privacy with bandwidth.

Facial paralysis is a disease caused by nerve damage, which can make patients lose facial movements. Facial paralysis patients usually have muscles on one side of the face noticeably droop, which seriously impacts the person's quality of life as shown in Fig. [skull]. Worse still, the eye on the affected side is unable to blink and will become dry and infected by debries, which can incur eye damage even blindness. To the best of scientists' knowledge, the paralysis is due to the pressure incurred by infection in the tunnel containing main trunk of facial nerves, where the tunnel is inside of the people's head termed as the Facial canal. In this demo, we present iBlink [1], a novel system to help paralysis patients to blink. Paralysis usually occurs in just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, the basic idea of iBlink is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that eye-movements of the both sides become symmetric.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. The paralysis usually occurs on just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, we design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that the blink of the both eyes become symmetric. To the best of our knowledge, this is the first piece of wearable device for facial paralysis therapy. Our contributions are: First, we propose an eye-blink detection mechanism based on deep convolutional neural network (CNN), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99%. Our eye-image library for training CNN models is published online for further related studies, which contains more than $30,000$ eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. Moreover, we conduct clinical trials in a hospital, in order to obtain the design basis and verify effectiveness of our device.

In this paper we study how individual sensors can compress their observations in a privacy-preserving manner. We propose a randomized requantization scheme that guarantees local differential privacy, a strong model for privacy in which individual data holders must mask their information before sending it to an untrusted third party. For our approach, the problem becomes an optimization over discrete mem-oryless channels between the sensor observations and their compressed version. We show that for a fixed compression ratio, finding privacy-optimal channel subject to a distortion constraint is a quasiconvex optimization problem that can be solved by the bisection method. Our results indicate interesting tradeoffs between the privacy risk, compression ratio, and utility, or distortion. For example, in the low distortion regime, we can halve the bit rate at little cost in distortion while maintaining the same privacy level. We illustrate our approach for a simple example of privatizing and recompressing lowpass signals and show that it yields better tradeoffs than existing approaches based on noise addition. Our approach may be useful in several privacy-sensitive monitoring applications envisioned for the Internet of Things (IoT).