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Traditional model-based control methods typically require accurate system dynamics. However, when controlling a complex non-linear system such as a quadrotor unmanned aerial vehicle (QUAV), the dynamics are unknown and it is challenging to tune the control parameters manually. This paper proposes a novel model-free learning method that combines the advantages of a model-based method, i.e., sliding mode control (SMC), with the iterative learning control (ILC) method. Specifically, we selected a designed sliding surface to obtain the expected tracking error trajectory as the learning objective, and the system tracking errors of the angles of the QUAV constitute the state space. Then, the policy of converging to the sliding surface is learned by an ILC algorithm. We have provided theoretical proof of the convergence, and validated the proposed method with real-world experiments where the sine wave signals of roll and pitch angles were tracked. The results have demonstrated the effectiveness of the method with less tracking errors as well as faster learning speed compared with a baseline PID controller and a sliding mode controller.

In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives. This has catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis of resultant solution sets presents considerable challenges. This is primarily attributed to the high-dimensional nature of the data and the constraints imposed by static visualization methods, which frequently culminate in visual clutter and impede interactive exploratory analysis. To address these challenges, this paper introduces ParetoLens, a visual analytics framework specifically tailored to enhance the inspection and exploration of solution sets derived from the multi-objective evolutionary algorithms. Utilizing a modularized, algorithm-agnostic design, ParetoLens enables a detailed inspection of solution distributions in both decision and objective spaces through a suite of interactive visual representations. This approach not only mitigates the issues associated with static visualizations but also supports a more nuanced and flexible analysis process. The usability of the framework is evaluated through case studies and expert interviews, demonstrating its potential to uncover complex patterns and facilitate a deeper understanding of multi-objective optimization solution sets. A demo website of ParetoLens is available at https://dva-lab.org/paretolens/. [ABSTRACT FROM AUTHOR]

Synthetic data, complementary with real-world datasets, have shown their effectiveness for autonomous driving in urban scenarios. However, for specific and complex environments, such as surface mines, which often involve extreme weather and corner cases, synthetic datasets are notably scarce. In this article, PMScenes, the first synthetic dataset for autonomous driving in surface mines, is built upon the previously public AutoMine dataset. It extends the available data by constructing corner case scenarios, such as unusual intrusions; collisions; and extreme weather scenarios including rain, blizzards, fog, and dust storms—situations that would bring significant safety risks and are hard to capture in the real world. Our article explores the enhancement methods for synthetic to real for perception tasks of autonomous driving under different challenging scenarios. The outcomes demonstrate that PMScenes significantly enhances the performance across a variety of perception tasks compared with the results obtained when training only with real-world datasets. PMScenes is released at https://github.com/PMScenes/PMScenes. [ABSTRACT FROM AUTHOR]

The human ear has emerged as a bidirectional gateway to the brain's and body's signals. Recent advances in around-the-ear and in-ear sensors have enabled the assessment of biomarkers and physiomarkers derived from brain and cardiac activity using ear-electroencephalography (ear-EEG), photoplethysmography (ear-PPG), and chemical sensing of analytes from the ear, with ear-EEG having been taken beyond-the-lab to outer space. Parallel advances in non-invasive and minimally invasive brain stimulation techniques have leveraged the ear's access to two cranial nerves to modulate brain and body activity. The vestibulocochlear nerve stimulates the auditory cortex and limbic system with sound, while the auricular branch of the vagus nerve indirectly but significantly couples to the autonomic nervous system and cardiac output. Acoustic and current mode stimuli delivered using discreet and unobtrusive earables are an active area of research, aiming to make biofeedback and bioelectronic medicine deliverable outside of the clinic, with remote and continuous monitoring of therapeutic responsivity and long-term adaptation. Leveraging recent advances in ear-EEG, transcutaneous auricular vagus nerve stimulation (taVNS), and unobtrusive acoustic stimulation, we review accumulating evidence that combines their potential into an integrated earable platform for closed-loop multimodal sensing and neuromodulation, towards personalized and holistic therapies that are near, in- and around-the-ear. [ABSTRACT FROM AUTHOR]

Genomics plays an essential role in the early detection, classification, and targeted cancer therapy based on the analysis of precise alterations at the molecular level. Using the most reliable approach is essential for the exact interrogation and cross-examination of complex and multi-high-dimensional “Multi-omics” cancer genomics data. In recent years, deep learning has been successfully utilized to deal with large cancer genomics data and has the potential to transform predictive biology. This review aims to explore the recent advancements in the application of deep learning models in basic cancer omics research, including different methodologies for the interrogation of bulk cancer omics data and the importance of cross-platform data integration. The paper provides insights into advantages, limitations, potential for improvement, research gaps, future direction, and an in-depth comparison of the models currently used in the field of cancer genomics, highlighting the crucial need for collaboration and interdisciplinary research in the field. [ABSTRACT FROM AUTHOR]

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) models tailored to the intricacies of the medical field, bridging the gap between limited AI models and the varied nature of healthcare practices. The advancement of a healthcare foundation model (HFM) brings forth tremendous potential to augment intelligent healthcare services across a broad spectrum of scenarios. However, despite the imminent widespread deployment of HFMs, there is currently a lack of clear understanding regarding their operation in the healthcare field, their existing challenges, and their future trajectory. To answer these critical inquiries, we present a comprehensive and in-depth examination that delves into the landscape of HFMs. It begins with a comprehensive overview of HFMs, encompassing their methods, data, and applications, to provide a quick understanding of the current progress. Subsequently, it delves into a thorough exploration of the challenges associated with data, algorithms, and computing infrastructures in constructing and widely applying foundation models in healthcare. Furthermore, this survey identifies promising directions for future development in this field. We believe that this survey will enhance the community's understanding of the current progress of HFMs and serve as a valuable source of guidance for future advancements in this domain. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction. [ABSTRACT FROM AUTHOR]

Breath analysis and monitoring have emerged as pivotal components in both clinical research and daily health management, particularly in addressing the global health challenges posed by respiratory and metabolic disorders. The advancement of breath analysis strategies necessitates a multidisciplinary approach, seamlessly integrating expertise from medicine, biology, engineering, and materials science. Recent innovations in laboratory methodologies and wearable sensing technologies have ushered in an era of precise, real-time, and in situ breath analysis and monitoring. This comprehensive review elucidates the physical and chemical aspects of breath analysis, encompassing respiratory parameters and both volatile and non-volatile constituents. It emphasizes their physiological and clinical significance, while also exploring cutting-edge laboratory testing techniques and state-of-the-art wearable devices. Furthermore, the review delves into the application of sophisticated data processing technologies in the burgeoning field of breathomics and examines the potential of breath control in human-machine interaction paradigms. Additionally, it provides insights into the challenges of translating innovative laboratory and wearable concepts into mainstream clinical and daily practice. Continued innovation and interdisciplinary collaboration will drive progress in breath analysis, potentially revolutionizing personalized medicine through entirely non-invasive breath methodology. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD. [ABSTRACT FROM AUTHOR]

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging. [ABSTRACT FROM AUTHOR]

Flexible data placement (FDP) is an emerging interface within the NVM express (NVMe) storage standard, aiming to decrease write amplification factor (WAF) in solid state drives (SSDs) through explicit user-controlled data placement. Currently, the FDP ecosystem burdens embedded software programmers with low-level systems programming to efficiently deploy FDP SSDs. We propose FDPFS, a file system that elevates the abstraction to file systems by exposing FDP SSDs as directories to which programmers can easily group and direct semantically similar data for user-controlled data placement. Under the hood, FDPFS performs the tedious low-level tasks of interfacing and assigning these semantically grouped data to different SSD erase blocks to reduce WAF, and improve overall SSD performance and lifetime. Our case study on the filebench benchmark demonstrates that our FDPFS prototype not only eases explicit data placement, but also yields up to 34% reduction in the SSD WAF which promises improved overall performance and lifetime of the SSD. [ABSTRACT FROM AUTHOR]

Low density parity check code (LDPC) is the most popular error correction code (ECC) for current nand flash memory controllers. Next generation flash memory, such as triple-level cell (TLC) and quad-level cell (QLC) with a higher bit error rate, bring up the demand for superior ECC algorithms providing excellent performance and acceptable hardware overhead. This letter proposes turbo product code (TPC) as an alternative to LDPC by exploring embedding of a TPC engine into a solid-state drive (SSD) controller architecture. The implementation of this TPC engine uses 2-D error coding and applies the Bose-Chaudhuri–Hocquenghem (BCH) code in each dimension. The algorithm is further improved by flipping the dedicated bit matrix when the basic TPC decoding algorithm terminates with uncorrectable conditions. This bit-flipping enhanced TPC (BFE-TPC) module is finally integrated into a nonvolatile memory express (NVMe) SSD controller driving eight flash memory channels. This BFE-TPC module illustrates the potential of 2D-TPCs as a replacement for LDPCs due to its high throughput and low hardware overhead. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is anticipated to augment the performance of beyond fifth-generation (B5G) and sixth-generation (6G) networks by intelligently manipulating the state of its components. Rather than employing reflective RIS for aided communications, this paper proposes an innovative transmissive RIS-enabled transceiver (TRTC) architecture that can accomplish the functions of traditional multi-antenna systems in a cost-effective and energy-efficient manner. First, the proposed network architecture and its corresponding transmission scheme are elaborated from the perspectives of downlink (DL) and uplink (UL) transmissions. Then, we illustrate several significant advantages and differences of TRTC compared to other multiantenna systems. Furthermore, the downlink modulation and extraction principle based on time-modulation array (TMA) is introduced in detail to tackle the multi-stream communications. Moreover, a near-far field channel model appropriate for this architecture is proposed. Based on the channel model, we summarize some state-of-the-art channel estimation schemes, and the channel estimation scheme of TRTC is also provided. Considering the optimization for DL and UL communications, we present numerical simulations that confirm the superiority of the proposed optimization algorithm. Lastly, numerous prospective research avenues for TRTC systems are delineated to inspire further exploration. [ABSTRACT FROM AUTHOR]