Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n× 2nafter a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal.

Source-free unsupervised domain adaptation (SFUDA) aims to conduct prediction on the target domain by leveraging knowledge from the well-trained source model. Due to the absence of source data in the SFUDA setting, the existing methods mainly build the target classifier by fine-tuning the source model incorporated with empirical adaptation losses. Although these methods have achieved somewhat promising results, nearly all of them typically suffer from the closed-fitting dilemma that their models are dominantly affected by these easy-to-distinguish instances than those hard-to-distinguish ones, resulting from the absence of the labeled source data. To address aforementioned issues, we propose the Dipolar Confidence Learning (DCL) for SFUDA. Specifically, we conduct positive confidence learning on the samples with standard outputs to avoid overfitting of the model to these samples. In contrast, we perform negative confidence learning for the samples with abnormal outputs to optimize the complementary label, which forces the network to pay more attention to these confusing samples. Furthermore, to achieve more generalized domain alignment, both the confidence-based fuzzy mixup and rotation-based self-supervised learning are respectively constructed to boost the representation ability of the target model. Finally, extensive experiments are conducted to demonstrate the effectiveness and performance superiority of the proposed method.

Open set domain adaptation aims to transfer knowledge in the presence of unknown samples in the target domain. Previous approaches use additional classifiers or threshold-based methods to identify unknown samples and try to investigate the information of class diversity within the unknown samples. Despite achieving excellent adaptation results, these methods ignore those samples that lie on the cluster boundaries, especially the clustering-based methods. In this paper, we propose a novel Neighbor Prototype Contrastive Clustering (NPC2) method, which uses the Local Semantic Structure (LSS) to help these low-confidence samples located on the boundary of clusters to return to their own clusters. Further, we propose Local Semantic Consistency (LSC) to evaluate the clustering result and apply it to the domain adaptation process as a metric to assess the reliability of the samples. Results on four benchmarks show that our NPC2significantly outperforms most state-of-the-art methods with higher LSC.

In smart healthcare facilities designed especially for the elderly, noncontact electrocardiogram (ECG) measurements could provide essential information about an elderly person’s health by enabling long-term health analytics. In this research work, we propose an Internet of Things (IoT)-based noncontact ECG measurement system. The noncontact measurement is done using flexible electrodes that are made of fabric. These fabric-based flexible electrodes are designed to measure ECG signals from the sole of the feet (SOF) or the palms of the hands (POHs) without touching human skin. To mitigate the impact of nearby electromagnetic radiation on the electrodes, a double layer of isopotential shielding is placed underneath the two active electrodes. The gathered biosignals are stored in the IoT device and transmitted to the cloud. To reduce the amount of stored and transmitted data, we improved our adaptive coding algorithm. The adaptive coding results in an average data reduction of 72%. The data can be fully recovered in the cloud for further analyses using advanced cloud-based tools in ThingSpeak. The study tested the proposed system on 35 participants, including elderly persons, adults, and children. Based on the experiments, the proposed system accurately measures the ECG signal. We validated the results with the ground truth data [polysomnography (PSG)] showing an average heart rate (HR) error of$\mp 1$beat per minute (BPM). Moreover, we compared QRS complexes detected on wrists with those detected from SOF (with or without socks), POH (with or without gloves), and one hand and one foot (with or without a sock and glove), and found no significant differences.

Recently, nonshared-and-imbalanced unsupervised domain adaption has been proposed to fix domain shift from Big Data source domain with long-tail distribution to specific small target domain with imbalanced distribution, including two challenges: 1) nonshared classes sharing in big data with long-tail distribution; and 2) imbalanced domain adaptation. Prior approaches explore knowledge sharing between classes to improve performance of unsupervised domain adaption methods. However these methods have inductive bias for prior tree or graph. And previous contrastive domain adaptation methods take center-based prototypes as positive samples which only coarsely characterize the domain structure, and fail to depict the local data structure. To fix these problems, we propose a novel framework called contrastive memory feature transfer (CMFT). To solve nonshared data sharing without inductive bias, we build a centroid memory baseddirected memory transfermechanism to enhance imbalanced class features with similar nonshared class centroid. To address the imbalanced domain adaptation, we design a fault-tolerant and fine-grainedneighborhood prototypefor the contrastive learning which can narrow the domain shift. The proposed CMFT outperforms previous methods on most benchmarks.

Source-free unsupervised domain adaptation (SFUDA) aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although many SFUDA works use the pseudo-labeling strategy to improve the accuracy of pseudo-labels in the target domain, these strategies ignore the influence of domain shift on calculating the reference distribution of pseudo-labels. In this article, we propose a novel kind of SFUDA with trusted pseudo samples (SFUDA-TPS), which uses reliable feature reference distribution to solve the SFUDA problem. In SFUDA-TPS, we design a target feature correcting classifier to alleviate the problem of feature reference distribution deviating from target domain samples distribution. On this basis, the more reliable feature reference distribution is calculated by selecting the target domain samples with a high amount of information, i.e., low entropy in the fixed source domain classifier and target feature correcting classifier. The implicit alignment between the source domain and target domain is realized by learning the source domain distributions hidden in the fixed source domain classifier. Experimental evaluations illustrate the effectiveness of our proposed method in solving SFUDA tasks.