Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Settling of AC-Coupled Amplifier for Active Contactless ECG Electrode

Contactless electrodes have the ability of long- term wearable physiological recording to meet the massive data requirements in digital health. Active electrodes have been used to avoid noise induced by the high-impedance contactless electrodes for better data quality. However, the settling time of the active contactless electrodes is relatively long (e.g., several seconds), restricting the use of contactless electrodes in frequent power on-off operations to save power which is usually supplied by a battery. We develop a low-power active contactless electrode circuit based on the AC-coupled amplifier with a speed-up settling property. Simulation results demonstrated that the ECG baseline drifted during the settling period after power on was eliminated. The proposed electrode is suitable for power-saving operations in long-term wearable ECG applications.
Optimized time-domain control of passive haptic teleoperation systems for multi-DoF interaction

This paper presents a time-domain passivity controller for multi-DoF haptic-enabled teleoperation systems aimed at improving performance in terms of transparency for a given task. By solving an online convex optimization problem, the proposed approach enhances transparency of interaction along specific directions of the environment space which are significant for the task at hand, while guaranteeing system stability. An experimental evaluation of the effectiveness of the proposed design is presented, enrolling twenty participants. We compared the performance of the proposed approach vs. those of a standard energy-bounding time-domain algorithm during the exploration of a virtual sphere. Results show that, as the communication delay between the local and remote agents grows, the proposed technique better preserves transparency along the directions that are more important for the task at hand.
Plasmonic photocatalysis using porous bimetallic AuPt structure

In this work, we report a facile way of fabricating a porous bimetallic alloy of AuPt sponge structure by thermal dewetting followed by a dealloying technique. The porous nature and inherent plasmonic properties of the metal components make the as-fabricated alloy an effective catalyst for driving the photodegradation of methylene blue. A considerable change is observed in the photoreduction efficiencies in presence of electron and hole scavenging species signifying the difference in the role of charge carrier.
Optical Measurements of the Skin Surface to Infer Bilateral Distinctions in Myofascial Tissue Stiffness

About half the U.S. adult population suffers from chronic neuromusculoskeletal pain. While its evaluation and treatment are widely addressed by therapies using soft tissue manipulation (STM), their efficacy is based upon clinician judgment. Robust biomarkers are needed to quantify the effects of STM on patient outcomes. Among noninvasive methods to quantify the mechanics of myofascial tissue, most are limited to small ($\lt 10$ mm$^{\mathbf{2}}$), localized regions of interest. In contrast, we develop an approach to optically simultaneously measure a larger ($\sim 100$ cm$^{2}$) field of deformation at the skin surface. Biomarkers based on skin lateral mobility are derived to infer distinctions in myofascial tissue stiffness. In specific, three cameras track ink speckles whose fields of deformation and stretch are resolved with digital image correlation. Their ability to differentiate bilateral distinctions of the cervicothoracic region is evaluated with four participants, as a licensed clinician performs STM. The results indicate that the optically derived surface biomarkers can differentiate bilateral differences in skin mobility, with trend directions within a participant similar to measurements with an instrumented force probe. These findings preliminarily suggest skin surface measurements are capable of inferring underlying myofascial tissue stiffness, although further confirmation will require a larger, more diverse group of participants.
Geometry Dependent Multi-stage Compact Thermal Model for Advanced Nanosheet FETs

Gate-All-Around Nanosheet FETs (NSFETs) are promising candidates for sub 5nm node, however, the aggressive scaling NSFETs are susceptible to the self-heating effect (SHE). A precise thermal model compatible with industry SPICE model, e.g. BSIMCMG, is critical to improve the yield of NSFET-based integrated circuit design and to reduce the development timeline of the advanced process. In this paper, we propose a geometry dependent multi-stage thermal RC network to accurately predict self-heating over a wide frequency range, from DC to THz, for the advanced NSFETs, which is validated by the transient thermal simulations based on 3-D Finite Element Modeling (FEM) method. The proposed compact thermal model enables to take the impacts of nanosheet width, number of stacks (gate fingers) and gate length into account, where the stage and model parameters of the thermal RC model are determined by Bayesian deconvolution and Particle Swarm Optimization (PSO) methods, respectively. The underlying physical understandings behind the geometry dependent thermal behaviors are discussed in detail.
Investigating Safety and Efficiency of Wearable Mixed Reality Helmet Interaction Design for Delivery Riders in Multitasking Environment

As the demand for delivery services continues to surge, an efficient and safe interaction design for delivery riders is of utmost importance. With wearable mixed reality (MR) technology showing potential for enhancing the performance of many tasks in various domains, we investigate the safety and efficiency of wearable MR helmet interaction designs for delivery riders operating in multitasking environments. To assess the effectiveness of MR helmets, we focus on the design of critical interaction design such as displaying navigation information, order information, and customer communication. We analyzed and evaluated the MR helmet's capabilities using real-world scenarios in a multitasking environment, with potential distraction factors. We constructed a system architecture design based on the Internet of Things (IoTs) and the Internet of Vehicles (IoVs). We also presented the user interaction design details such as augmentative visual cues, real-time notifications, and hands-free communication. The proposed MR helmets have great potential to improve the overall efficiency of delivery riders in completing tasks while enhancing their overall safety by reducing information overload.
Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar Sensor Applications

The millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of Things (IoT). This has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. This can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. This paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. The LNA was designed to operate at 0.6V and 700 μA for low power consumption. The LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. The LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power.
Distributed Optimal Power Flow for a Standalone All-Electric Ship using Asynchronous ADMM

Optimal Power Flow (OPF) is used in a standalone all-electric ship (AES) to minimize generation cost, while maintaining the voltage within constraints. Due to computational complexity in solving the OPF problem using a centralized approach, a distributed optimization technique using the alternating direction method of multipliers (ADMM) is employed. Two types of ADMM methods: synchronous and asynchronous, are used for a 9-node ring bus topology and their computational efficiency is compared. Master and workers control the system by dividing it into areas, and the sequence of operations is demonstrated considering any communication delays. The proposed control strategy can be extended to a higher node dc microgrid that includes power electronic converters, pulsed loads, energy storage, and the efficiency can be tested for different topologies.
Image Compressed Sensing Using Multi-Scale Characteristic Residual Learning

Deep network-based image compressed sensing (CS) methods have attracted much attention in recent years due to their low reconstruction complexity and high reconstruction quality. However, the existing methods usually use one or multiple convolution layer(s) consisting of convolutional kernels with the same size to extract image features in image sampling, which results in incomplete feature extraction. Besides, the existing models usually focus on the extraction of deep features in image reconstruction, while ignoring the influence of shallow features. To overcome these issues, this paper proposes a multi-scale characteristic residual learning network (dubbed MSCRLNet) for image CS. In this network, convolutional kernels with different sizes are used to capture multi-level spatial features in image sampling, and a multi-scale residual network with channel attention is used to speed up network convergence in image reconstruction. Experiments show that the proposed MSCRLNet outperforms many existing state-of-the-art methods.
Data-Mining with Three Genome-Scale Approaches Supports that Lungfish is the Closest Living Relative of Land Vertebrate, but not Coelacanth

The origin of tetrapod has been one of intense debating open questions for decades between coelacanth (Latimeria chalumnae) and lungfish (Protopterus annectens). For resolving this incongruence in phylogenies, a genome-wide data mining approach is used to retrieve 43 shared genes of seven taxa from GenBank and further 1001 orthologous genes of ten taxa from the Ensembl and NCBI. We used the maximum gene-support tree approach and the majority-rule branch approach to analyze 43 nuclear genes encoding amino acid residues, and compared these results to those inferred with the concatenation approach. Our results successfully provide strong evidence in favor of the lungfish-tetrapod hypothesis, but reject the coelacanth-tetrapod hypothesis based on significantly fewer gene support frequency and lower taxon jackknife probabilities for the coelacanth-tetrapod clade than the lungfish-tetrapod one with the maximum gene-support tree approach and the jackknife method for taxon subsampling. When more genomic data became available in recent years, sequencing data of 1001 shared genes was mined. We used the maximum gene-support approach with this larger dataset successfully to infer that lungfish is the closest relative of land vertebrates with a significant difference at p < 0.01 (Chi-Square test) in gene support values between the maximum gene-support tree and the second most gene-support tree with ML methods. The second most gene support frequency to the maximum gene support frequency (SM ratio), a combined index, is a better support measure than a single absolute value of support to reveal the insight of the phylogenetic support. Our results also show increasing the number of shared genes is much more effective than increasing the number of taxa.
Design and Deployment of ML Model for Cardiac Disease Prediction-A review

One of the most life-threatening diseases is cardiovascular disease. Its high fatality-rate catalysis nearly 17 million deaths worldwide. Prompt diagnosis assists in treating the disease at the right time to intercept mortality. Analyzing the existence and absence of the disease can be done using a variety of machine learning and deep learning techniques. In this review paper the complete procedure to make intelligent model is discussed. The UCI dataset is used in this project to classify cardiac disease using logistic regression techniques. To enhance the execution of the model, pre-processing of data by uncluttering the dataset, detecting the missing values is done and feature selection was accomplished by correlation with the target value for all the features. The distinctly positive correlated features were chosen. Then classification is performed by splitting the dataset into training data and testing data in the ratio of 80:20 split-up. The designed model secured 83.6% accuracy. The effectiveness of the proposed model was thoroughly gratifying and was competent to predict evidence of having heart disease in a certain individual which exhibited quality precision (77%) and recall (89%). This heart disease prediction system amplifies medical care and mitigates the cost. This research paper gives us significant knowledge that can help us predict, if the patient has a defective heart or not. The proposed work's evaluation has been carried out in Google Colab. Deployment has been done using the joblib library.
An Underwater Wideband Multi-Highlight Active Target Velocity Estimation Method Based on Cepstrum and Matched Filter

In this paper, a multi-highlight target echo model is investigated, and the target is considered to be of a constant relative velocity to the sonar. The echo is usually equivalent to the superposition of reflections from single highlight. Due to the relative motion of the active sonar and the target, the echo is stretched or compressed. Moreover, noise and reverberation are existed in the underwater environment. All of the above degrade the performance of the target velocity estimation. As a nonlinear signal operation method, cepstrum can change the convolution operation in time domain into the summation operation in the cepstrum domain. Based on the good performance of cepstrum operation in detecting periodic signals, a cepstrum-based target velocity estimation method is proposed. In this method, a combined pulse signal is transmitted, and the received echo is successively subjected to Matched Filter and cepstrum operation, the position of the peak in the cepstrum domain is searched to estimate the velocity of the target. Simulation results demonstrated that the proposed method can estimate the target velocity precisely at low signal-to-noise ratio using small computational effort.
Multimodal Stacked Cross Attention Network for Fine-Grained Fake News Detection

Fake news is usually disseminated in a multimodal form, which incorporates natural language, visual language, and so on. Therefore, many deep learning approaches are proposed to detect multimodal fake news. However, a drawback of existing methods is that they simply fuse unimodal features and ignore the latent semantic alignment of image and text modalities. In this paper, we propose a novel Multimodal Stacked Cross Attention Network (MSCA) to better align and fuse multimodal token-level textual and visual features for fake news detection. Experiments conducted on two publicly available datasets show that our method can significantly improve performance compared with other models. Furthermore, experimental analysis shows that MSCA can effectively align and fuse token-level features of multiple modalities.
Data Approach to Biometrics in Cybersecurity with Related Risks

This paper will research the implications that biometrics have on cybersecurity. The paper will introduce topics such as what biometrics are, the recent advancements in it, and how biometric data is stored. The paper will then proceed to discuss privacy implications of biometric authentication, how it is implemented at an organizational level, its cost, and the performance impacts of using it. Subsequently, the paper will delve into what models and systems are currently used for biometric authentication and the risks posed by using it. The risks posed in paragraphs prior will be addressed in their own section, and the advantages will be presented to be weighed against the risks. The paper will also include discussions on how biometric data can be stored securely and processed using technology like cancellable biometrics. The paper will present the findings, and recap arguments brought forward throughout the paper.
Perception of and Response to a Haptic Device as a Function of Signal Complexity

Haptics devices have been developed in a wide range of form factors, actuation methods, and degrees of freedom, often with the goal of communicating information. While work has investigated the maximum rate and quantity of information that can be transferred through haptics, these measures often do not inform how humans will use the devices. In this work, we measure the differences between perception and use as it relates to signal complexity. Using an inflatable soft haptic display with four independently actuated pouches, we provide navigation directions to participants. The haptic device operates in three modalities, in increasing order of signal complexity: Cardinal, Ordinal, and Continuous. We first measure participants’ accuracy in perceiving continuous signals generated by the device, showing average errors below $5 ^{o}$. Participants then used the haptic device in each operating mode to guide an object towards a target in a 2-dimensional plane. Our results indicate that human’s use of haptic signals often lags significantly behind the displayed signal and is less accurate than their static perception. Additionally signal complexity was correlated with path efficiency but inversely correlated with movement speed, showing that even simple design changes create complex tradeoffs.
Visual Place Recognition Datasets for Indoor Spaces

Visual place recognition (VPR) is widely cast as a challenging image retrieval problem. Recently, many studies in this area have achieved superior results. However, most existing works consider outdoor spaces rather than indoor spaces. To fill this gap, this paper for the first time constructs a benchmark based on realistic environments for indoor VPR, involving three new indoor datasets which contains 25, 233 RGB images in total. These datasets cover typical indoor environments with over 250 places and provide a wide range of challenging cases. Moreover, this paper introduces a patch relation module based on spatial coordinate position of image patches and a global average pooling pyramid to get discriminative and robust features. Extensive experiments are conducted on our datasets to validate the effectiveness of the proposed method. The results indicate that indoor VPR in realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be released at https://github.com/Dauntless-Wind/indoor-visual-place-recognition.
Fully Adaptive Constant Module Sequence Set Design for Waveform-Agile Coherent Radar System in the Presence of Signal-Dependent Clutter

With increased degrees of freedom of the transmitter, waveform-agile radar system can get performance improvement by changing its transmission on-the-fly in response to target detection's requirement. In literature, the existing cognitive radar transmit waveform design is based on a single dimension. In order to make full use of the degree of freedom and power of the transmission signal, this paper studies the two-dimensional transmission waveform optimization problem, and optimizes the transmission waveform in both fast and slow time dimensions. We first construct a new waveform-agile model called fully adaptive constant mode sequence set (FACMSS), and then implement two-dimensional waveform optimization by shaping the ambiguity function redefined on the adaptive matched filter bank(AFA) in the presence of signal-dependent clutter, and finally propose a joint inter and intra-pulse modulation optimization algorithm (JIIMOA) to solve the problem. Numerical examples show the feasibility and effectiveness of the proposed method.
Design and Simulation of an Electromagnetic Actuator for a Linear Ultra-fast Disconnector (UFD) with Mechanical Stress Consideration

Due to the development of DC power systems and the growth of microgrids, fast DC Circuit Breakers (DC CB) play an essential role in fault current limitation. In need of low energy consumption and high speed, Thomson Coil (TC) is used in Ultra-Fast Disconnectors (UFD) of Hybrid CBs (HCB). In this paper, a TC and its driver are designed and modeled. Then, the stress of UFD components at the collision moment is studied by obtaining the travel curve and the collision velocity. The mechanical endurance of UFD is evaluated according to the IEC62271-103 standard based on the stress (von Mises Stress) applied to its various parts, especially when the armature collides with the endpoint. Finally, appropriate options for mechanical standardizing or classification of UFD structure are proposed.
Hospitality Industry 4.0 - Customize and Optimize Real-Time Allocation of Rooms

In the modern era of Industry 4.0, where customization and offerings play a major role in the commercial world, different industries have implemented various realistic approaches to enable their customers to choose the products with available preferences and priorities. Industries like retail have enabled their customers to choose their products based on their preferred colors, sizes, and materials; automobile industry has enabled their customers to pick their own colors, materials for interiors, add-ons for safety features etc., for their chosen vehicles. Equivalently, different entities in the hospitality industry have also made several attempts to meet the customers' expectation of customizing their products (i.e., rooms) with the available preferences and priorities (i.e., room amenities and hotel properties), but their systems are being unable to cope with the complex operational challenges in accommodating rooms at business or transit hotels that have a large room inventory and an average occupancy of more than 50% per day. To overcome the functional challenges like the transfer process of guests (customers), their timeline and turnaround process of rooms, we have invented an efficient system having 3 main components: 1.The Room Type Assignment System helps guests to get into their desired room types along with their preferred amenities. 2.The Check-In Check-out (CICO) Machine Learning Prediction Model helps hotels to get all the rooms that are available to be mapped to the guests at the time of check-in. It also provides those rooms whose booking gets canceled and are now available to get mapped again to any other guest. 3.Looping Method for Room Number Assignment helps in real-time allocation of a preferred room type to the guest along with the room number based on his/her preferences and priorities. Hence, our proposed system will not only offer magnificent experiences to our guests and improve hotel room occupancy rates but also commencement of doors to more enhanced operational models for the hospitality industry.