Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optically-Powered 432 MHz Wireless Tag for Batteryless Internet-of-Things Applications

This paper presents an optically-powered wireless tag, integrating a charge pump (CP) and a low-power RF transmitter for batteryless Internet-of-Things (IoT) applications. The proposed charge pump with hysteresis regulation mechanism (HRM) powers the transmitter circuit using indoor light energy. The charge pump enables the transmitter whenever the output capacitor stores sufficient energy and guarantees a short period signal transmission. The proposed low-power transmitter contains the subharmonically injection-locked PLL and the capacitive coupling technique to achieve a large frequency multiplication ratio. The proposed batteryless wireless chip is fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The measurement results demonstrate that the proposed charge pump provides a 1.1 to 1.3 V hysteresis regulated voltage under indoor light illumination larger than 300 lux. The charge pump achieves 84.1% peak efficiency and the communication distance of 3 meters is demonstrated while only consuming <inline-formula> <tex-math notation="LaTeX">$248~\mu \text{W}$ </tex-math></inline-formula>.
Occams Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.
A Novel Approach for Improving the Accuracy of Moving Objects Detection and Tracking using an Advanced Pattern Matching Technique

: Moving object detection and tracking is an essential part of digital image processing. Object detection and recognition used image processing techniques like image segmentation, classification and grouping of objects with pattern matching for addressing issues in different dimensions. Object tracking is very important in logical video surveillance system. This research papers presents a review on the standard existing techniques, which can be combined with several other advanced methods like extended kalman filtering for efficient moving objects detection and tracking. The present work focuses on the factors like object detection accuracy, object tracking time and pattern matching rate.
On Sharing an FIB Table in Named Data Networking

As a new networking paradigm, Named Data Networking (NDN) technology focuses on contents, and content names are used as identifiers for forwarding and routing, as opposed to IP addresses in the current Internet. NDN routers forward packets by looking up a Forwarding Information Base (FIB), each entry of which has a name prefix and output faces. An FIB should have the information to forward Interest packets for any contents. Hence, the size of an FIB would be excessively large in NDN routers, and the traffic for building an FIB would be significant. In order to reduce the traffic associated with building an FIB table and memory requirement for storing an FIB table, this paper proposes a new efficient method which combines the routing of network connectivity and the building of a forwarding engine using Bloom filters. We propose to share the summary of an FIB using a Bloom filter rather than to advertise each name prefix. The forwarding engine of the proposed scheme is a combination of Bloom filters, and hence the memory requirement of the forwarding can be much smaller than the regular FIB. Simulation results using ndnSIM under real network topologies show that the proposed method can achieve nearly the same performance as the conventional link state algorithm with 6–8% of the traffic for distributing the connectivity information and 5–9% of the memory consumption.
Impact of Training Images on Radiometric Compensation

The increasing availability of both high-resolution projectors and imperfect displays make radiometric correction an essential component in all modern projection systems. Particularly, projecting in casual locations, such as classrooms, open areas and homes, calls for the development of radiometric correction techniques that are fully automatic and deal with display imperfections in real-time. This paper reviews the current radiometric compensation algorithms and discusses the influence of different training images on their performance.
Generalized Hierarchical Word Sequence Framework for Language Modeling

Language modeling is a fundamental research problem that has wide application for many NLP tasks. For estimating probabilities of natural language sentences, most research on language modeling use n-gram based approaches to factor sentence probabilities. However, the assumption under n-gram models is not robust enough to cope with the data sparseness problem, which affects the final performance of language models. In this paper, we propose a generalized hierarchical word sequence framework, where different word association scores can be adopted to rearrange word sequences in a totally unsupervised fashion. Unlike the n-gram which factors sentence probability from left-to-right, our model factors using a more flexible strategy. For evaluation, we compare our rearranged word sequences to normal n-gram word sequences. Both intrinsic and extrinsic experiments verify that our language model can achieve better performance, proving that our method can be considered as a better alternative for n-gram language models.
Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model

The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.
The Accuracy of Phonological Decoding Skill Among Japanese EFL Learners

Phonological decoding skill has proved to be an important factor in word recognition. However, its accuracy in the field of second language acquisition has not been adequately investigated. This study examined the details of phonological decoding skill in English. Sixty Japanese undergraduate and graduate students judged the lexicalities of sixty target words (Consonant vs. Vowel vs. Nonword condition) in an assigned lexical decision task. The results showed that response times were significantly longer for Consonant and Vowel conditions compared to Nonword condition, confirming that the participants possessed phonological decoding skill of consonants and vowels and employed such skill when making their lexical decisions. The results also suggested that there was no significant difference in terms of accuracy between the participants’ phonological decoding skill of consonants and that of vowels. In addition, error analysis revealed a number of factors related to high and low error rates: (a) the influence of the romaji writing system, (b) English sounds that are not differentiated in Japanese (e.g., /r/ and /l/), (c) sounds with the same pronunciation but different spelling (e.g., /ce/ and /se/), (d) variation in the spellings of the schwa sound, (e) high level of word familiarity, and (f) ease of visual recognition.
Using constraints to enhance decision-making in team sports

An important consideration in the development of skilled performance is the interaction between the environment and the performer. When a task is practiced in an environment that is not an accurate representation of the typical performance setting, or when the environment contains insufficient levels of variability, expert performance can deteriorate to the level of novices (Allard et al., 1980; Araujo et al., 2005) and learners will tend to rely on non-specifying information if that information allows reasonably accurate performance (Beek et al., 2003; Jacobs et al., 2001). Furthermore, the ability of the learner to converge upon more useful specifying information proceeds at a slower rate than if the learner had been exposed to a more representative and variable environment (Beek et al., 2003).
High Power Density 48–12 V DCX With 3-D PCB Winding Transformer

Planar transformer has been used in high frequency high power density dc–dc converters for its low profile. With the packaging technology development of the power devices, their footprints become smaller and smaller. However, the reduction in the core loss density of high frequency soft ferrite materials is slow, which results in an insensible reduction of the cross area of the transformer magnetic core. Hence, the transformer becomes the highest component in many high-density brick dc–dc converters, which restrict the improvement of power density. This letter proposes a three-dimensional (3-D) PCB winding structure, which surrounds the flat magnetic core. By mounting the components to the top and bottom layers of PCB windings, the transformer volume is reduced and the power density can be improved significantly. A 1.6 MHz 48–12 V 300 W DCX prototype with GaN device is built up to verify the 3-D PCB winding concept. The peak efficiency 97.3% and power density 1380 W/in3 are achieved by using the proposed 3-D PCB winding transformer and integrated layout.
Back-Support Exoskeletons for Occupational Use: An Overview of Technological Advances and Trends

OCCUPATIONAL APPLICATIONS Many new occupational back-support exoskeletons have been developed in the past few years both as research prototypes and as commercial products. These devices are intended to reduce the risk of lower-back pain and injury for workers in various possible application sectors, including assembly in automotive and aerospace, logistics, construction, healthcare, and agriculture. This article describes the technologies adopted for back-support exoskeletons and discusses their advantages and drawbacks. Such an overview is intended to promote a common understanding and to encourage discussion among different stakeholders such as developers, ergonomics practitioners, customers, and workers. TECHNICAL ABSTRACT Background: The large prevalence and risk of occupational lower-back pain and injury associated with manual material handling activities has raised interest in novel technical solutions. Wearable back-support exoskeletons promise to improve ergonomics by reducing the loading on the lumbar spine. Purpose: Since many new prototypes and products are being developed, this article presents an up-to-date overview of the different technologies. By discussing the corresponding advantages and drawbacks, the objective is to promote awareness and communication among developers, ergonomics practitioners, customers, and factory workers. Methods: The state-of-the-art is presented with a focus on three technological aspects: (i) the actuators generating assistive forces/torques, with a main distinction between passive and active devices; (ii) the structures and physical attachments that transfer those forces/torques to the user, with structures being soft, rigid, or a combination of the two; and (iii) the control strategies employed (i.e., how devices adjust assistive forces/torques to accommodate different activities and parameters). Discussion: The choice of actuation technology may determine the applicability of a device to different scenarios. Passive exoskeletons appear more suitable for tasks requiring relatively light assistance and little dynamic movements. By contrast, heavier and more dynamic tasks will justify the use of more complex active exoskeletons. While on-board battery power is increasingly present on active exoskeletons, the tradeoff between power autonomy and additional battery mass will probably depend on the specific application. Most back-support exoskeletons are implemented using rigid articulated structures, which tend to be heavy and bulky, but generate more appropriate patterns of forces. Fewer soft exoskeletons have been developed to date, although they could be integrated with or worn underneath standard working attire and offer greater user comfort. The adoption of any given device will ultimately depend on several factors, including user acceptance and the costs and benefits associated with specific applications.
Feasibility Study of Wafer-Level Backside Process for InP-Based ICs

This paper reports wafer-level backside process technology, established with the intent to ensure stable operation of InP ICs in the submillimeter wavelength band, which generally suffer from ground bounce and substrate resonance. Our process consists of thinning a 3-in InP wafer, forming dense vias with interval cooling steps, backside metallization with single-level wiring and crack-free dicing. We investigate the effects of the backside process on InP-based heterojunction bipolar transistors and high electron mobility transistors. The results show that the backside process contributes to stable operation up to the 300-GHz range without any degradation of transistor characteristics.
Scalable Mining of Contextual Outliers Using Relevant Subspace

In this paper, we propose a scalable mining algorithm to discover contextual outliers using relevant subspaces. We develop the mining algorithm using the MapReduce programming model running on a Hadoop cluster. Relevant subspaces, which effectively capture the local distribution of various datasets, are quantified using local sparseness of attribute dimensions. We design a novel way of calculating local outlier factors in a relevant subspace with the probability density of local datasets; this new approach can effectively reflect the outlier degree of a data object that does not satisfy the distribution of the local dataset in the relevant subspace. Attribute dimensions of a relevant subspace, and local outlier factors are expressed as vital contextual information, which improves the interpretability of outliers. Importantly, the selection of ${N}$ data objects with the largest local outlier factor value is categorized as contextual outliers in our solution. To this end, our scalable mining algorithm, which incorporates the locality sensitive hashing distributed strategy, is implemented on a Hadoop cluster. The experimental results validate the effectiveness, interpretability, scalability, and extensibility of the algorithm using both synthetic data and stellar spectral data as experimental datasets.
Perspectives and Challenges in Robotic Neurorehabilitation

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.
DESIGN OF A NOVEL UWB MICROSTRIP ANTENNA WITH SIW FEED

Antenna miniaturization, which is a requirement of modern wireless communication systems, is usually concomitant with the reduction of impedance bandwidth. On the other hand, small antennas should also possess stable radiation patterns across a broad frequency band, such as in UWB systems. In this paper, we propose a UWB antenna structure with a novel feeding system composed of an open cavity resonator. It has a wide relative bandwidth (of about 120%) particularly at the lower frequency limits. The variation of radiation pattern across its operating bandwidth is also negligible. The proposed antenna with the novel feed system is smaller and has a wider frequency bandwidth than other available UWB antennas in the literature. Furthermore, another antenna is proposed, which has a feeding system composed of a surface integrated resonator cavity, fabricated on a two-layer microstrip structure. It has achieved better miniaturization and bandwidth, albeit somewhat lower gain. Three prototype models of the proposed antennas are fabricated and measured, of which the frequency response is in excellent agreement with computer simulation results.
High Input Impedance Low-Noise CMOS Analog Frontend IC for Wearable Electrocardiogram Monitoring

This brief presents a CMOS analog frontend (AFE) IC with high input impedance low input-referred noise, which for wearable electrocardiogram monitoring. The IC has three main parts, pseudo-differential chopper-stabilized instrumentation amplifier, switched capacitor filter, and low-power continuous–time <inline-formula> <tex-math notation="LaTeX">${{\Sigma}} {{\Delta }}$ </tex-math></inline-formula> modulator. The pseudo-differential structure with chopper stabilization improves the input impedance, input–referred noise and chip area. Notably, we propose a new type of negative capacitor circuit to further boost the input impedance. Furthermore, the auto-zero technique is applied to the ripple reduction loop for suppressing the second harmonic ripple and maintain operation throughout the loop cycle. A dc servo loop suppresses the electrode dc offset. In <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS process, the proposed AFE IC occupies an active area of 0.99 mm<sup>2</sup>, consumes 5.8/<inline-formula> <tex-math notation="LaTeX">$46.6~\mu \text{A}$ </tex-math></inline-formula> from a 1.8 V supply, and achieves an input impedance above 2 <inline-formula> <tex-math notation="LaTeX">$\text{G}{{\Omega }}$ </tex-math></inline-formula>, a CMRR of 102 dB and a PSRR of 72 dB.
Comparison of various solution techniques in dispatching coupled electricity‐heat system with independent thermal energy storage

Notable benefit can be brought by combined operation of coupled electricity-heat system (CEHS) and be enhanced by introducing independent thermal energy storage (ITES). With prevalent constant-flow variable temperature (CF-VT) control strategy of heat network, explicitly formulating CEHS with ITES leads to mixed-integer non-convex programming. Although the problem can be reformulated as a mixed integer second-order cone programming (MISOCP) problem and solved by commercial solvers, improving the computation efficiency still needs more effort. Here, several alternative solution techniques, based on either reformulation or approximation methods, are studied and compared with the original MISOCP formulation. The computation efficiencies as well as on the solution accuracies of various solution techniques or a combination of them are investigated and analysed based on two constructed systems. Simulation results reveal that appropriately selecting formulations of electric and heat networks can effectively improve the performance of solving the original problem.
Improved Distributed Minimum Variance Distortionless Response (MVDR) Beamforming Method Based on a Local Average Consensus Algorithm for Bird Audio Enhancement in Wireless Acoustic Sensor Networks

Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio enhancement of WASN. However, there are still several disadvantages of this method, such as large noise residue and slow convergence rate. To overcome these shortcomings, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method for bird audio enhancement in WASN is proposed in this paper. In this method, the average metropolis weight local average consensus algorithm is first introduced to increase the consensus convergence rate, then a continuous spectrum update algorithm is proposed to estimate the noise power spectral density (PSD) to improve the noise reduction performance. Lastly, an MVDR beamformer is introduced to enhance the bird audio. Four different network topologies of the WASNs were considered, and the bird audio enhancement was performed on these WASNs to validate the effectiveness of the proposed method. Compared with two classical methods, the results show that the Segmental signal to noise ratio (SegSNR), mean square error (MSE), and perceptual evaluation of speech quality (PESQ) obtained by the proposed method are better and the consensus rate is faster, which means that the proposed method performs better in audio quality and convergence rate, and therefore it is suitable for WASN with dynamic topology.