Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels of temporal granularity provides a temporal perception of the current surgical scene during RAS, which is a crucial step towards many automated surgeon-assisting functionalities. We propose Hierarchical Estimation of Surgical States through Deep Neural Networks (HESS-DNN), a deep learning-based system that concurrently estimates the current super- and fine-grained states. HESS-DNN incorporates endoscopic vision, robot kinematics, and system events data from the da Vinci Xi surgical system. HESS-DNN is evaluated on a real-world robotic inguinal hernia repair surgery dataset: HERNIA-20, and achieves accurate state estimates of both surgical superstate and the corresponding fine-grained surgical state. We show that HESS-DNN improves state-of-the-art fine-grained state estimation across the entire HERNIA-20 RAS procedure through its hierarchical design. We also analyze the relative contributions of each input data type and HESS-DNN's design to surgical (super)state estimation accuracy.
Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

Global environmental changes, nuclear power risks, losses in the electricity grid, and rising energy costs are increasing the desire to rely on more renewable energy for electricity generation. Recently, most people prefer to live and work in smart places like smart cities and smart universities which integrating smart grid systems. The large part of these smart grid systems is based on hybrid energy sources which make the energy management a challenging task. Thus, the design of an intelligent energy management controller is required. The present paper proposes an intelligent energy management controller based on combined fuzzy logic and fractional-order proportional-integral-derivative (FO-PID) controller methods for a smart DC-microgrid. The hybrid energy sources integrated into the DC-microgrid are constituted by a battery bank, wind energy, and photovoltaic (PV) energy source. The source-side converters (SSCs) are controller by the new intelligent fractional order PID strategy to extract the maximum power from the renewable energy sources (wind and PV) and improve the power quality supplied to the DC-microgrid. To make the microgrid as cost-effective, the (wind and PV) energy sources are prioritized. The proposed controller ensures smooth output power and service continuity. Simulation results of the proposed control schema under Matlab/Simulink are presented and compared with the super twisting fractional-order controller.
Dynamic RGB-D SLAM Based on Static Probability and Observation Number

This article proposes a simultaneous localization and mapping (SLAM) method for RGB-D cameras in dynamic scenes, which can effectively overcome the influence of dynamic objects and improve the pose estimation accuracy of RGB-D cameras in dynamic scenes. To detect the feature points on the dynamic objects, a dynamic feature point detection method is proposed, which is based on double K-means clustering. Then, a static weight is established for each feature point in the current frame to represent the probability that the feature point is static. The static weight is composed of static probability and static observation number (SON). Finally, to make the traditional random sample consensus (RANSAC) algorithm more suitable for dynamic scenes, the proposed method improves the RANSAC algorithm. Experimental results show that the proposed method can effectively improve the pose estimation accuracy of the RGB-D camera in dynamic scenes.
Manipulating Avatars for Enhanced Communication in Extended Reality

Avatars are common virtual representations used in Extended Reality (XR) to support interaction and communication between remote collaborators. Recent advancements in wearable displays provide features such as eye and face-tracking, to enable avatars to express non-verbal cues in XR. The research in this paper investigates the impact of avatar visualization on Social Presence and user’s preference by simulating face tracking in an asymmetric XR remote collaboration between a desktop user and a Virtual Reality (VR) user. Our study was conducted between pairs of participants, one on a laptop computer supporting face tracking and the other being immersed in VR, experiencing different visualization conditions. They worked together to complete an island survival task. We found that the users preferred 3D avatars with facial expressions placed in the scene, compared to 2D screen attached avatars without facial expressions. Participants felt that the presence of the collaborator’s avatar improved overall communication, yet Social Presence was not significantly different between conditions as they mainly relied on audio for communication.
Modeling Co-Channel Interference in the THz Band

Terahertz (THz) wireless technology is envisioned to enable terabit-per-second (Tbps) and secure transmissions in sixth-generation (6G) communication networks and has attracted attention from academia and industry in recent years. Because the transmission range of THz radios is restricted compared to that of microwave radios, frequency reuses in the THz band become much more flexible and even possible among transceiver pairs in close proximity. However, without appropriate spatial arrangement and coordination, the frequency reuse in the THz band can also lead to severe co-channel interference and result in a low signal-to-interference-plus-noise ratio (SINR) or a signal-to-interference ratio (SIR), which finally degrades signal detection and network reliability. To thoroughly study the co-channel interference in the THz band, we model the co-channel interference by the compound channel model and analyze it in detail. The adopted channel model captures the key features of THz communication, such as, spreading loss, molecular absorption loss, and dynamic shadowing, which is much different and complicated than those used in the low-frequency band. The resulted SINR and SIR are investigated by approximating the sum of co-channel interference as a gamma distribution. The generalized analytical results are also reduced to specialized forms for two special cases, i.e., the single-interferer case and the case of multiple independent and identically distributed (i.i.d.) interferers. Due to the generalized nature of the THz interference model constructed in this paper, the results play a meaningful role in practical implementation and can be easily extended to advanced performance analyses for THz communication systems.
A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews

Social media and online reviews have changed customer behavior when buying fashion products online. Online customer reviews also provide opportunities for businesses to deliver improved customer experiences. This study aims to develop fashion style models, based on online customer reviews from e-commerce systems to analyze customer preferences. Topic Modeling with Latent Dirichlet Allocation (LDA) was performed on a large collection of online customer reviews in different categories to investigate customer preferences by building fashion style models in a semantic space. Online product review data from Amazon, one of the leading online shopping websites globally, and Rakuten, one of the representative online shopping websites in Japan, were used to reveal the hidden topics in the review texts. The obtained topic definitions were manually examined, and the results were used to build computational models reflecting semantic relationships. The obtained fashion style models can potentially help marketing and product design specialists better understand customer preferences in the e-commerce fashion industry.
A Novel Islanding Detection Technique Based on Event Index Value for Reconfigurable Microgrid

This article presents an islanding detection technique based on event index value, which is derived from the superimposed components of sequence impedance with the consideration of all the operating configurations of the microgrid. Voltage and current signals are acquired at the terminal of distributed generators to compute the impedance. The proposed technique successfully discriminates the islanding, and non-islanding events (NIE) under different operating conditions, including zero power mismatch, and hence eliminates the non-detection zone and unwanted tripping due to the various types of NIEs. The detection was performed within half cycle for all the conditions. The proposed technique has been illustrated on a 7-bus reconfigurable microgrid test system. A large number of non-islanding and islanding events have been simulated for a range of power mismatches. The proposed technique has also been verified on the modified IEEE 34-bus system. The obtained results have been compared with the existing techniques reported in the literature, which proves the supremacy of the proposed technique. The effects of sampling frequency and noise have also been studied.
Traffic Light Control Using Hierarchical Reinforcement Learning and Options Framework

The number of vehicles worldwide has grown rapidly over the past decade, impacting how urban traffic is managed. Traffic light control is a well-known problem and, although an increasing number of technologies are used to solve it, it still poses challenges and opportunities, especially when considering the inefficiency of the popular fixed-time traffic controllers. This study aims to apply Hierarchical Reinforcement Learning (HRL) and Options Framework to control a signalized vehicular intersection and compare its performance with that of a fixed-time traffic controller, configured using the Webster Method. HRL combines the ability to learn and make decisions while taking observations from the environment in real-time. These capabilities bring a significant adaptive power to a highly dynamic problem. The test scenarios were built using the SUMO simulation tool. According to our results, HRL presents better performance than those of its own isolated sub-policies and the fixed-time model, indicating a simple and efficient alternative.
LIDAR-based Stabilization, Navigation and Localization for UAVs Operating in Dark Indoor Environments

Autonomous operation of UAVs in a closed environment requires precise and reliable pose estimate that can stabilize the UAV without using external localization systems such as GNSS. In this work, we are concerned with estimating the pose from laser scans generated by an inexpensive and lightweight LIDAR. We propose a localization system for lightweight (under 200 g) LIDAR sensors with high reliability in arbitrary environments, where other methods fail. The general nature of the proposed method allows deployment in wide array of applications. Moreover, seamless transitioning between different kinds of environments is possible. The advantage of LIDAR localization is that it is robust to poor illumination, which is often challenging for camera-based solutions in dark indoor environments and in the case of the transition between indoor and outdoor environment. Our approach allows executing tasks in poorly-illuminated indoor locations such as historic buildings and warehouses, as well as in the tight outdoor environment, such as forest, where vision-based approaches fail due to large contrast of the scene, and where large well-equipped UAVs cannot be deployed due to the constrained space.
A new Bi-level programming model for uncertain supply chain problems

This paper focuses on a two-level supply chain coordination problem consisting of a single supplier and multiple manufacturers under an uncertain environment. Firstly, a new bi-level programming model is proposed. Secondly, an innovative two-stage stochastic planning is combined into the lower-level manufacturer model, and a Progressive Hedging Algorithm (PHA) is designed to solve it. Then, the solved solution is substituted into the upper-level model to find the optimal solution of the bi-level programming. Finally, numerical experiments are used to verify the feasibility of the model and the algorithm.
Learning-Based Force Control of a Surgical Robot for Tool-Soft Tissue Interaction

Nowadays, robots are increasingly used in various surgery applications. Meanwhile, in many of these applications, the operation tools of surgical robots need to be in contact with human soft tissues. Furthermore, this tool-soft tissue interaction brings great challenges to robot control and system performance due to the nonlinearity, viscoelasticity, and uncertainties of the soft environment. To address these challenges and achieve the desired interaction behavior, a learning-based force controller for a surgical robot, which consists of a feedforward plus feedback controller, a radial basis function neural network-based controller, and an adaptive proportional–integral-type sliding mode control-based compensator, is presented in this letter. To display the stability of the proposed controller, the control system of the robot is analyzed through the Lyapunov method. Finally, several experiments are carried out in the robot prototype and the results illustrate that good tracking performance and guaranteed robustness can be obtained by the proposed controller.
Performance Analysis of Deep Learning Algorithms for Intrusion Detection in IoT

Due to the wide availability of IoT devices at affordable cost and the ease of use has increased IoT devices increased usage. Due to the enormous usage of the Internet of Things (IoT) devices, the security aspects related to the data are also a significant concern in this data-driven world. Negligence of security measures from users can result in severe data falsification or data thefts. In this scenario, the Intrusion Detection System has a pivotal role in IoT security. Incorporating the deep learning techniques is an effective way to predict various attacks, either known or unknown. This paper highlights the various security threats associated with IoT, the importance of deep learning in IoT intrusion detection, and various IoT intrusion detection systems using deep learning. Comparative analysis of the different deep learning techniques was performed. The results have shown Convolution Neural Networks gave high accuracy in prediction based on various evaluation metrics.
Two Ways for Early Detection of a Stroke Through a Wearable Smart Helmet: Signal Processing vs. Electromagnetism

Brain stroke treatments are strongly time-dependent. Indeed, the literature stresses the need for quick diagnosis to guarantee the most effective therapy with the famous motto “time is brain” [1]. Numerically speaking, a crucial goal for strokes is trying to achieve a door-to-needle time shorter than 60 minutes, called The Golden Hour [2], within which patients have the highest probability of a full recovery. This article presents a prototype of a Smart Helmet, a wearable device originally introduced in [3], [4], thought to be employed on site, to efficiently recognize and detect a brain stroke in a patient when the first aid medical team arrives. This work presents two different but complementary approaches: signal-processing-based and electromagnetic (EM). The former aims at outputting a two-level diagnosis: the presence (or not) of a stroke and (if it is present) of which type (ischemic or hemorrhagic), while the latter provides an accurate imaging of the brain that must be interpreted by medical staff. Both approaches are analyzed and compared, showing their strengths and highlighting their weaknesses.
Graphene Formation by Decomposition of C60

Graphene is formed through the thermally induced decomposition of C60 in combination with a Ni thin film. After transfer to a SiO2 substrate, optical microscopy and Raman spectroscopy confirm the presence of graphene for films of C60 buried under nickel or alternatively for C60 adsorbed on a nickel surface. For buried films the graphene thickness is shown to depend on the fullerene dosage, with evidence of domain growth from nucleation sites separated by tens of micrometers. The adsorption of C60 and the changes arising from annealing are also monitored using scanning tunneling microscopy. We relate our results to previous studies of the decomposition of C60 on nickel and other transition metals.
Gaussian Tracking With Kent-Distributed Direction-of-Arrival Measurements

This paper presents a Gaussian tracking algorithm with direction-of-arrival (DOA) measurements modelled via the Kent distribution. The key aspect of the algorithm is that the Kent distribution directly models the specific characteristics of DOA measurements in the 3-D space, and can account for different uncertainties in azimuth and elevation, which the von Mises-Fisher distribution cannot. At each update step, the algorithm performs iterated statistical linear regressions. We provide two implementations of the algorithms, one based on sigma-points and the other on analytical linearisation. The effectiveness of the approach is evaluated via numerical simulations.
Produção microbiana de lipideos

Neste trabalho foram desenvolvidos estudos visando o estabelecimento de um processo de producao de lipideos microbianos a partir de fontes renovaveis, particularmente xilose, carboidrato derivado do processo de hidrolise de bagaco de cana-de-acucar e que em breve devera estar disponivel para fermentacao. Inicialmente foi verificada a influencia da fonte de nitrogenio e a relacao inicial carbono-nitrogenio sobre o processo de acumulo delipideos utilizando duas linhagens caracteristicas. A utilizacao de sulfato de amonio juntocom extrato de levedura em uma relacao C/N de 50 g/g foi adequada para o processo de producao de lipideos. A seguir, 25 leveduras oleaginosas foram testadas quanto a sua capacidade de assimilacao de xilose e producao de lipideos. A produtividade de producao de lipideos e elevado rendimento energetico da levedura Lipomyces starkeyi DSM 70296 permitiram sua escolha para as etapas posteriores. Estudos de inibicao por substrato foram desenvolvidos e diferentes modelos matematicos foram ajustados aos dados experimentais. O modelo de Mulchandani representou adequadamente a cinetica de inibicao. Um modelo do cultivo em batelada simples foi desenvolvido baseado nos dados cineticos obtidos, o qual foi validado para cultivo com limitacao de nitrogenio em biorreator de dois litros. A analise de composicao dos lipideos obtidos mostrou semelhanca a de fontes vegetais. Finalmente, dois modos de operacao em batelada alimentada foram testados, o modo de alimentacao simples aumentou significativamente a produtividade do processo quando comparado ao processo em batelada simples convencional. A alimentacao usando pH-amonio/auxostat seguida de alimentacao de carbono dobrou a concentracao celular final obtida nos modos anteriores, mantendo niveis de acumulo aceitaveis. Assim, um processo de producao de lipideos a partir de pentoses foi viabilizado, adicionalmente o lipideo produzido atende as especificacoes necessarias do valor cetano para a producao de biodiesel%%%%Studies attempting the establishment of a microbial lipid production process from renewable resources, mainly xylose, were developed. This pentose, obtained from sugar cane bagasse hydrolysis, is expected to be available in large quantities and is going to be used as a substrate in fermentation processes in the near future. Initially, the effects of nitrogen source and initial carbon to nitrogen ratio over the lipid accumulation process were tested using two typical oleaginous strains. The joint use of ammonium sulphate plus yeast extract at an initial C/N ratio of 50 g/g were determined as adequate conditions for induction of lipid production with these yeasts. Subsequently in this work, 25 oleaginous yeasts were tested in order to evaluate the ability to consume xylose and lipid production. As a result of this stage, Lipomyces starkeyi DSM 70296 was selected regarding its highest productivity and energetic yield over all the set of yeasts evaluated. Thus, this strain was used in the development of the later studies. Substrate inhibition studies were…
Artificial Intelligence and Data Fusion at the Edge

Artificial intelligence (AI), owing to recent breakthroughs in deep learning, has revolutionized applications and services in almost all technology domains including aerospace. AI and deep learning rely on huge amounts of training data that are mostly generated at the network edge by Internet of Things (IoT) devices and sensors. Bringing the sensed data from the edge of a distributed network to a centralized cloud is often infeasible because of the massive data volume, limited network bandwidth, and real-time application constraints. Consequently, there is a desire to push AI frontiers to the network edge toward utilizing the enormous amount of data generated by IoT devices near the data source. The merger of edge computing and AI has engendered a new discipline, that is, AI at the edge or edge intelligence. To help AI make sense of gigantic data at the network edge, data fusion is of paramount significance and goes hand in hand with AI. This article focuses on data fusion and AI at the edge. In this article, we propose a framework for data fusion and AI processing at the edge. We then provide a comparative discussion of different data fusion and AI models and architectures. We discuss multiple levels of fusion and different types of AI, and how different types of AI align with different levels of fusion. We then highlight the benefits of combining data fusion with AI at the edge. The methods of AI and data fusion at the edge detailed in this article are applicable to many application domains including aerospace systems. We evaluate the effectiveness of combined data fusion and AI at the edge using convolutional neural network models and multiple hardware platforms suitable for edge computing. Experimental results reveal that combining AI with data fusion can impart a speedup of 9.8× while reducing energy consumption up to 88.5% over AI without data fusion. Furthermore, results demonstrate that data fusion either maintains or improves the accuracy of AI in most cases. For our experiments, data fusion imparts a maximum improvement of 15.8% in accuracy to AI.
An Optimal Control Strategy for LCL Tuned Inductive Power Transfer (IPT) Systems

Conventional dual and triple variable control approaches do not offer optimal performance for Wireless power transfer (WPT) systems based on inductive power transfer (IPT) technology, particularly for applications where the system is subjected to variations in the magnetic coupling, component values, and power levels. This paper proposes a four-variable optimal control strategy for LCL compensated (tuned) bidirectional IPT (BD-IPT) systems. The proposed control strategy, which ensures power regulation at maximum possible efficiency despite any changes in coupling and power levels, is described in detail, and a mathematical model is presented to investigate its performance. Simulated results of a 1kW BD-IPT system are also presented, benchmarking against conventional control methods, to demonstrate that the proposed control strategy offers superior performance even under large variations in system parameters.