Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Instrument Polyphonic Melody Transcription Based on Deep Learning

Automatic music transcription (AMT) is an important task in the field of music information retrieval (MIR). Polyphonic melody transcription remains a challenging problem. Existing neural network models for music transcription are generally tailored to a single instrument, such as a piano or a guitar. In this work, we develop a methodology for polyphonic melody transcription. It is a neural network with high resolution output and high performance which can not only to transcribe polyphonic melodies, but also label the corresponding instrument. We investigate its performance on several datasets as well robustness in a simulated noise experiment.
Combined Location Method Based on Error State Kalman Filter

Based on the data fusion method of iterative Error State Kalman Filter framework, this paper realizes the combined location of optical and inertial navigation. The method uses a tight coupling combination, uses the measurement value of Optical Position Sensor (OPS) to the marked point to correct the prior estimation of IMU, and takes the data error of OPS and IMU as the observation and state variables, thus simplifying the calculation of Jacobian matrix and avoiding the complexity caused by nonlinearity. The experiment shows that this method can effectively improve the positioning accuracy, enhance the robustness of OPS, and has good practicability.
Research on False Information Detection Based on Multimodal Event Memory Network

recent years, with the rapid development of the Internet, a large amount of false information has been widely disseminated on public social media platforms, thus misleading readers and causing serious negative effects. How to accurately identify and detect false information has become a major concern of the public and government. The existing false information detection methods focus on the simple splicing of text and image features, without considering the interaction between different modalities and the rich deep semantic information behind the text. In addition, most false information detection models do not work well on emerging events. Therefore, this paper proposes a false information detection method based on multi-modal event memory network(MEMN). Specifically, text features are extracted through BERT and Text-CNN, image features are extracted using the VGG-19 network, feature interactions of multiple modalities are captured through an attention mechanism, and finally an event memory network is used to mine connections between false information and events. A large number of experiments are carried out on the public microblog dataset. The experimental results show that the accuracy of this method is about 7 percentage points higher than that of other single-modal and multi-modal baseline methods such as EANN, and the generalization ability of the model is improved.
Vmin Prediction Using Nondestructive Stress Test

We propose a novel minimum operating voltage (Vmin) prediction method using nondestructive stress test to avoid area overhead and reduce test time. We process stress-test faillogs and generate summation values by three strategies to predict Vmin. In addition, we select important test patterns by Spearman correlation and simulated annealing to reduce test time. Two regression models are adopted in our experiment. Experimental results on advanced 7nm and 3nm chip designs show that the average RMSE of our predict Vmin can be as low as 4.48 mV to 8.66mV by one strategy with linear regression model. Our method gives smaller RMSE than process monitor prediction methods with no area overhead, and is 50 to 62.5 times faster compared to conventional testing.
Split Additive Manufacturing for Printed Neuromorphic Circuits

Printed and flexible electronics promises smart devices for application domains, such as smart fast moving consumer goods and medical wearables, which are generally untouchable by conventional rigid silicon technologies. This is due to their remarkable properties such as flexibility, non-toxic materials, and having low-cost per area. Combined with neuromorphic computing, printed neuromorphic circuits pose an attractive solution for these application domains. Particularly, the additive printing technologies can reduce large amount of fabrication complexities and costs. On the one hand, high-throughput additive printing processes, such as roll-to-roll printing, can reduce the per-device fabrication time and cost. On the other hand, jet-printing can provide point-of-use customization at the expense of lower fabrication throughput. In this work, we propose a machine learning based design framework, that respects the objective and physical constraints of split additive manufacturing for printed neuromorphic circuits. With the proposed framework, multiple printed neural networks are trained jointly with the aim to sensibly combine multiple fabrication techniques (e.g., roll-to-roll and jet-printing). This should lead to a cost-effective fabrication of multiple different printed neuromorphic circuits and achieve high fabrication throughput, lower cost, and point-of-use customization.
Edge Computing for IoT Security: Integrating Machine Learning with Key Agreement

The Internet of Things (IoT) is one of the most widely used technologies today, the number of IoT connections growing by 18% to 14.4 Billion active endpoints globally. Thus, with such a large scale IoT application, security becomes the focus. The issue of intrusion and interception has been a controversial and much disputed subject within the field of IoT security. The main limitation of IoT security, however, is a single security technology cannot ensure security of the IoT system. The Internet of Things requires diverse technologies applied at different layers to work together to ensure security. Based on these problems, proposed a security solution with integrity is necessary. Edge computing is a great way to solve security problems for resource-constrained devices. This paper proposes a machine learning and cryptography combined IoT security scheme at edge computing, achieves monitoring and detection abnormal behaviors of IoT system and automatically responds at an early stage. Designed a lightweight key management scheme without third-party involvement. The combination of active detection and passive protection for IoT security is achieved. Meanwhile, data for this study were collected using Edge-IIoTset dataset for the prediction of IoT security threats in edge computing networks. In this paper, experiments offers some important insights into the model selection for the prediction of time series data. The models selected include Support Vector Machine(SVM), K-Nearest Neighbor(KNN), and Long Short-Term Memory(LSTM) models. The significance of the comparison of several models is to assess their accuracy in detecting IoT communication data. To compares the performance of models, the following performance evaluation parameters were used: Precision, Recall and F1-Score. This study has identified LSTM has better precision in time series data such as communication data, 10% more precision than the other models used in this paper.
Semantic SLAM Based on Compensated Segmentation and Geometric Constraints in Dynamic Environments

Most of the existing slam algorithms are designed based on the assumption of a static environment, this strong assumption limits the practical application of most slam systems. The main reason is that moving objects will cause feature mismatch in the pose estimation process, which in turn affects the accuracy of localization and mapping. In this paper, we propose a SLAM algorithm in a dynamic environment. First, we use the BlendMask network to detect potential moving objects to generate masks for dynamic objects. The geometrically constrained joint optical flow method is used to detect dynamic feature points. Secondly, aiming at the failure of semantic segmentation network segmentation, a missed detection compensation algorithm based on the invariance of adjacent frame speed is proposed. Finally, a keyframe selection strategy is proposed to construct a semantic octree graph containing only static objects. We evaluate our algorithm on TUM RGB-D and real scene datasets. The experimental results show that the algorithm has high accuracy and real-time performance.
An Improved Hard Thresholding Pursuit Algorithm for Compressive Sensing

The tail- ℓ1 minimization model greatly improves the sparse signal recovery ability compared with ℓ1 minimization model. However, solving the tail- ℓ1 minimization problem requires high computational cost and a lot of time. The hard thresholding pursuit (HTP) technology is attractive due to its computational efficiency in practice. Inspired by the HTP technology, the HTP technology is considered to be an efficient technique to solve the tail- ℓ1 minimization problem. This paper introduces an improved HTP technology, namely tail-HTP. The tail-HTP technology retains the computational simplicity of the HTP technology and greatly improves the efficiency of solving the tail- ℓ1 minimization problem. In addition, the tail-HTP technology also improves the sparse signal recovery ability of the HTP technology. The experimental results verify the high efficiency and superior sparse signal recovery ability of the tail-HTP technology.
Asphalt Mix Master Curve Construction Using Sigmoidal Fitting Function with Non-Linear Least Squares Optimization

This paper presents a new simplified method of constructing a master curve of asphalt mix using test data covering a large range of temperatures from –18°C to 55°C. It utilizes sigmoidal fitting function and compressive cyclic (complex) modulus test data obtained at matrix combination of different frequencies and test temperatures. In the master curve construction, the time temperature superposition was modeled two different ways. First, using known time-temperature superposition equations, and second, shilling test data experimentally, i.e., not assuming any functional form for the time-temperature relationship. The master curve construction was done using an Excel spreadsheet with the solver function, which is a tool for performing optimization with non-linear least squares regression technique. The analysis of over sixty mixtures indicated that the experimental approach agreed the best with the Arrhenius shifting equation, correlation coefficient R 2 being 0.922. Also, the experimental shifting was most flexible, producing the best fit among the studied shifting equations due to the fact that it has the most degree of freedom.
Silent Speech Recognition using EEG Signals *

Patients suffering from paralysis, and neuro-muscular diseases are unable to communicate. Hence, there is a need for an alternative way of communication. This research work has tried to address this issue using Electroencephalograph (EEG) signals. EEG is the recording of electrical activity produced by the firing of neurons within the brain. However, EEG recordings are always contaminated with artifacts, which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. For this, a fresh EEG dataset with six words is collected from 10 subjects. The artifacts which contaminate the quality of EEG data are removed and empirical mode decomposition is used to decompose EEG signals into various intrinsic mode functions. Linear and nonlinear timedomain features are extracted from the modes. A feature set is obtained by selecting highly discriminant features using the analysis of variance test. Classification is performed using seven recent machine learning algorithms.
An Asynchronous Power Optimization Method for Microprocessor Based on The Event-Matching Mechanism

Aiming at the high power consumption caused by clock circuits in the synchronous processor, this paper proposed an asynchronous power optimization method based on the event-matching mechanism. It first partitioned events based on the processor behavior. Secondly, it designed the asynchronous control path based on the event-matching mechanism. Finally, it used the asynchronous control path instead of the clock circuit to drive the processor. The verification results based on this method show that the total power of the target processor is reduced by 52 % on Xilinx Artix-7 FPGA, and the total power is reduced by 87% under the 180nm CMOS technology. Furthermore, this method has the best performance per watt compared with other related works.
An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.
Driving behavior recognition method based on trajectory data detected by millimeter wave radar

In this paper, a multi-step vehicle driving behavior recognition method based on roadside millimeter-wave radar detecting trajectory data is proposed. The time and vehicle radial speed in trajectory data are selected as characteristic parameters. By analyzing the characteristic parameters of vehicles, different vehicle driving behaviors are classified. The proposed method marks and judges single driving behaviors, continuous driving behaviors, and complete driving behaviors in the state of RA (rapid acceleration), RD (rapid deceleration), GA (general acceleration), GD (general deceleration), and CS (constant speed) by calculating acceleration, interval time, and duration. The identification of vehicle driving behavior is completed finally. Using the vehicle trajectory data of continuous traffic flow scenarios at urban signal intersections as a sample, the established recognition method is applied for recognition and the results are compared with the actual driving behaviors of the sample. The identification results are consistent with the driving behavior reflected by the sample time-speed variation curves. It shows that the identification method proposed in this paper can effectively identify five types of driving behavior, and the accurate identification results of vehicle driving behavior have significance for traffic safety and traffic congestion improvement decision-making.
Autonomous Hyperloop Control Architecture Design using MAPE-K

In the very recent past, there has been a trend for passenger transport towards electrification of the vehicles to reduce greenhouse gas emissions. However, due to the low energy density of battery technology, electrification of airplanes is not possible with current technologies. Here, Hyperloop systems can offer a climate-friendly alternative to short-haul flights but face some technical challenges to be resolved. In contrast to conventional rail systems, the Hyperloop concept uses magnetic propulsion and levitation to operate and has no physical contact with the environment. Consequently, mechanical backup solutions do not suffice to avoid catastrophic events in case of failure. Software solutions must, therefore, ensure fail-operational behavior, which requires autonomous adaptability to uncertain states. The MAPE-K approach offers a solution to achieve such adaptability. In this paper, we present a hierarchical architecture that combines the MAPE-K concept with the Simplex concept to achieve self-adaptive behavior. We impose our autonomous architecture on the controller design for the levitation system of a Hyperloop pod and show that this controller, designed using our methodology, outperforms a conventional PID controller by up to 76%.
Communication Device for Converting Text to Braille language for Visually Impaired People

The primary goal of this paper is to offer a comprehensive solution to the various communication issues that visually impaired people face daily. Technologies are constantly evolving, particularly in the area of contact via mobile phones, which is essential. The only part of the message application that the visually impaired can view is the tactile surface of the display. However, the system's quality is poor because blind people are unable to clearly recognize the text. As of now, impaired people can only use Braille technology for reading. By employing this Braille system, the proposed method makes it feasible for people who are blind to read messages and respond to them. Blind persons can use the mobile messaging programme in the same way as other users. For responding back a keypad is used, where the reply message is sent one-by-one by pressing each key. With the keypad options, the user is able to send information through GSM module a message.
SAGERoute: Synergistic Analog Routing Considering Geometric and Electrical Constraints with Manual Design Compatibility

Routing is critical to the post-layout performance of analog circuits. As modern analog layouts need to consider both geometric constraints (e.g., design rules and low bending constraints) and electrical constraints (e.g., electromigration (EM), IR drop, symmetry, etc.), it becomes increasingly challenging to investigate the complicated design space. Most previous work has focused only on geometric constraints or basic electrical constraints, lacking holistic and systematic investigation. Such an approach is far from typical manual design practice and can not guarantee post-layout performance on real-world designs. In this work, we propose SAGERoute, a synergistic routing framework taking both geometric and electrical constraints into consideration. Through Steiner tree based wire sizing and guided detailed routing, the framework can generate high-quality routing solutions efficiently under versatile constraints on real-world analog designs.
Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach’s feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively.
Anti-counterfeiting Method of CAN Terminal Based on Device Physical Fingerprint

With the development of science and technology, the electronic control unit (ECU) equipment has gradually become complex, and the security of the controller area network (CAN) bus protocol as the standard protocol for ECU communication has also been higher. In recent years, researchers have conducted extensive research on the security of the CAN bus, such as the anti-counterfeiting attack mechanism based on intrusion detection technology and the authentication code technology, which has improved the security of the CAN bus. However, directly modifying the CAN protocol itself based on intrusion detection technology will cause insufficient backward compatibility and a large number of devices cannot be reused; The authentication code-based approach cannot trace the origin of counterfeit devices and forensic evidence of attack devices, so further improvements are needed. In view of the above two problems, this paper proposes a CAN terminal anti-counterfeiting attack method based on physical fingerprint, which calculates the autocorrelation difference between the collected original signal and the calculated reconstructed signal, and obtains the physical fingerprint of the device as the basis for CAN terminal identity authentication, thereby achieving anti-counterfeiting attack. In this paper, 30 CAN terminals are used to experimentally verify the method in this paper, collect and transmit random data and transfer fixed data CAN frames to form two sets of data sets, and train and test using linear discriminant models, and the final model can identify the device identity rate on the two data sets up to 99%.
GV Black Inspired Hierarchical Multiclass Classification using Panoramic Radiographic Synthetic Data

The purpose of this research is to demonstrate that using the generated synthetic dataset will produce better results than the original teeth x-rays and thus be more efficient for carrying out hierarchical multiclass classification, this will also allow dental caries classification to be automated in accordance with the GV Black Standards. Five distinct models that were trained and tested using both the new and original datasets are compared. As the first step in each of these tasks, simple object detection will be used to identify each tooth, and then hierarchical classification will be used to achieve the desired results for classifying the cavities so that appropriate treatment can be determined based on the class of caries.
FPGA-Based Accelerator for Rank-Enhanced and Highly-Pruned Block-Circulant Neural Networks

Numerous network compression methods have been proposed to deploy deep neural networks in a resource-constrained embedded system. Among them, block-circulant matrix (BCM) compression is one of the promising hardware-friendly methods for both acceleration and compression. However, it has several limitations; (i) limited representation due to the structural characteristic of circulant matrix, (ii) limitation of the compression parameter, (iii) need to specialize the dataflow for BCM-compressed network accelerators. In this paper, rank-enhanced and highly-pruned block-circulant matrices compression (RP-BCM) framework is proposed to overcome these limitations. RP-BCM comprises two stages: Hadamard-BCM and BCM-wise pruning. Moreover, a dedicated skip scheme is introduced to processing element design for exploiting high-parallelism with BCM-wise sparsity. Furthermore, we propose specialized dataflow for a BCM-compressed network on a resource-constrained FPGA. As a result, the proposed method achieves parameter reduction and FLOPs reduction for ResNet-50 in ImageNet by 92.4% and 77.3%, respectively. Moreover, the proposed hardware design achieves $3.1\times$ improvement in energy efficiency on the Xilinx PYNQ-Z2 FPGA board for ResNet-18 on ImageNet compared to the GPU.