Miracle-dz/UniFAR-FTUs huggingface dataset:
Erratum to Kellman P, McVeigh ER. Image reconstruction in SNR units: a general method for SNR measurement. Magn Reson Med. 2005;54:1439–1447.

In the above article, a noise prewhitening approach was used prior to image reconstruction. In this approach, the noise correlation matrix becomes the identity defined with unit variance for real and imaginary noise components. Hence, the noise correlation Rn did not appear explicitly in Eq. [7] as required. In this erratum Eq. [7] is rewritten to include the noise correlation. This step required modifying the definition of noise correlation (Eq. [1]) to be consistent with parallel imaging literature [Eq. 5], which led to further modification of Eqs. [5, 6]. The results are not affected since the experimental validation used the alternative noise prewhitening approach. The updated equations include the prewhitened form as Eq. [8]. The revised Eq. [1] and rewritten subsection Array Combining follow.
Signal Denoise Method Based on the Higher Order Cumulant and Local Tangent Space Mean Reconstruction

In signal denoise method to nonlinear time series based on principle manifold learning, reduction target dimension is chosen at random, which cause low efficiency. Local low dimensional manifold is obtained by the eigenvalue decomposition to the covariance matrix, but covariance belongs to the second order statistics and cannot reflect the nonlinear essential structure of signal, these reduce denoise efficiency and effect. In order to solve these problem, a new denoise algorithm based on the higher order cumulant and local tangent space mean reconstruction is proposed in this reserch. First, the signal's intrinsic dimension is obtained as dimension of reduction targets by maximum likelihood estimation. And then making use of restraining character to colored noise of high order cumulan,covariance matrix is constructed by high order cumulant function instead of second order moment function. The data outside intrinsic dimension space will be regarded as noise signal to be eliminated. Finanly the process of global array by affine transformation will be replaced by mean reconstruction,the data after denoise may be obtained in the inverse process of the phase space reconstruction. The effectiveness of the algorithm is verified through the denoise experiment in fan vibration signal with noise.
Evidence for hydrated silicates toward SN 1987A

We have observed a sharp absorption edge at 2.7 μm in the near-IR spectrum of SN 1987A on day 53 after the progenitor star's core collapsed. We attribute this feature to structural OH in silicate minerals in circumstellar dust ejected from the progenitor star during its red supergiant phase. We obtain a mass column density of 4.3×10 -4 g cm -2 (±7%) for the absorbing material in the line of sight toward SN 1987A. The radii of the dust particles must be smaller than 0.04 μm to be consistent with the low value of interstellar extinction toward SN 1987A. A region of broad absorption extends from the long-wavelength side of the absorption edge at 2.7 μm which suggests that interlayer H 2 O is also present in these circumstellar silicates
Compressive sensing reconstruction techniques with magnitude prior information

This paper considers compressive sensing (CS) reconstruction with magnitude prior information. The magnitude prior information is described by mean and covariance of the unknown signal. Towards a reconstruction with minimum mean square errors (MMSE), we propose several CS reconstruction algorithms that use the magnitude prior information. Numerical simulations demonstrate that our approach reduces the reconstruction distortion. Potential applications of the proposed techniques include radio spectrum surveillance, sensor networks, etc. (5 pages)
SaaS quality of service attributes

Software as a Service (SaaS) is one of the cloud computing branch and it is rapidly growing. Quality of Service (QoS) for SaaS cloud is considered as an important concern among researchers. The QoS specification, particularly, for SaaS is known as a big gap in current researches. Therefore, in this study, the main QoS attributes for SaaS consisting of 33 attributes have been reviewed. The results show a comprehensive specification of QoS attributes for SaaS which cover more aspects of QoS for SaaS excluding the business perspective. The attributes then have been classified in terms of both user and provider perspectives and can be useful as a guideline for both users and providers.
On the dynamics of neural networks realizing associative memories of first and higher order

This paper presents a new study on the dynamics of neural networks realizing associative memories of first and higher order. This is accomplished by focusing on the dynamics of a system of coupled differential equations that is shown to be equivalent to the neural network which behaves as an associative memory. This equivalence allows the assignment of a Lyapunov function to neural networks realizing associative memories of any order. This analysis provides some new interpretations for the outer product rule, or correlational learning, and also a basis for the development of more sophisticated learning schemes. A fundamental difference in the behaviour of associative memories of even and odd order is also investigated.
Mutual Coupling Reduction Between Two Element Antenna Using Parallel Lines with Triangular Grooves and Tapered End

This paper proposes a mutual coupling reduction method for two element antenna using an isolating structure having high impedance to operate in 5G band. Proposed isolating structure consist of a pair of elements with triangular grooves and flared end which offers high impedance resulting in suppression of surface waves. Triangular grooves are added to enhance isolation. An isolation enhancement of 28.6dB is obtained over 3.6 to 3.8 GHz band by inserting the isolating structure between two radiating patch antennas of dimension 19.4mm ×25mm with an end to end spacing of 0. 05λ. Envelope correlation coefficient (ECC) less than 0.0024 is obtained which shows less correlation between radiating elements. Experimental results match well with simulation results.
Epilepsy Prediction using Machine Learning

Due to the severity of an Epileptic disorder there has been profound research in the recent years. Epilepsy is a brain disorder caused due to an imbalance of electrical activity of neural cells. It has extreme effects on the lives of patients including psychological and emotional distress. In severe cases, people suffer from less mobility and their social lives get disrupted. Prediction of Epilepsy can reduce it's impact on the patient with the help of medical assistance. Traditional methods to predict Epilepsy are vulnerable to error. Machine Learning can provide results with better accuracy and performance. Besides this, detection of Epilepsy is cumbersome and it needs a medical expert for diagnosis. This delays the time for treatment. An early diagnosis can improve the lives of patients drastically. EEG ( Electroencephalogram) is used for the purpose of recording electrical signals from the brain. Multiple electrodes are placed on the scalp of the patient at different points to collect electrical impulses. This paper focuses on different Machine Learning models to compare their performance and accuracy. EEG signals are present in the time domain. In order to obtain data about electrical signals from the frequency domain as well we have used DWT (Discrete Wavelet Transform). DWT decomposes the signal into sub bands. The decomposed data is fed into feature selection models followed by machine learning models like ANN, KNN, Logistic Regression and SVM.
RRP: A Reliable Reinforcement Learning Based Routing Protocol for Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widespread adoption of WMSN advancements is still hampered by security concerns and limitations of routing protocols. Routing in WMSNs is a challenging task due to the fact that some WMSN requirements are overlooked by existing routing proposals. To overcome these challenges, this paper proposes a reliable multi-agent reinforcement learning based routing protocol (RRP). RRP is a lightweight attacks-resistant routing protocol designed to meet the unique requirements of WMSN. It uses a novel Q-learning model to reduce resource consumption combined with an effective trust management system to defend against various packet-dropping attacks. Experimental results prove the lightweightness of RRP and its robustness against blackhole, selective forwarding, sinkhole and complicated on-off attacks.
Indoor air quality assessment and control using fuzzy inference

In recent years, more and more occupants have suffered from respiratory illness due to poor indoor air quality (IAQ). In order to address this issue, this paper presents a method to achieve efficient assessment and adaptive control of IAQ. Firstly, based on fuzzy inference, a novel fuzzy air quality index (FAQI) model is proposed to effectively assess IAQ. Then, a simple adaptive control mechanism, called SACM, is designed to automatically control the ventilation system (actuator) according to real-time FAQI value. Finally, simulation experiments are per-formed by comparing with regular control (time-based control), which show that our proposed method comprehensively evaluates overall IAQ using various air parameters (CO2, VOC, HCHO, PM2.5, PM10, etc), and reduces average FAQI value.
A modular and mesh-capable LoRa based Content Transfer Protocol for Environmental Sensing

Sensors are increasingly collecting data everywhere, changing how we relate to and manage the environment. These data answer scientific questions of researchers, but can also generate social, cultural, and political effects, reinforcing the need for data in near real time. In this work, a low-power, scalable, and sustainable communication solution based on LoRa is proposed to develop an oceanographic monitoring system composed of water quality monitoring buoys that can measure, among others, parameters such as water temperature, chlorophyll-a, turbidity, oxygen concentration, etc. An exhaustive evaluation is carried out with a real test bed in a 135 Km2 coastal lagoon, demonstrating that our proposal offers good performance in terms of transfer time and latency, energy consumption, and range, showing good scalability with the number of buoys added.
Efficient Conditional Handover Algorithm in 5G with Blockages using Recurrent Neural Network

In the Third Generation Partnership Project (3GPP) specification [1], the conditional hand over procedure is defined to reduce the hand over failures in 5G networks by early preparation of the radio resource for user equipment (UE). In this paper, in order to minimize the resource reservation time while maintaining the radio link failures (RLF) rate of a UE, we make the first attempt to trigger the conditional handover of the UE only when the UE would experience an RLF in the near future. Our idea is to adjust the handover margin of triggering the conditional hand over of a UE based on the prediction on whether the UE would experience an RLF in the near future. To achieve an accurate prediction in the urban scenario with blockages, we reduce the conditional handover problem to a classification problem and solve the classification problem using a recurrent neural network (RNN). Simulations show that the proposed algorithm has extraordinary performance in terms of the RLF rate and resource reservation time, as compared with the state-of-the-art methods.
Solitary wave formation in one‐dimensional photonic crystals

We present a variational approach to the problem of solitary wave formation in one‐dimensional nonlinear photonic crystals. In particular, we analyze how an arbitrary initial pulse is reshaped into a stable solitary wave by shedding excess radiation. Owing to the strong Bragg‐scattering near photonic band edges this reshaping results in non‐trivial dynamics and we show that within the variational framework we can quantitatively describe this complex wave dynamics for relatively short times. For longer times, the results become less accurate but still allow a qualitatively correct description. Finally, we discuss how an improved handling of the coupling to radiation modes can lead to an accurate description of the long‐term behavior. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Design and Development of a Speech Recognition System for Autistic People

Autism spectrum disorder (ASD) is one of the fast growing neurodevelopmental disorders across the world. By suitable interventions, their life can be improved a lot. The severity of disease and level of intervention required is different for different persons. Speech given by the ASD people are distorted ones. In this project a distorted Malayalam speech recognition system is developed using Mel-frequency cepstral coefficients(MFCCs) and its derivative features of speech. A Deep neural network is used for recognizing distorted speech. Based on the relevance of words in the life of an autistic patient, level of similarities and difficulties in pronunciation, a distorted Malayalam speech dataset is created. The distorted Malayalam speech recognition system got a Word error rate of 2.8%.
Research on Gaussian mixture model to predict Lithium battery life

Living in an era of social economy and energy storage technology, according to the historical monitoring data, the remaining Useful life(RUL) of the lithium battery to make predictions is important. In this paper, the data driven method is used to systematically analyze the influence of single kernel function and super parameters of kernel function on prediction distribution. On this basis, the combined kernel function is derived. The Gaussian process regression model based on the combined kernel function is used to predict the health status and remaining life of lithium batteries offline. Compared with the prediction results of a single kernel function, the Gaussian process regression based on the combined kernel function not only guarantees the local learning ability, but also improves the generalization ability and effectively improves the prediction accuracy.
Arousal effects on Fitness-to-Drive assessment: algorithms and experiments

Several elements can affect the drivers' behaviour while they are performing driving activities. Ranging from visual to cognitive distractions, emotions and other drivers' conditions (that could emerge from biometric data, such as temperature, heartbeat, pressure, etc.) can play a significant role, performing as a factor that can increase drivers' response time. This could be crucial in avoiding dangerous situations and in deciding and performing actions that could influence the happening of car accidents. This paper introduces the concept of the “Fitness-to-Drive” index and aims to evaluate how the arousal effects can influence the drivers' status. The paper presents some experimental evaluations we have conducted on a driver simulator, discussing the obtained results.
Deep Reinforcement Learning Based Beamforming Codebook Design for RIS-aided mmWave Systems

Reconfigurable intelligent surfaces (RISs) are envisioned to play a pivotal role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, the optimal tuning of the phase shifters at the RIS is a challenging task due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Conventionally, wireless systems rely on pre-defined reflection beamforming codebooks for both initial access and data transmission. However, these existing pre-defined codebooks are commonly not adaptive to the environments. Moreover, identifying the best beam is typically performed using an exhaustive search that leads to high beam training overhead. To address these issues, this paper develops a multi-agent deep reinforcement learning framework that learns how to jointly optimize the active beamforming from the BS and the RIS-reflection beam codebook relying only on the received power measurements. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Simulation results show that the proposed learning framework can learn optimized active BS beamforming and RIS reflection codebook. For instance, the proposed MA-DRL approach with only 6 beams outperforms a 256-beam discrete Fourier transform (DFT) codebook with a 97% beam training overhead reduction.
An Overview of Current technology of Skin-Attachable Haptic Interface

Skin-attachable haptic interface is a type of skin-integrated electronics aiming to create a vivid tactile experience on human skin. They are malleable, extensible, and portable. In this review, we explain the primary mechanism of the skin-attachable haptic interface based on the skin's tactile perception. Then we show the necessity of developing proper actuators for tactile sense generating. For these actuators, we categorized them as vibrotactile and kinesthetic actuators. Here we introduce their components, mechanism, and properties. And then, we show how to achieve high-resolution and multimodal operation in a skin-attachable haptic interface. Afterward, we discuss the advantage and disadvantages of current technology. The limitation of the energy system, the lack of synchronization of different types of sensing, and the block to external interference are mentioned in the discussion. In conclusion, we look forward to the future of the skin-attachable haptic interface and conclude that a crucial step for the skin-attachable haptic interface is to improve the application value to promote the development of the related industry. This paper has a guiding significance for understanding the development situation of skin-attachable haptic interface and looking for related research direction.
Small Universal Antiport P Systems and Universal Multiset Grammars

Based on the construction of a universal register machine (see [7]) we construct a universal antiport P system working with 31 rules in the maximally parallel mode in one membrane, and a universal antiport P system with forbidden context working with 16 rules in the sequential derivation mode in one membrane for computing any partial recursive function on the set of natural numbers. For accepting/generating any arbitrary recursively enumerable set of natural numbers we need 31/33 and 16/18 rules, respectively. As a consequence of the result for antiport P systems with forbidden context we immediately infer similar results for forbidden random context multiset grammars with arbitrary rules.
PI, PID and Fuzzy PID Control of SAB in Solid-State Transformers for Wind Power Interfacing

As the integration of unconventional energy sources like solar and wind grows, and fairly large loads like electric vehicle charging stations, the performance of the distribution grid has considerably been affected. This calls for an efficient as well as flexible means for integrating them into the grid. Solid State Transformers (SST) are a good option for reducing various power quality issues like voltage and frequency fluctuations, for reactive power compensation, to improve system stability, etc. In this paper, the system's effectiveness is investigated for varying wind speeds using PI, PID and Fuzzy PID controllers and their performances are compared. It also studies the system performance after introduction of a disturbance. The system is simulated using MATLAB SIMULINK software.
Research On Nonlinear Gain Adaptive Sliding Mode Control Of Ship Trajectory Tracking Based On Disturbance Observer

A nonlinear gain adaptive sliding mode control method based on disturbance observer is designed to solve the problem of ship modeling error and 3DOF fully driven ship trajectory tracking control disturbed by unknown external environment. In this method, the error between the current position and the expected position of the ship and the error between the current speed and the expected speed are comprehensively considered to design a nonlinear gain adaptive dynamic sliding mode surface. the nonlinear gain effectively weakens the chattering phenomenon of the traditional sliding mode control input, and a disturbance observer is introduced to estimate the uncertain disturbances caused by external unknown environmental disturbances and modeling errors. By selecting the appropriate Lyapunov function, the controller can ensure the ultimate uniform boundedness of all signals in the trajectory tracking system. Finally, the simulation verification is carried out based on a transport ship, and the results show that the designed controller can ensure that the transport ship can achieve effective trajectory tracking, and has high tracking accuracy and strong robustness to modeling errors and unknown disturbances caused by the external environment.
Pair-Less Bluetooth for Touchless Interaction

The Covid-19 pandemic accelerated the need for touchless interactions with technology in public spaces. By leveraging mobile phones which are equipped with Bluetooth wireless transmitters, we enable touchless and app-less interactions between people and technology. The Bluetooth signal broadcast from everyday devices such as smart phones is used by Bluetooth receivers, such as wireless earbuds, to pair the devices and communicate data. In this work, on the other hand, the broadcasted Bluetooth signal is detected, along with any change in the signal caused by rotating one's phone or waving one's hand by the phone. These intentional gestures with one's phone in the presence of an equipped Bluetooth receiver are interpreted as 'words' of a communication language. Without the need to pair devices or download any software, the communication language enables touchless interactions between a user holding a phone and a computing system in a public space
Attention based Multi Modal Learning for Audio Visual Speech Recognition

In recent years, multimodal fusion using deep learning has proliferated in various tasks such as emotion recognition, and speech recognition by drastically enhancing the performance of the overall system. However, the existing unimodal audio speech recognition system has various challenges in handling ambient noise, and varied pronunciations, and is inaccessible to hearing-impaired people. To address these limitations in audio-based speech recognizers, this paper exploits an idea of an intermediary level fusion framework using multimodal information from audio as well as visual movements. We analyzed the performance of the transformer-based audio-visual model for noisy audio. We accessed the model across two benchmark datasets namely LRS2 and Grid. Overall, we identified that multimodal learning for speech offers a better WER compared to other baseline systems.