Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Pilot Studies of the Edgeless Preclinical PET Insert: ScintoTube II

Efforts towards further improving the 3D spatial resolution and sensitivity of pre-clinical (i.e., for small animals) Positron Emission Tomography (PET) systems have been pursued during the last years. Most of the available pre-clinical PET systems are built of multiple detectors arranged in a cylindrical geometry with unavoidable gaps (in both axial and transaxial axis) between detectors. These gaps decrease detection sensitivity. Moreover, independently of the scintillator technology employed (monolithic or pixelated), the finite size of the detector modules deteriorates the photon impact determination specially, close to the edges.Aiming to reduce inter-module gaps and edge-effects, we introduced recently a gapless/edgeless scanner design, the so-called ScintoTube I. This system consisted of a single LYSO annulus with a cylindrical inner diameter but 10 outer facets to simplify mounting of standard flat SiPM arrays. The overall performance of the system was acceptable but, the junctions of the facets disrupted the light distribution pattern. To improve performance, we have designed a second prototype, named ScintoTube II, in which both outer and inner facets are cylindrical. The present proceeding summarizes the mechanical design and preliminary evaluation of the ScintoTube II.
Capacity multistage degradation analysis and knee point prediction method of Lithium-ion battery

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline rate and the peak value of increment capacity curve (IC curve), the capacity degradation was divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. Moreover, a method based on neural network was proposed to predict the knee point. Two features were used to predict the capacity and cycle of the knee point, which were the slope of the capacity degradation curve and the difference of the IC curve with the maximum correlation. Hence, the experimental results showed that the position of the knee point could be accurately predicted using only the first 100 cycles of the battery, which leaded to the accurately prediction of the knee point.
Gate-Oxide Degradation Monitoring of SiC MOSFET Based on the Third Quadrant Characteristics

In this paper, a condition monitoring method for gate oxide degradation of trench silicon carbide (SiC) power MOSFET is presented. In this method, the body diode voltage at the inflection point of the output characteristic curve of the third quadrant SiC MOSFET at zero gate voltage is used as the characteristic parameter. The current monitoring methods of electrical parameters are often inaccurate due to coupling junction temperature, or cannot achieve on-line monitoring due to intrusiveness, but the proposed method can ignore the effect of junction temperature, and provides a good foundation for on-line condition monitoring.
Optimized Fuzzy Logic Controller for a Bipedal Walking Robot

This paper proposed a control strategy that enables precise walking behavior for a bipedal robot. The system is a bipedal robot composed of five principal links and actuated by two motors per leg (hip, knee) to walk in a circular planar dimension. Due to the complexity of determining, with high precision, the equations of the dynamics describing the robot, a hybrid fuzzy logic controller was designed and optimized with the Particle Swarm Optimization (PSO) algorithm. The control strategy consists of a tracking controller (torque) and state controller (walking phase: set the goal angle) per motor. The proposed fuzzy rules systems were established experimentally and are simple and easy to apply. The values of the input and output fuzzy sets and the desired positions were optimized using the PSO algorithm to guarantee that the robot can walk for two hours without falling while keeping the torso of the robot vertical. The system was evaluated through simulation, reaching four times the optimal walking time (8 hours and 30 minutes) without falling and traveling a distance of seven kilometers using simple fuzzy controllers.
Enhancing IoT Security Through AI-Based Anomaly Detection and Intrusion Prevention

Enhancing the Internet of Things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. The difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like Machine Learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. This enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. The outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863.
Capacity Degradation Prediction of Lithium-ion Battery Cycling with Various Protocols

Lithium-ion batteries are important for electrical energy storage and have been widely used in many scenarios like electric vehicles and battery energy storage systems. Capacity degradation prediction is critical for improving both the safety and economics of lithium-ion battery systems. However, the prediction is still challenging, since the battery health condition can not be measured directly while the capacity degradation is slow and non-linear. In this work, a complete set of capacity degradation forecast models is proposed. The capacity self-recovery phenomenon is reconsidered and its relationship to cycling protocols is revealed. Remaining useful life predictor is designed to estimate the rate of degradation and capacity self-recovery phenomenon is applied to build an attention-based model. The proposed method is studied in a real-world dataset consisting of 124 cells cycling with various protocols. In the quantitative comparison, the proposed method exhibits better overall performance over existing ones.
Temporal trends in reef fish assemblages inside Virgin Islands National Park and around St. John, U.S. Virgin Islands, 1988-2006

This report is a result of long-term fish monitoring studies supported by the National Park Service (NPS) at the Virgin Islands National Park since 1988 and is now a joint NPS and NOAA collaboration. Reef fish monitoring data collected from 1988 to 2006 within Virgin Islands National Park (VINP) and adjacent reefs around St. John, U.S. Virgin Islands (USVI) were analyzed to provide information on the status of reef fishes during the monitoring period. Monitoring projects were initiated by the National Park Service (NPS) in the 1980s to provide useful data for evaluation of resources and for development of a long-term monitoring program. Monthly monitoring was conducted at two reef sites (Yawzi Point and Cocoloba Cay) starting in November 1988 for 2.5 years to document the monthly/seasonal variability in reef fish assemblages. Hurricane Hugo (a powerful Category 4 storm) struck the USVI in September 1989 resulting in considerable damage to the reefs around St. John. Abundance of fishes was lower at both sites following the storm, however, a greater effect was observed at Yawzi Point, which experienced a more direct impact from the hurricane. The storm affected species differently, with some showing only small, short-term declines in abundance, and others, such as the numerically abundant blue chromis (Chromis cyanea), a planktivorous damselfish, exhibiting a larger and longer recovery period. This report provides: 1) an evaluation of sampling methods, sample size, and methods used during the sampling period, 2) an evaluation of the spatial and temporal variability in reef fish assemblages at selected reef sites inside and outside of VINP, and 3) an evaluation of trends over 17 years of monitoring at the four reference sites. Comparisons of methods were conducted to standardize assessments among years. Several methods were used to evaluate sample size requirements for reef fish monitoring and the results provided a statistically robust justification for sample allocation.
Development Of Deep Neural Network Model For Images Remotely Sensed Through Muti-Image Super Resolution Technique

This presents an outstanding chance for the field of remote detecting to acquire extra information and data from caught information. Nonetheless, most of as of late distributed endeavors have focused on the Single-image Super-resolution issue. In this paper the examination introduced here proposes a Residual Attention Multi-image Super-resolution network (RAMS) that effectively finishes the Multi-image Super-resolution challenge by at the same time interfacing different pictures in light of spatial and temporal connections. To beat the restrictions of the neighborhood district of convolutional activities and accomplish cognizant information combination and data extraction from various low-resolution pictures, we present the instrument of visual component consideration utilizing 3D convolutions. In addition to using established residual associations to stream superfluous low-recurrence signals and focus the calculation on more significant high-recurrence parts, our depiction learning network uses various contributions with the same scene. The suggested substantial learning-based technique is leading-edge for distant identification applications, as shown by extensive testing and comparisons with existing open strategies for single or multi-image super resolution. Although CNN models outperform other multi-image super-resolution models quantitatively, our test results demonstrate that while RAMS models appear to perform well, they fall short of other models when it comes to measuring picture quality.
Human-Road Dual Trust Mechanism in Adaptive Distributed Shared Control Framework under Lane Keeping Scenario

As intelligent systems that consider the driver in the loop (DiL), advanced driver-assistance systems (ADASs) have been widely applied in intelligent vehicles to improve driving safety and comfort. To provide more driving pleasure while maintaining driving safety, this paper researches the ADAS platform and proposes a human-road dual trust (HRDT) mechanism to achieve the adaptive distributed shared control under the lane keeping scenario. Differing from the existing trust mechanisms that only focus on driving information, road geometric information is introduced to assess the driving risk more comprehensively by using the fuzzy logic algorithm in the proposed mechanism. Moreover, a novel indicator is designed to provide the integrated measurement of the lateral deviation and the autopilot dependence. Finally, a hierarchical validation process with the driver-in-the-loop platform is applied to prove that the HRDT mechanism helps reduce the autopilot dependence significantly while keeping a minor lateral deviation to maintain driving safety.
A Predictive Method of Crosstalk Peak Considering Packaging Parameters for SiC MOSFETs

Silicon carbide-based metal-oxide-semiconductor field effect transistor (SiC MOSFET) device has been widely applied due to its lower on-resistance and parasitic parameters compared with silicon (Si) device. However, unexpected crosstalk phenomenon occurs and could lead to false turn-on or gate-oxide damage of the devices. In this article, a predictive method of crosstalk peak is proposed to provide theoretical guidance for crosstalk suppression, which can improve the reliability of the systems. Firstly, the generation of crosstalk is illustrated. Secondly, the influence of packaging parameters on crosstalk prediction and measurement is discussed. Furthermore, an improved model considering packaging parameters is proposed to obtained accuracy crosstalk peak. Finally, the crosstalk peaks predicted by the proposed method are compared experimentally with the actual measured values. The results have verified the proposed method can achieve accurate crosstalk peak prediction.
A Novel One-Transistor Active Pixel Sensor With In-Situ Photoelectron Sensing in 22 nm FD-SOI Technology

For the first time, a novel active pixel sensor (APS) with 22 nm fully depleted silicon-on-insulator (FD-SOI) technology is experimentally demonstrated. The APS in-situ integrates photo sensing, charge integration, buffer amplification, and random access in one transistor without charge transfer and assistance of additional transistors. The deep depletion effect in the substrate of the SOI MOSFET is used to collect photoelectrons which are then sensed by the top Si channel. A sensor array is proposed and high sensitivity is experimentally verified in a simplified circuit.
Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses MLP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (MLP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an MLP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.
A Bi-Level Scheduling Strategy for EV Aggregator based on a Dynamic Pricing Mechanism under the Contract Service Mode

With the new round of power system reform in China, electric vehicles (EVs) have entered the power grid on a large scale. It has become a key issue to explore the potential for demand response (DR), in which the electric vehicle aggregator (EVA) plays an important role. In order to further establish an EVA service mode considering the individual needs and decision preferences of EV users, under which the balance of interests between EVA and EV can be realized, this paper puts forward an EVA contract service mode as well as a bi-level charging and discharging scheduling strategy based on dynamic pricing mechanism. The contract service mode includes contracts that match needs for different types of EVs: reservation contract, real-time contract and EV-emergency-transaction contract, which are different in scheduling authority, reservation demand and contract price. A charging-right trading strategy is designed to realize the benefit transfer between EVs. The dynamic pricing mechanism is regarded as the link of the guidance-response relationship of EVA-EV. In this paper, the game theory method is used to solve the joint optimal scheduling strategy. The simulation results show that the proposed strategy can successfully balance the interests of EVA and EV and effectively realize the power peak-load shifting of EVA. While meeting the personalized needs of EV users, it improves the flexibility, regularity and stability of EVA scheduling.
Polycystic Ovary Syndrome Detection using Deep Learning

Polycystic Ovary Syndrome (PCOS) is an endocrine disorder that is commonly found in women of reproductive age. The disorder is identified by the increase of androgen, oligomenorrhea, anovulation, and sometimes due to the presence of many ingrown follicles in the ovary or the cysts. This endocrine disorder does not allow the follicle to grow for ovulation. Anovulation in women leads to an increase in weight, hair on the face and neck, missed period cycles, and can also cause high blood pressure and diabetes in the coming years. An early diagnosis of PCOS will help in reducing the chances of in fertilisation, diabetes and heart disease in women and hence we have a proposed a methodology in this paper that will help in the early diagnosis of PC OS. We have used a dataset of 541 patients that was available on Kaggle. Then the deep learning model is trained with the dataset obtained from Kaggle and the comparison of the result was done with the DT, SVM and RF. The accuracy obtained by the deep learning classifier was 98.07% and when it was compared with other functional classifiers such as SVM, DT AND RF (accuracy lies between 83-96%) it was found that deep learning classifier achieved the highest accuracy. Hence, we can say that the proposed model provides an effective way of PCOS prediction. The result of the study confirms that our proposed DL model obtained the best accuracy rate among all the different classifiers used by different authors to predict PCOS. The accuracy of our proposed system can further be increased by including the omics data.
Fiber Optic Monitoring System Based on Totally Analog Circuitry for Safety Application at CERN

In this study, a novel monitoring system based on fiber optic technology is investigated. It is based on a full analog electronic circuitry and full passive optical devices, allowing to increase its robustness and reliability, without losing in terms of measurement accuracy. Due to these peculiarities, the conceived system satisfies the constrains imposed by the detector safety systems, a dedicated section in charge to control and handle anything is related to the equipment protection of experimental apparatus like the Large Hadron Collider (LHC) experiment at CERN. Experimental tests of the proposed system have been conducted in laboratory environment and the results, in terms of temperature variation, are shown. Two Fiber Bragg Grating sensors under test are employed, monitoring a deviation of 20°C with 1°C step, simulating a possible scenario in which the system will be employed in the next months.
Examining Social Media Posts for Identification of Anxiety and Depression Utilizing Machine Learning Techniques

The prevalence of mental diseases like depression and anxiety among students has become a growing concern in recent years. With the widespread use of social media platforms, individuals often express their emotions, thoughts, and experiences through posts and interactions. This paper investigates the feasibility of classifying students' anxiety and depression based on the content of their social media posts. Recently, some research communities in various domains have shown enthusiasm for investigating the potential of leveraging social media platforms for extracting valuable insights for decision-making and creative knowledge. Social media has been utilized to aid software development activities, analyze cryptocurrency trends, identify network communities, and make recommendations, among others. Apart from other fields within the eHealth domain, the application of social media platforms and the sophisticated analysis of extensive datasets to anticipate mental health disorders like depression, schizophrenia, eating disorders, anxiety, and addictive tendencies has garnered substantial attention and interest. However, one of the challenges lies in either having enough historical data or regular monitoring of a patient's activities to identify a person's mental health condition using traditional methods. To tackle this concern, we introduce an approach aimed at categorizing individuals with past mental health conditions through the utilization of information sourced from a widely-used social media platform such as Facebook.
Speed Planning Strategy in Urban Environments Considering Passenger’s Preferences, Traffic Signals and Road Limits

A personalized speed planning strategy for autonomous vehicles (AVs) is proposed in this paper, which can generate individual speed trajectories for passengers with different driving preferences in typical urban environments with multiple traffic signals, road terrain, and speed limits. Firstly, the K-means method is used to analyze and cluster the drivers’ data in the case of free driving (not following the front vehicle) in the NGSIM dataset, and then the actual longitudinal speeds and accelerations preference of different drivers’ groups in the urban environments can be obtained. Secondly, according to the information on road restrictions, traffic signals phase, and driving preferences of the passengers in AVs, a decision-making mechanism is designed to calculate the personalized traveling time of each traffic signal section, in which the preferences of speeds are transformed into the preferences of traveling time. Then, the dynamic programming (DP) algorithm and the bisection method are integrated to plan personalized speed trajectories for each traffic signal road section, with consideration of the factors such as preferred traveling time in this road section, road speed limits, passenger comfort, driving economy, etc. Finally, a typical urban scenario is designed for algorithm verification and performance evaluation. The test results show that the strategy can generate safe, comfortable, energy-saving, and personalized speed trajectories for different groups of passengers.
Extensive Analysis of Machine Learning Techniques in the Field of Heart Disease

CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that circulates filtered blood to each part of the body. The normal working of the heart is affected due to various conditions like Obesity, Hypertension, lifestyle etc. The large volume of heart diseases is avertable, but they continue to increase for the reason that protective measures are insufficient. To cure these life-threatening diseases, we need a more reliable and accurate system. Machine learning procedures have been employed on numerous therapeutic datasets to industrialise the investigation of medical data. Recently, many researchers have started applying a number of machine learning techniques to assist the medical community and experts in the detection of heart-related issues.Classification technique like Support Vector Machine,Artificial Neural Network and Random Forest are examples of data mining and machine learning approaches that are used to forecast heart disorders. This can help clinician to decide on the diagnosis and forecast of cardiac illness. The prime intention behind writing this research paper is to use machine learning procedures to forecast a patient's heart condition.