A 5mW 1.8V Low Over-Sampling Ratio ΣΔ Modulator with 81dB Dynamic Range

This work demonstrates that the ΣΔ modulator with a low oversampling ratio is a viable option for the high-resolution digitization in a low-voltage environment.Low power dissipation is achieved by designing a low-OSR modulator based on differential cascade architecture,while large signal swing maintained to achieve a high dynamic range in the low-voltage environment.Operating from a voltage supply of 1.8V,the sixth-order cascade modulator at a sampling frequency of 4-MHz with an OSR of 24 achieves a dynamic range of 81dB for a 80-kHz test signal,while dissipating only 5mW.

Over-sampling algorithm for imbalanced data classification

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.

Treatment of Imbalance Dataset for Human Emotion Classification

Developments in biomedical science, signal processing technologies have led Electroencephalography (EEG) signals to be widely used in the diagnosis of brain disease and in the field of Brain-Computer Interface (BCI). The collected EEG signals are processed using Machine Learning-Random Forest and Naive Bayes- and Deep Learning-Recurrent Neural Network (RNN), Neural Network (NN) and Long Short Term Memory (LSTM)-Algorithms to obtain the recent mood of a person. The Algorithms mentioned above have been imposed on the data set in order to find out what the person is feeling at a particular moment. The following thesis is conducted to find out one of the following moods (happy, surprised, disgust, fear, anger and sadness) of a person at an instant, with an aim to obtain the result with least amount of time delay as the mood differs. It is pretty obvious that the accuracy of the output varies depending upon the algorithm used, time taken to process the data, so that it is easy for us to compare the reliability and dependency of a particular algorithm to another, prior to its practical implementation. The imbalance data sets that were used had an imbalanced class and thus, over fitting occurred. This problem was handled by generating Artificial Data sets with the use of SMOTE Oversampling Technique.

A New Approach for on-Line Blind Equalization and Channel Identification

A new approach for blind equalization and channel identification is proposed in this paper. The equalization scheme is based on over sampling technique and an independent component analysis network. The equalized sequence and its higher order statistics are used to identify the channel parameters. Compared to traditional equalization methods, the proposed approach is with a simple architecture, and does not need learning sequences. Computer simulations show the validity of the proposed method.

A precision sampling system for fast corrector power supply of photon source

Purpose At present,the high-energy photon source(HEPS)is under construction in Beijing.HEPS has beam emittance lower than 40 pm rad.In order to ensure low beam emittance,a high-performance fast orbit feedback system(FOFB)is designed for fast and accurate correction of beam orbit.The FOFB needs to have the smallest adjustment step.Therefore,as the execution unit of the FOFB system,the output current resolution of the fast corrector power supply needs to be as small as possible.In FOFB systems,precise correction of beam orbit is required for fast corrector power supply with output current resolution better than 60μA.A precision digital sampling system needs to be designed to meet the high requirements of output current resolution.Method The precision operational amplifier is used to complete the scaling and sampling of signals.The precision operational amplifier is used as the front-end processing in the circuit design to complete the amplitude processing and filtering.Meanwhile,the precision operational amplifier is used as the driver of the precision analog to digital converter(ADC)chip.A precision ADC chips based on oversampling technology is used.With this scheme,the selected ADC chip can have the advantages of both high speed and high precision.A simulation prototype is built for test,and the performance parameters of key chips in the design are given.Results A precision voltage reference is used to test the designed digital sampling system.The test results showed that the acquisition system has an effective resolution of 21.6 bits.The HEPS fast corrector power supply is used for testing the developed precision sampling system.The test result showed that the output current resolution of fast corrector power supply is lower than 16μA.