Carbon Nanomaterials Derived from Black Carbon Soot: A Review of Materials and Applications

Fabrication of highly hydrophobic sand@soot with core–shell structure and large-scale production possibility for oil/water separation

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping.

https://www.mdpi.com/1099-4300/24/8/1103/pdf?version=1660729470

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network

Turning a negative into a positive: Trends, guidelines and challenges of developing multifunctional non-wettable coatings based on industrial soot wastes

We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patient and caregiver requirements.

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices.

A hydrophobic and superoleophilic adsorbent was prepared by coating candle soot (CS) on the surface of a recycled egg carton material (ECM). This waste material has been explored as a cost-effective adsorbent to remove oil and detergent from water. The surface of the material was coated with CS solution prepared by mixing soot with acetone and characterized by scanning electron microscopy and contact angle measurements. The rate of fall of contact angle for water and oil was evident of water rejection and oil absorption capability of the coated-waste material. Further, the effect of temperature on the contact angle between water and surface was observed. The carbon-coated ECM demonstrates good absorption capacity with oils of different densities, without pre-treatments and surface modifications. It also shows its capability to absorb detergent from water with a pH value declining towards 7. Thus, a waste material can act as an effective alternative for filtering of oil and detergent water for households and industries.

Candle soot-coated egg carton material for oil water separation and detergent adsorption

With the advent of technology and multimedia production, the world has witnessed a tremendous increase in digital media attacks, which duplicates, forges and tamper the data leading to the violation of copyright laws. In this paper, a robust and secure digital image watermarking is proposed, which exploits the chaotic behaviour of the non – linear oscillators realized through Memristive diodes. The proposed scheme relies on a Human Visual System (HVS) model in order to mimic the real-life scenario. To improve the robustness of the proposed approach and to further increase the security of the digital watermarked media whilst still retaining compatibility with the real-time events, Histogram of Oriented Gradients (HOG) and extreme learning machine (ELM) is implemented. Secure key generation by means of scrambling through Arnold Transform and the coefficients of Memristive Chaotic Oscillator ensures extreme security. The watermark embedding followed the pixel transformation based on discrete cosine coefficient modification, and a semi-blind watermarking extraction procedure was carried out through trained ELM models. A detailed analysis has been presented to evaluate the tradeoff between imperceptibility, security and robustness using performance metrics like PSNR, NC, SSIM, and BER. To establish a real-time implementation of the proposed architecture, the simulated results were verified using real-time chaotic signals generated from the chaotic oscillator, which dictates excellent performance against watermarking attacks and image processing tasks.

/pdf/robust-and-secure-digital-image-watermarking-technique-using-1kgfyvr6jv.pdf

Robust and Secure Digital Image Watermarking Technique Using Arnold Transform and Memristive Chaotic Oscillators

Epilepsy is the key concern of medical practitioners and machine learning researchers since last decade EEG signals play a very crucial role in early detection of epilepsy as well as cure of epilepsy The traditional approach to analyze EEG signals includes two main steps: feature extraction and classification Since multi-channel EEG data is chaotic data, selecting optimal features and classifying them are major challenges There exist a number of feature extraction and classification techniques proposed by researchers which perform well For feature extraction, wavelets have been proved to perform state-of-the-art performance, but no such state-of-the art performance exists for classification techniques The classifiers explored in the presented work include random forest classifier, support vector machine, Naive Bayes, k-nearest neighbor, decision trees, artificial neural network, and logistic regression Experimental results on the UCI dataset represent that random forest is performing best with 9978% accuracy

Comparative Study on Machine Learning Classifiers for Epileptic Seizure Detection in Reference to EEG Signals

Proton irradiation effects on buffer-free gallium nitride on silicon carbide high electron mobility transistor-based radio frequency power amplifier

This work presents an accurate, scalable, and efficient hybrid machine learning (ML) and neural network (NN) model based, cross‐platform application to analyze and estimate crucial device parameters like scattering (S‐) parameter, gain, power, device‐width and traps of GaN high electron mobility devices. A hybrid multilayer perceptron random forest (RF) model with 13 input parameters was trained on Technology Computer‐Aided Design generated data. An inclusive view of device physics using various device parameters has been presented, which helps to develop insights into structural architecture, DC, and RF performance of the device, and its trade‐off as a function of frequency and biases, all in one go. Moreover, a cross‐platform graphical user interface application is developed to ease the complete process, which further makes the platform scalable to other devices as well. The extremely fast prediction time of 3–7 s and 35–40 ms in regression and classification, respectively, depict the excellent time efficiency of the proposed approach. The results show superior performance with the global mean absolute error (MAE) ranging from 8e−5 to 0.010 and mean squared error (MSE) ranging from 5e−7 to 0.072 accounts for the overall accuracy of 99% and above for the S‐parameter and current gain predictions. A maximum 0.06% variation in the prediction of device width over 140 iterations depicts the excellent generalization capability of the model. The evaluated results thus direct the usefulness of the cross‐platform applications in accelerating accurate device parameter estimations and efficient optimization in device design.

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Samriddhi Raut

Papers

Candle soot-coated egg carton material for oil water separation and detergent adsorption

Robust and Secure Digital Image Watermarking Technique Using Arnold Transform and Memristive Chaotic Oscillators

Comparative Study on Machine Learning Classifiers for Epileptic Seizure Detection in Reference to EEG Signals

Proton irradiation effects on buffer-free gallium nitride on silicon carbide high electron mobility transistor-based radio frequency power amplifier

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations