Miracle-dz/UniFAR-FTUs huggingface dataset:
Lung Cancer Detection on CT Scan Images with Deep Learning Methods: Sugeno Fuzzy Integral-Based CNN Ensemble Method

Lung cancer causes the highest mortality rate among all cancers. Existing methods for detecting Lung cancer are not optimal, as they rely mostly on a single model or multiple models employing probability averaging. This study is proposed to differentiate malignant, benign and normal cells of lung by employing the Sugeno Ensemble method in conjunction with four Convolutional Neural Networks (CNN): VGG11, SqueezeNet, GoogLeNet, and Wide ResNet-50-2 to improve accuracy of Lung cancer detection. Each model will be individually trained with the dataset before Sugeno ensemble is performed to produce the final accuracy. This study evaluates the accuracy of applying Sugeno fuzzy logic techniques to CT scan images of Lung cancer to determine if it outperforms existing methods and investigates the limitations of earlier research. This study examines the image of cancer morphologies from various patients. The confusion matrix is then utilized to evaluate the performance of the framework. The ultimate achieved accuracy rate is 98.47%. This study found that integrating the Sugeno ensemble technique with the four CNN models increases Lung cancer detection performance. These discoveries could aid in the development of systems to identify Lung cancer patients and provide implications for artificial intelligence and the medical industry.
A Hybrid Feature Extraction Technique for Optimized Motor Imagery Classification in BCI

Motor Imagery (MI) Classification is critical in Brain-Computer Interface (BCI) systems, allowing mental intentions to control external equipment. In this research, we propose a hybrid feature extraction strategy for optimal MI Classification in BCI. The technique uses Multi-Scale Principal Component Analysis (MSPCA) to denoise and improve the signal’s quality. Following that, the preprocessed signals are subjected to independent applications of the Power Spectral Density (PSD), Continuous Wavelet Transform (CWT), and Hilbert Transform (HT), with each transformation extracting distinct features. These features are then merged to create a complete feature set. AlexNet, a re-known and efficient deep learning architecture, is then used for MI task categorization, which has shown promising results. Experiment findings on a publicly available dataset show that our proposed technique works impressively, with an amazing classification accuracy of around 99.2%. This hybrid strategy has various advantages over traditional methods. First, including MSPCA improves signal quality, reducing the impact of noise and other artifacts on classification performance. Second, combining PSD, CWT, and Hilbert Transform features yields a very comprehensive representation of MI patterns that extracts both spectral and temporal information. Third, by exploiting the capabilities of AlexNet, a cutting-edge deep learning model, excellent classification accuracy is achieved by efficiently learning complicated patterns from the combined feature space. All of these benefits add up to make our hybrid feature extraction technique a highly viable solution for improving MI Classification in BCI systems.
Restoration of Face Mask-Induced Speech Intelligibility Degradation Via Neural Bandwidth Extension

Since coronavirus disease 2019 (COVID-19) has prevailed in some countries, it is still mandatory to wear face masks to prevent from spreading the viruses. However, the impact of wearing face masks creates distractions and interruptions in voice communication in terms of sound quality on both perceptual quality and especially intelligibility. To provide a solution to this problem, we employ deep learning-based bandwidth extension (BWE) technique with deep neural network to restore intelligibility. Furthermore, we conduct acoustic measurement of impulse responses of various face masks to analyze the degradation effect of wearing face mask on speech sounds. The experimental results have proven effective, with a noticeable enhancement in speech intelligibility compared to mask-induced noisy speech.
Study on the Transmitting Array for Ground Microwave Wireless Power Transmission System

A transmitting array design scheme for ground microwave wireless power transmission system is presented. To reduce the cost and simultaneously ensure a high transmission efficiency, a microstrip array based on subarrays technique is proposed. And the stepped amplitude distribution is adopted. The feeding network with the substrate integrated coaxial line technique is designed. With this structure, good isolation can be guaranteed. For the ground demonstration system associated the space solar power satellite, diameters of the transmitting and rectenna arrays are 1.6m and 7m, respectively. And they are separated by 100m. A high beam collection efficiency of 92.25% is achieved.
RL4NET++: A Packet-Level Network Simulation Framework for DRL-Based Routing Algorithms

As the rising complexity for network simulation and the development of deep reinforcement learning (DRL), existing network simulation frameworks cannot meet the high demand for DRL-based simulation. To solve this issue, we propose RL4NeT++, a packet-level network simulation framework based on OMNeT++ for DRL-based routing algorithms. To the best of our knowledge, RL4NeT++ is the first network simulation framework that takes asynchronous updating strategy into consideration and applies it for consistence to the training logic of DRL. In addition, RL4NeT++ has fast simulation and training speed, user-friendly interface, diverse network configurations, high modularization, convenient customization, and strong adaptability. We run extensive experiments to demonstrate the application of RL4NeT++ in DRL-based routing algorithm and prove the high simulation speed and strong adaptability of RL4NeT++.
Detecting Non-Injured and Injured Humans in Thermal Imaging using YOLOv8

Convolutional neural network (CNN) techniques have propelled You Only Look Once (YOLO) to the forefront of object detection. Although YOLO models yield superior performance, the detection rate diminishes in low-light conditions. This paper considers thermal imaging which can eliminate such limitation by capturing the infrared radiation of an object. A YOLOvS-based detection algorithm is proposed to distinguish between non-injured and injured humans. During an incident of mass casualty, it is beneficial to identify potential victims rather than locating all humans whether injured or not. Given the scarcity of infrared thermal imaging datasets in public repositories, a custom dataset of 2848 images is created, augmented, and annotated with the assistance of Roboflow. Subsequently, the pre-trained YOLOvS model undergoes transfer learning by leveraging the power of Google Colab. Armed with graphics processing unit (GPU), the solution is able to achieve a mean average precision (mAP)50:95 of 0.772 and processing time of 12.3 ms for the validation image set. The duration is almost doubled while executing the model over a thermal video, which represents more realistic situations.
Green Cover Identification and Hotspot Analysis

Global challenges such as urbanization and climate change have a significant negative impact on people’s health and well-being. The current regretful state of forests can be reversed step by step by simply spreading awareness about the situation. Trees must be a part of the global conversation if the people are to create thriving, healthy communities. To solve the forest related problems, a website has been implemented as an engagement tool, which also helps educating people about the current state of forests and encourage them to maintain the forest covers by analyzing the visual representation of forests. QGIS will be used along with geospatial data for making the forest maps. An appropriate QGIS Co-ordinate Reference System called as World Robinson Projection has been used for the mapping. The results found from the information analysis will help in getting results like the distribution of forests in the mapped area, forest type that dominates the area and the factors that are being affected by the presence of the dominating forest type.
An Analog Integrate-and-Fire Neuron with Robust Soft Reset Mechanism

This paper presents a novel soft reset technique for analog Spiking Neural Networks with minimal circuit complexity. The reset mechanism plays a vital role in SNNs, affecting the inference accuracy directly. Implementing the soft reset mechanism in analog SNNs has traditionally posed challenges due to the difficulties in subtracting static analog values, which is vulnerable to noise and variations. Utilizing the Schmitt trigger, the proposed reset mechanism changes the threshold voltage and the sign of the membrane potential accumulation, rather than implementing an analog subtractor. Compared to the conventional analog neurons, the proposed neuron is immune to error in passing the residual charge to next output spike generation.
Prototype Design and Testing of Electric Eddy Current Braking System with Flexible Braking Patterns

The function of the brake is crucial in an automotive. The conventional mechanical braking system has many limitations. In contrast, Eddy Current Braking (ECB) has several advantages. It can be employed as complementary to the traditional brake.With ECB, the vehicle’s speed can be decreased and with the mechanical brake it can be stopped. ECB can be employed for IC engine based vehicles as well as Electric Vehicles (EV). This paper discusses ECB and a hardware setup as a prototype is proposed. The arrangement consists of electromagnets and a power converter for the controlled establishment of flux. Design aspects of the braking system are provided. The tests are carried out and the results are presented. It’s observed that the braking profile can be controlled with the help of a converter. It’s observed that there are potential benefits of employing ECB in automobiles.
Performance Comparison of Clocked Comparators Using Impulse Sensitivity Function

The clocked comparator is a critical component in various systems, including Analog-to-Digital Converters (ADCs) and high-speed interfaces. Clocked comparators are regarded as linear periodically time-varying (LPTV) systems. The impulse sensitivity function (ISF) has been recognized as a well-established approach for characterizing the behavior of LPTV systems and facilitating performance analysis. In this paper, we characterize and compare the ISF of conventional and charge-steering based StrongARM comparators using periodic AC simulation. By employing this approach, we conduct a comparative analysis of key performance metrics, including sampling aperture, DC gain, and bandwidth. The insights and methodologies presented in this work contribute to the optimization of comparator designs and facilitate efficient decision-making processes.
Fruit and Vegetable Grading with Transfer Learning and Convolutional Neural Networks for Better Productivity

Grading provides quality assurance by confirming that agricultural products match predetermined standards, which can boost consumer trust in the goods. Additionally, it aids in ensuring that consumers receive products that are risk-free and error-free. In this study, a fruit and vegetable grading system based on deep learning methods such as transfer learning and convolutional neural networks (CNN) is proposed. The suggested approach correctly categorizes various fruit and vegetable varieties according to their outward appearance. Objective is to ascertain the effect of various grading parameters, such as size, shape, and color, on the grading system's correctness. This study utilized labeled image dataset for training models. Transfer learning is utilized to enhance the performance and accuracy of the grading system by leveraging pre-trained CNN models. The experimental findings show that the suggested system outperforms existing fruit grading systems and achieves excellent accuracy to comprehend how grading and standardization affect farming's profitability.
Vascular Intervention Surgical Robot Master-Slave Force Sensing Technology

In the context of current medical practice involving the use of existing master-slave vascular intervention surgical robots, there exists an inability for physicians to perceive the actual force exerted by their hands on the guidewire. This limitation restricts their ability to flexibly tighten or loosen the guidewire, potentially introducing safety concerns during surgical procedures. In light of this challenge, a master force sensing manipulation device based on the STM32 micro controller has been developed. The system’s hardware primarily comprises the STM32 micro controller, HC4067 sixteen-channel analog switch, 32-channel resistive pressure sensors, and a communication module. The system is integrated into the primary end of the surgical robot, enabling real-time measurement of the force applied by the human hand during guide wire manipulation. The collected data are then transmitted in real-time to an upper-level computer via a Bluetooth module and forwarded to the secondary-end controller using the TCP/IP protocol. Experimental results affirm the system’s stability and reliability, its capacity to effectively discern variations in force applied by operators of varying expertise levels, thereby establishing a foundation for the realization of human-machine interaction in subsequent developments of surgical robots.
Hyper-Space: Blockchain-based Decentralized Cloud Storage System

The emergence of cloud storage services have revolutionized the way we store, access, and share data. However, centralized cloud storage systems have inherent weaknesses such as security and privacy concerns, and a single point of failure. Decentralized cloud storage systems based on blockchain technology have emerged as a solution to these problems. In this paper, we present Hyper-Space, a blockchain-based decentralized cloud storage system that offers a secure, reliable, and efficient way to store and access data. Hyper-Space utilizes Swarm peer-to-peer network for data storage and retrieval, Polkadot blockchain network for metadata storage, and AES and ECC encryption algorithms for data security. The system is designed to enable renters to rent out their system’s storage space, and clients to access the storage service through a smart contract signed by both parties. Our experimental results demonstrate that Hyper-Space is a promising solution for decentralized cloud storage systems.
Integrating Multi-source Data for CO₂ Emission Prediction at City and County Level – A Case Study in Shanxi Province, China

Accurately estimating carbon dioxide (CO2) emission is a fundamental guarantee for implementing and evaluating regional carbon emission reduction strategies. Taking Shanxi Province as a case study, this paper investigates multi-source data: NPPNIIRS nighttime lights data, socioeconomic statistical data, and meteorological data, to achieve accurate prediction of CO2 emissions at the city and county level using a prediction model based on Random Forest algorithm. The RF model was trained and tested using the data from 2012 to 2017, and then applied to predict city-county level CO2 emission for 2018-2020. Comprehensive experiments have been conducted to compare the RF model with linear regression, Gaussian progress regression and support vector regression models, as well as to investigate the effect of multi-source data integration. In addition, the distribution and temporal trends of CO2 emissions in the cities and counties of Shanxi Province are analyzed. The main findings are: ❨1❩ Integrating multi-source factors could achieve a high prediction accuracy of CO2 emissions than using a single data source, and combining meteorological data with either nighttime lights indicators or socioeconomic factors can greatly improve the RF model performance; ❨2❩ The RF model with built-in feature importance determination can achieve good performance of CO2 prediction at the city and county level without feature selection; ❨3❩ From 2012 to 2020, the carbon emissions in Shanxi Province showed a trend of first decreasing and then increasing, and a total growth rate is approximately 15.4%. The city-county analysis of CO2 emissions can provide reference for the formulation of emission reduction plans in various regions of Shanxi Province.
Four Points that Provide a Solution in Railway Line Detection and Tracking

The novel use of the Euclidean distance between consecutive pixel intensity values is presented as a solution for railway line detection and tracking. The Euclidean distance between consecutive pixel intensity values, a searching strategy and a mathematical tool, in the form of collinearity testing, are the key elements of this method of railway line detection and tracking. The Euclidean distance between consecutive pixels’ intensity values defines edges. Four points are marked where significant changes in pixel intensity values occur on two horizontal lines across an image; two on each line. These points are used for detecting and tracking railway line tracks using the collinearity test of the cross product of two vectors. The four points are also used as the control points for correcting perspective distortion in an image, better known as transforming the image into a bird’s-eye view using the Top View Transformation model. Finally, the four points are used in calculating the changes in the pitch angle as the altitude of the railway line track bed rises and falls. The changes in the pitch angle are used in a model to stabilize the transformation of the image into the bird’s-eye view. The searching strategy, which is the cornerstone of this algorithm refers to the placement of the two lines across the image. An equation is presented that governs how far apart the two lines should be based on the curvature of the railway line track.
An Analytical Model for Domain-Specific Accelerator Deploying Sparse LU Factorization

The Lower-Upper (LU) factorization for sparse linear equations is the main bottleneck of many scientific computation problems, including the SPICE-like circuit simulation in modern VLSI design. However, the highly sparse and irregular data structure, the vast floating-point computing operations, and the strong data dependency are restricting the parallelism of LU factorization on general-purpose processors. Aimed to evaluate the acceleration potential of customized chips for LU factorization before implementation, we build an analytic model to estimate the performance of domain-specific accelerator deploying LU factorization. We first propose a Left-Right-Looking hybrid algorithm by analyzing the workloads of basic LU factorization algorithms to improve the utilization rate of process elements. Furthermore, we design a typical minimal domain-specific architecture (DSA) optimized for parallelism with strong floating-point computing power. Finally, we build an analytical model to estimate the performance while mapping the hybrid algorithm onto the DSA platform. Taking the UFMC sparse matrix collection as the benchmark, we list the estimated performance through MATLAB profiling. It can achieve two orders of magnitude speedup than the KLU/NICSLU solvers on CPU. The proposed model can be generalized for other accelerator exploration in the early design stage.
A Modeling Study: Applying Carbon-Based Interconnects to BS-PDN Architecture

In recent years, BS-PDN (Back-Side power delivery network with Buried-Power-Rails) has been proposed to meet the challenge of continuous scaling of integrated circuits with excellent PPA (power, performance, and area). However, the additional bonding layer and thin substrate introduced by BPR (Buried-Power-Rails) lead to poor heat dissipation, making it difficult to handle situations with severe heat dissipation requirements such as high-performance computing applications. In this paper, we explore the feasibility of applying Carbon-based interconnect to BS-PDN to improve its performance through modeling and simulation. Our results demonstrate that Carbon-based interconnects have comparable electrical characteristics to copper-based interconnects, ~20.4% better thermal performance, and ten times more reliability. However, it is not fully applicable for BS-PDN due to the limitation of the process and the large loss of current density. Furthermore, the interconnect combining Carbon and Cu shows a level of electrical characteristics comparable to the Cu-based interconnect and has better heat dissipation and reliability with little loss in current density, which is a potential solution.
A PAM-4 Baud-Rate CDR with High-Gain Phase Detector Using Shared Sampler

This paper proposes a pulse amplitude modulation-4 (PAM-4) baud-rate clock and data recovery circuit that improves phase detector (PD) gain by increasing transition density. The proposed idea is that one sampler (shared sampler) serves as a data sampler and an error sampler simultaneously to minimize the number of samplers. It can reduce power consumption while having a high phase detector gain. In addition, the accuracy of the early/late signal is improved through the pattern dependent phase detector. Simulation result shows power consumption of the proposed receiver is 0.79[pJ/bit] at -17.8dB channel loss. This work is designed using 65nm process.
Control of Multibody Systems in DAE Form Applied to a Cable Robot

: Certain types of multibody systems cannot be conveniently modelled using minimal coordinates. Then, redundant coordinates can be chosen to represent the system kinematics. This results in the equations of motion being described by differential-algebraic equations (DAEs). Control of systems described by DAEs is not straight-forward in practical applications. In this paper, application and implementation of a linear quadratic regulator is shown for under-actuated systems in DAE form. Therefore, the equations of motion are linearised around the desired state trajectory and transformed into ordinary differential equations using Maggi’s formulation with application of the QR decomposition. For implementation of the state feedback controller, measurements or estimates of the full state vector are required. For observer design, an unscented Kalman ﬁlter is proposed which is able to account for measurements of the algebraic coordinates. Application of the controller in a two-degree of freedom control structure is shown in simulations and experiments on an underactuated cable robot.
Multi-Stream TCP Design

The proposed Multi-Stream TCP (MS-TCP) transport protocol challenges the traditional one-to-one relationship between the Transport Control Protocol (TCP) connections and full-duplex streams. This paper explores the feasibility and advantages of allowing a single TCP connection to support multiple streams through distinct sockets, with a focus on enhancing the efficiency of concurrent web transactions. The MS-TCP protocol addresses the increasing demand for efficient utilization of TCP network connections in today’s network interoperation, dominated by HTTP-based transactions, offering a legacy-aware architecture alternative to the disruptive HTTP/3 QUIC solution. By relaxing the strict connection-stream relationship, MS-TCP introduces the concept of “channel”, a single TCP-based connection that can accommodate multiple concurrent streams. The paper presents a detailed examination of the design, architecture, and implementation choices involved in realizing the requirements of MS-TCP. These requirements include adherence to POSIX Standard APIs, seamless integration with existing network middleboxes, mitigation of Head-of-Line (HoL) blocking issues, enabling zero roundtrip time, and skipping the slow-start state when activating new streams.