Basic Architecture Research Articles

Integrating AI with Green Internet of Things in Healthcare for Achieving UN’s SDGs

The integration of the Green Internet of Things (Green-IoT) and Artificial Intelligence (AI) in healthcare has the potential to revolutionize a wide range of industries, including healthcare wherein Green-IoT-connected medical devices and wearables can collect health data, which can be analyzed by AI algorithms to improve patient outcomes and support better decision-making by healthcare professionals. The convergence of AI and IoT in the field of smart health coupled with machine learning algorithms are enabling new and innovative solutions for healthcare delivery and management. AI algorithms and machine learning techniques can be used to analyze vast amounts of data generated by IoT devices, such as wearable devices, sensors, and smart home health devices, to provide insights into patient health and well-being. This research presents a review of patients’ healthcare services. Particularly, we first give an overview of essential parameters of patients’ healthcare services through Green-IoT-enabled sensor technologies under the use case scenario. We then present a basic architecture for IoT-based healthcare systems considering key requirements in the light of the UN’s Sustainable Development Goals discussing their strengths and weaknesses in the context of the framework for patients’ healthcare services. Finally, we explored various security threats for AI-based architecture and their solutions with a comprehensive methodology to design robust and resilient patients healthcare services system needed in the context of the UN’s sustainable development goals.

Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and mAP@.5:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in mAP@.5:.95. The model has shown robustness and effectiveness in accurately detecting various pests.

Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process.

Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing the mechanical performances of the produced parts. Henceforth, this research proposes an algorithm to identify the best injection gate (IG) location/s starting from a 3D model and a user-defined load case. The procedure is composed of a first Visual Basic Architecture (VBA) code that automatically sets and runs Finite Volume Method (FVM) simulations to find the correlation between the fiber orientation tensor (FOT) and the IG locations considering single and multiple gates combinations up to three points. A second VBA code elaborates the results and builds a dataset considering the user-defined loading and constraint conditions, allowing the assignment of a score to each IG solution. Three geometrical components of increasing complexity were considered for a total of 1080 FVM simulations and a total computational time of ~390 h. The search for the best IG location has been further expanded by training a Machine Learning (ML) model based on the Gradient Boosting (GB) algorithm. The training database (DB) is based on FVM simulations and was expanded until a satisfactory prediction accuracy higher than 90% was achieved. The enhancement of the local FOD on the critical regions of three components was verified and showed an average improvement of 26.9% in the stiffness granted by a high directionality of the fibers along the load path. Finite element method (FEM) simulations and laboratory experiments on an industrial pump housing, injection-molded with a polyamide-66 reinforced with 30% of short glass fibers (PA66-30GF) material were also carried out to validate the FVM-FEM simulation frame and showed a 16.4% local stiffness improvement in comparison to the currently employed IG solution.

Deep Learning for Medical Image-Based Cancer Diagnosis.

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Supervised Single Channel Speech Enhancement Method Using UNET

This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time Fourier transform (STFT) is exploited on the noisy time domain signal to build a noisy time-frequency domain signal which is called a complex noisy matrix. We take the real and imaginary parts of the complex noisy matrix and concatenate both of them to form the noisy concatenated matrix. We apply UNET to the noisy concatenated matrix for extracting speech components and train the CNN model. In the testing phase, the same procedure is applied to the noisy time-domain signal as in the training phase in order to construct another noisy concatenated matrix that can be tested using a pre-trained or saved model in order to construct an enhanced concatenated matrix. Finally, from the enhanced concatenated matrix, we separate both the imaginary and real parts to form an enhanced complex matrix. Magnitude and phase are then extracted from the newly created enhanced complex matrix. By using that magnitude and phase, the inverse STFT (ISTFT) can generate the enhanced speech signal. Utilizing the IEEE databases and various types of noise, including stationary and non-stationary noise, the proposed method is evaluated. Comparing the exploratory results of the proposed algorithm to the other five methods of STFT, sparse non-negative matrix factorization (SNMF), dual-tree complex wavelet transform (DTCWT)-SNMF, DTCWT-STFT-SNMF, STFT-convolutional denoising auto encoder (CDAE) and casual multi-head attention mechanism (CMAM) for speech enhancement, we determine that the proposed algorithm generally improves speech quality and intelligibility at all considered signal-to-noise ratios (SNRs). The suggested approach performs better than the other five competing algorithms in every evaluation metric.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI). Pulmonary technetium-99m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes. The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001). The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.

Metadata Management and Asset Exchange in the Agricultural Data Ecosystem of the Project Agri-Gaia

AbstractThe particularities of the agricultural ecosystem of cooperating partners suggest a highly distributed, open data architecture to support the exchange and re-use of data and data-driven applications. We describe the data strategy and the federated basic architecture of the Agri-Gaia ecosystem. We present an ontology-based approach to metadata management which extends a bucket store for arbitrary data storage by an RDF-based metadata graph store and employs widely-used domain ontologies as a conceptual basis. Data and service providers are free to extend the describing metadata at any time, according to their needs. The resulting set of interconnected platforms supports the publication, retrieval and secure sharing of exposable data under full control of their owners.

Exploring the Architecture and Its Influence of Badshahi Mosque Lahore (Bmlh): A Unesco Tentative Heritage Site

Asia has a variety of diverse ancient cultures and civilizations. In this paper, we studied the architecture of a UNESCO tentative heritage site: Badshahi Mosque Lahore (BMLH, 1671–1673), built by the Mughal emperor Aurangzeb Alamgir (AA). The site is special since it had the largest mosque title (human capacity = 100,000) in the world from 1673 to 1986, has great architectural importance, and is one of the most popular tourist attractions in the world. Various mosque architectures (MA) emerged over the past 1400 years worldwide to preserve each region's diversity, identity, and culture. A documentation visit was arranged to study this site and feel the real architecture of the Mughals after decades of preserved form. The study found that BMLH underwent significant alterations while keeping the original MA. This unique study further focused on the basic architecture of the Mughal Era. A detailed study of the architectural themes of BMLH showed that the architectural style of this heritage included regional, cultural, and traditional impacts. By analyzing the architectural themes and distinctive features of BMLH, we have uncovered the distinct regional, religious, and psychological influences that led to Mughal-era mosque architecture's development. Further documentation was reported about the current structural situation of Badshahi Mosque; the number of seepages has been pointed out, and several decays were sighted during our visit. For this significant site's perfect preservation, it was proposed that high-end preservation strategies be implemented in the future.

Structural organization of the rat hyppocampal formation

The review examines the historical aspects of the hippocampus nomenclature. Its anatomical study began by the ancient Greeks, who called the unusual–looking structure a “ram’s horn” – cornu ammonis. The term “hippocampus” (ancient Greek: ἱππόκαμπος, from ἵππος, “horse” and κάμπος, “sea monster”, or “sea-horse”) was first introduced in the sixteenth century by anatomist J.C. Arantius. The term “hippocampal formation” is currently applied to a group of cytoarchitectonically different adjacent areas, including, along with the hippocampus itself, the dentate gyrus, subiculum, presubiculum, parasubiculum and entorhinal cortex. The reason for including these six regions in the “hippocampal formation” group is that they are connected to each other by unique and largely unidirectional pathways. The review is devoted to the spatial, morphological and cyto- and myeloarchitectonic organization of all departments of the rat hippocampus formation and the distinctive neuroanatomic characteristics of its departments. Comparative features of the structure of the hippocampus formation of a rat, monkey and human are described. Although the volume of the hippocampus is about 10 times larger in monkeys and 100 times larger in humans compared to rats, the basic architecture of the hippocampus formation is common, although there are some species differences. The relatively simple organization of the main cellular layers in combination with the highly organized laminar distribution of hippocampal neuron processes contributes to its use as a model system in modern neuroscience.

Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way

Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a hurdle. In this work, we propose an optical diffractive convolutional neural network (ODCNN) that is capable of performing image processing tasks in computer vision at the speed of light. We explore the application of the 4f system and the diffractive deep neural network (D2NN) in neural networks. ODCNN is then simulated by combining the 4f system as an optical convolutional layer and the diffractive networks. We also examine the potential impact of nonlinear optical materials on this network. Numerical simulation results show that the addition of convolutional layers and nonlinear functions improves the classification accuracy of the network. We believe that the proposed ODCNN model can be the basic architecture for building optical convolutional networks.

Tourism architecture design strategy in tourism area planning based on G-DINA model

Abstract Tourism architecture is an important material guarantee for carrying out tourism activities in scenic spots and is an important part of the tourism system. This paper analyzes the theory and structure of the G-DINA model in detail. To conceptualize the geospatial tourism architecture, form a conceptual cognition, and establish a basic geospatial tourism architecture cognitive model. Then the G-DINA model is brought into the basic cognitive model, and cognitive judgments are made using observation of tourism building features to form a G-DINA-based geospatial cognitive model of tourism buildings for planning and design of scenic tourism buildings. Experimental proof: By comparing the number of tourists in a tourist area for two months, it is obvious that the number of tourists in the second month has increased by 592 compared with the number of tourists in the first month, which is 160% of the number of tourists in the first month, and the comprehensive index of tourism economy has increased from 0.08 to 0.5, with an annual growth of 8.3%, showing a gradual upward trend in general. Accordingly, it can be considered that the attractiveness and competitiveness of tourist areas are significantly improved through the rational planning and design of tourist buildings. This verifies that reasonable design of tourism architecture and scientific planning of tourism areas can strengthen the harmony between tourism architecture and local style and surrounding environment, highlight regional characteristics and let them be inherited, and promote tourism development.

Recognition and location of marine animal sounds using two-stream ConvNet with attention

There are abundant resources and many endangered marine animals in the ocean. Using sound to effectively identify and locate them, and estimate their distribution area, has a very important role in the study of the complex diversity of marine animals (Hanny et al., 2013). We design a Two-Stream ConvNet with Attention (TSCA) model, which is a two-stream model combined with attention, in which one branch processes the temporal signal and the other branch processes the frequency domain signal; It makes good use of the characteristics of high time resolution of time domain signal and high recognition rate of frequency domain signal features of sound, and it realizes rapid localization and recognition of sound of marine species. The basic network architecture of the model is YOLO (You Only Look Once) (Joseph et al., 2016). A new loss function focal loss is constructed to strengthen the impact on the tail class of the sample, overcome the problem of data imbalance and avoid over fitting. At the same time, the attention module is constructed to focus on more detailed sound features, so as to improve the noise resistance of the model and achieve high-precision marine species identification and location. In The Watkins Marine Mammal Sound Database, the recognition rate of the algorithm reached 92.04% and the positioning accuracy reached 78.4%.The experimental results show that the algorithm has good robustness, high recognition accuracy and positioning accuracy.

Basic RISC-V Instruction Set Architecture: Design and Validation

Abstract: This project's primary goal is to design and implement a simple RISC V instruction set architecture. This paper offers insights into the architecture of the risc v instruction set. This system employs the RISC V R-type (register) type instruction format. Using this format, we designed the fundamental isa and tested its functionality using verilog code. There is no licence fee for using RISC V, an open source isa that is available to everyone. Reduced instruction set (RISC) computers are created to make the individual instructions given to computers to perform various tasks more manageable. Most instruction set architectures, or isas, are proprietary and cannot be used or modified without permission from the companies; as a result, an isa that is free and open source, which is provided by risc v, will help in increasing the speed and lowering system costs by using these instruction set formats, and we are designing 32 bit isa architecture. instruction formats for the risc v instruction set architecture

Ideological and Political Resources Service System Needs Analysis

The ideological and political education in universities has shifted from political courses to professional courses, and from teacher ideological and political education to full staff ideological and political education. Many departments and individuals at Beijing University of Technology have accumulated a certain amount of ideological and political resources. In order to achieve more centralized management and greater sharing of dispersed ideological and political resources, and to make ideological and political achievements more widely applied, the school needs an ideological and political resource service system. The establishment of a system helps to strengthen the management of ideological and political resources in schools and enhance individuals' knowledge level of ideological and political resources. It is crucial to implement a standardized and practical system and connect it with the ideological and political resources of different departments and individuals. This article focuses on the requirements analysis section of the ideological and political resource system, as requirements analysis is the cornerstone of almost all software design and development processes. Without a complete process of requirements research, analysis, and modeling, it is impossible to construct the basic architecture and functions of the software, resulting in the final designed software not meeting the user's usage requirements. A good requirement analysis will enable developers to have a more accurate understanding of the specific functions of the system, arrange development time and schedule, and thereby improve the usability of the system and the completion efficiency of developers. This article provides an in-depth analysis of the ideological and political resource service system from both functional and non-functional aspects, laying a solid foundation for subsequent design and implementation of the system.

Smart Warehousing as a Wave of the Future

Background: The unprecedented supply chain disruptions caused by the prolonged COVID-19 pandemic forced many firms to change their way of doing business dramatically. These changes include quickly responding to the growing demand for online orders and the corresponding direct shipments to customer locations. These changes have been further accelerated by rapid technological innovations resulting from the fourth industrial revolution (Industry 4.0). One of the most notable technological transformations that we have witnessed is the growing popularity of smart warehousing concepts. Although smart warehousing may represent a wave of the warehousing future, the published literature rarely documents its underlying principles, specific application targets, and potential impacts on supply chain performance. This research aims to identify key drivers of the digital warehousing revolution and describe important value propositions for warehousing automation. Methods: To help companies develop smart warehouses successfully as an integral part of a supply chain link, I conceptualize an ideal smart warehousing system, design its basic architecture, propose specific milestones for monitoring the progress of smart warehouse development, and then, identify critical success factors for its full utilization in today’s volatile warehousing environment. This paper employed qualitative content analysis to conceptualize smart warehousing development and establish a smart warehousing framework. Results: A smart warehouse will bring many managerial benefits, including warehousing cost efficiency, labor productivity, and agility in the era of the knowledge economy. Conclusions: This paper will enable companies to accelerate digital transformation and improve their competitiveness amid the post-pandemic industrial revolution.

Predicting chaotic dynamics from incomplete input via reservoir computing with (D+1)-dimension input and output.

Predicting future evolution based on incomplete information of the past is still a challenge even though data-driven machine learning approaches have been successfully applied to forecast complex nonlinear dynamics. The widely adopted reservoir computing (RC) can hardly deal with this since it usually requires complete observations of the past. In this paper, a scheme of RC with (D+1)-dimension input and output (I/O) vectors is proposed to solve this problem, i.e., the incomplete input time series or dynamical trajectories of a system, in which certain portion of states are randomly removed. In this scheme, the I/O vectors coupled to the reservoir are changed to (D+1)-dimension, where the first D dimensions store the state vector as in the conventional RC, and the additional dimension is the corresponding time interval. We have successfully applied this approach to predict the future evolution of the logistic map and Lorenz, Rössler, and Kuramoto-Sivashinsky systems, where the inputs are the dynamical trajectories with missing data. The dropoff rate dependence of the valid prediction time (VPT) is analyzed. The results show that it can make forecasting with much longer VPT when the dropoff rate θ is lower. The reason for the failure at high θ is analyzed. The predictability of our RC is determined by the complexity of the dynamical systems involved. The more complex they are, the more difficult they are to predict. Perfect reconstructions of chaotic attractors are observed. This scheme is a pretty good generalization to RC and can treat input time series with regular and irregular time intervals. It is easy to use since it does not change the basic architecture of conventional RC. Furthermore, it can make multistep-ahead prediction just by changing the time interval in the output vector into a desired value, which is superior to conventional RC that can only do one-step-ahead forecasting based on complete regular input data.

Software system of automatic identification and distributed storage of patient medical data

Due to the rapid development of information technologies, informatization in the medical industry is essential. The main component of electronic health care is medical information systems designed for the accumulation, processing, analysis and transmis-sion of medical data. In the medical field, specialized software products are used to per-form diagnostic studies, process the results of laboratory tests, and make decisions at the stage of establishing a diagnosis. The use of mobile devices in medical information systems is developing. However, the degree of automation of processes in the provision of medical services and the protection of the personal and medical data of patients is still insufficient. The purpose of the research is to create a basic architecture of a software system that would simplify the process of developing software for automated input, processing, search and confidential patient access to their medical data in a medical information system based on multi-color barcoding of information using mobile devices. The architecture of the software system is proposed, in which, based on the princi-ples of distribution, anonymization, and data ownership, a patient can provide access to medical personnel to their medical data by reading a multi-color interference-resistant barcode from one smartphone (patient’s) by the camera of another smartphone (doctor’s). It is shown that in order to ensure the reliability of such transmission, it is neces-sary to use an interference-resistant barcode, which would ensure the integrity of the data in the conditions of possible distortion of the barcode image (change in lighting, scanning angle, trembling of the operator's hand, blurring or skewing of the image, etc.). The use of mobile devices for the barcode method of transmission and processing of data allows providing the protected electronic co-operating of a patient and a doctor both directly and remotely. It guarantees high reliability and confidentiality of the ex-change of data. The proposed technical solutions make it possible to improve the quality of medi-cal care and strengthen the protection of the patient's medical data.

Innovative reform strategy of college music education art practice based on information fusion technology

Abstract This paper is designed to improve the efficiency and quality of teaching and learning efforts and to meet the multidimensional information needs of music educators and learners. From the perspective of information fusion technology, we build a multi-source information fusion system based on the basic system architecture. The maximum likelihood, least squares, and D-S algorithms were used to calculate and construct the sampling evaluation index equations. A survey of the teaching methods used in music teaching in colleges and universities was conducted to analyze three strategies needed for college education reform: innovative teaching methods, strengthening teachers’ music competence and quality, and establishing feedback mechanisms for college music teaching evaluation. The combined survey results show that 72% of music teachers believe their teaching skills have been greatly enhanced, while students’ willingness to take music lessons after using information integration technology has all risen to over 80%.Therefore, in the process of music teaching in colleges and universities, the integration of information integration technology can not only improve students’ interest in learning but also improve teachers’ teaching ability, which provides a certain degree of reference for the reform of music teaching in colleges and universities.

Common Mechanism for Detecting Multiple DDoS Attacks

An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems.