Miracle-dz/UniFAR-FTUs huggingface dataset:
Earable Design Analysis for Sleep EEG Measurements

Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms).
Genotypic variation in rice for grain yield and quality as affected by salt-affected field conditions

ABSTRACT A field experiment was conducted under two natural field conditions at the Research Farm (normal soil) and Proka Farm (salt-affected soil) of The Institute of Soil and Environmental Sciences (ISES), University of Agriculture, Faisalabad, Pakistan, to evaluate the performance of 11 rice genotypes in normal and salt-affected conditions. The experiment was laid out in randomized complete block design (RCBD) with three replications. The gas exchange attributes were measured at vegetative stage whereas the grain and straw yields and the yield components were recorded at maturity. After harvesting, the ionic parameters including sodium (Na+) and potassium (K+) were determined. Afterward, grain quality in terms of length, width, milling recovery, broken fraction, and chalkiness was also determined for the selected genotypes. Salt-affected conditions adversely affected the physiology, yield, and quality of the tested genotypes. The genotypes KS-282 followed by Shaheen Basmati showed significantly higher photosynthetic rate, transpiration rate, and stomatal conductance under both normal and salt-affected conditions, whereas the genotypes 99404 followed by 99417 showed minimum values of gas exchange attributes. The grain and straw yields were the highest in the case of KS-282 at both sites, whereas the lowest grain and straw yields were observed in the case of 99440 followed by 99417 under both normal and salt-affected conditions. Regarding the quality attributes, Super Basmati produced longer grains but with higher broken fraction and lower milling recovery, whereas the reverse was observed in the case of KS-282.
Power Grid Parameters Determining Based on Extended Sensitivity Matrix in Electricity Market

With the continuous advancement of the electric power reform, regional power grids have gradually developed into an interconnected form with high complexities. To reduce the complexity of the large-scale power grid and simplify the analysis and calculation, an equivalent network with simplified critical information that can effectively simulate the original grid is required in the operation of electricity market. To this end, this paper proposes a method for extracting the critical parameters of large-scale power grid based on an improved power transmission distribution factor (PTDF) matrix. The developed improved PTDF matrix is easy to be calculated and can be directly used as the network parameter of the equivalent model. In the case study, the proposed method is first illustrated on the IEEE 14-nodes test system. Then, the proposed method is leveraged to extract the critical parameters for Shanghai Power Grid based on the field BPA data. Four metrics including the locational marginal price (LMP), active power outputs of generators, reserved node voltage phase angles, and the power flow of reserved lines are testified on both the equivalent networked and original network. The results demonstrate that the proposed improved PTDF matrix-based parameters extraction method can well reflect the operating conditions of the system under normal conditions while also applicable for congestions.
Comparison and Analysis of Performance Using Different Classifiers for Classification of Motor Imagery EEG Signals

Brain-Computer Interface system (BCIs) helps a person with severe motor impairment to control external devices without using the peripheral nervous system. The performance of the system defines the system's effectiveness. The major challenge in BCI to bring it out of the laboratory is the classification accuracy. To overcome this, a robust signal processing algorithm is required with an effective classifier system. In this paper, we have studied and analysed the performance of three different classifiers i.e., Linear Discriminant Analysis (LDA), Diaglinear and Mahalanobis distance classifier's accuracy for classification of left and right hand Motor Imagery (MI) Electroencephalograph (EEG) signals. It is observed that the performance of LDA and Mahalanobis are almost equal, however the Mahalanobis performs well for subject that doesn't perform well with the other two classifiers. This work can be further extended for classification of four different classes in future.
Using Transfer Learning and Pattern Recognition to Implement a Smart Waste Management System

Smart cities are nowadays a very growing industry and a way of life that people want to experience. The technology that is used in the smart cities very well provide better living standards for the people. Smart city is most commonly utilised the benefits of information and communication technology. Internet of Things has opened a very vast space and has brought in many opportunities for all kind of research and development to integrate technology into people's life and hence make smart city is possible. this is a very vast topic to discuss as it has many different applications like water leak detection, gas leak detection, street lights that are smart and a very well designed traffic management system. also making use of this technology a very efficient way to collect waste can also be designed to maintain a very healthy lifestyle for the people and also minimizing the operation cost. This research paper presents a very well designed and completely new architecture In order to implement a smart waste management system which at its core implements artificial intelligence technique and machine learning with a combined focus on Internet of Things. By combining this approach with deep learning a system can be made which has a very minimal operation cost while not compromising on performance.
New composites based on low-density polyethylene and rice husk: Elemental and thermal characteristics

We developed new composites by combining the solid waste from Low-Density Polyethylene in the form of plastic bag (PB) and biomass from rice husk (RH),in the form of (RH)x(PB)1-x (x = (1, 0.9, 0.7, 0.5)), as alternative fuels for electrical energy sources, and for providing the best solution to reduce environmental pollution. Elemental compositions were obtained by using proximate analysis, ultimate analysis, and X-ray fluorescence spectroscopy, and the thermal characteristics were obtained from thermogravimetric analysis. The compositions of carbon and hydrogen from the ultimate analysis show significant increases of 20–30% with increasing PB in the composite. The activation energy for RH is 101.22 kJ/mol; for x = 0.9 and 0.7, this increases by 4 and 6 magnitude, respectively, and for x = 0.5, shows remarkable increase to 165.30 kJ/mol. The range of temperature of about 480–660°C is required for combustion of the composites (RH)x(PB)1-x (x = (1, 0.9, 0.7, 0.5)) to perform the complete combustion process and produce high energy. In addition, the calorific value was determined by using bomb calorimetry, and shows value for RH of 13.44 MJ/kg, which increases about 30–40% with increasing PB content, indicating that PB has a strong effect of increasing the energy realized to generate electricity.
Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Motion pattern analysis uses a variety of methods to recognise physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. Real cycling experiments were recorded in a hilly area with routes of about 12 km long. Signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data, including the detection of heart rate recovery delay as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The classification of downhill and uphill cycling based upon accelerometric data achieved an accuracy of 93.9 % and 95.0 % for the training and testing data sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. These techniques may also be applied to wide ranging applications in rehabilitation and in the diagnostics of neurological disorders.
FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep Learning

Millions of people are affected by stuttering and other speech disfluencies, with the majority of the world having experienced mild stutters while communicating under stressful conditions. While there has been much research in the field of automatic speech recognition and language models, stutter detection and recognition has not received as much attention. To this end, we propose an end-to-end deep neural network, FluentNet, capable of detecting a number of different stutter types. FluentNet consists of a Squeeze-and-Excitation Residual convolutional neural network which facilitate the learning of strong spectral frame-level representations, followed by a set of bidirectional long short-term memory layers that aid in learning effective temporal relationships. Lastly, FluentNet uses an attention mechanism to focus on the important parts of speech to obtain a better performance. We perform a number of different experiments, comparisons, and ablation studies to evaluate our model. Our model achieves state-of-the-art results by outperforming other solutions in the field on the publicly available UCLASS dataset. Additionally, we present LibriStutter: a stuttered speech dataset based on the public LibriSpeech dataset with synthesized stutters. We also evaluate FluentNet on this dataset, showing the strong performance of our model versus a number of baseline and state-of-the-art techniques.
BIST Based Aging Fault Prediction Using Machine Learning

With the rapid reduction in the size of chips nowadays, the phenomenon of circuit aging is becoming more significant. The impact of circuit aging includes slower chip speed, irregular timing characteristics, and increased power consumption. Such impacts have a negative effect on the proper functioning of the chips. Instead of over-designing chips to ensure reliable operation for a long time, this research work proposes a system to monitor the aging process and determine the stage of aging the chip so that, the necessary corrective measures can be undertaken to avoid any functional failures that may occur over time.
IoT-based Automated Greenhouse for Deep Water Culture Hydroponic System

Greenhouses are climate-controlled environments that allow maintaining the ideal conditions for plant development. A large proportion of these structures are controlled manually to keep within the range of desired parameters such as temperature, humidity, pH, and heat index, requiring one or more operators to repeatedly visit the greenhouse. Autonomous control and monitoring of these variables through sensors, actuators, and modules that allow the combination of the Internet of Things (IoT) applied to these technologies provide a good performance in plant growth, since it can store and access records in the cloud at any time over platforms such as ThingSpeak and Blynk. This paper is oriented to planning an autonomous deep water culture hydroponic system that saves water resources and produces under limited environmental conditions. For this reason, a prototype has been developed, which includes a Real-Time Clock (RTC) module, a level sensor (9SSE1), and a temperature and humidity sensor (DHT11), which send the data to the NodeMCU (development board with IoT platform) that will process the information to control the coolers, light bulb, buzzer, and greenhouse lights.
Dynamic Power Splitting Simultaneous Wireless Information and Power Transfer Split Receiver for Wireless Sensor Networks

Simultaneous wireless information and power transfer (SWIPT) presents a genuine opportunity toward achieving sustainable low-energy wireless devices. This paper introduces a receiver design for an energy harvesting sensor node (SN). The receiver is equipped with multiple radio frequency (RF) inputs. Furthermore, the receiver contains separate circuitries for information decoding (ID) and energy harvesting (EH) with dynamic power splitting (PS). The ID and EH circuits are connected to a power splitter that first combines all the RF inputs and then splits the power between the circuitries. Moreover, dynamic PS allows regulating the power split ratio between EH and ID circuitries. Consequently, the output of the EH circuit can be monitored and increased by adjusting the PS ratio. The proposed receiver design increases the harvestable energy by moving the ID into a separate circuitry. A voltage multiplier arranged in a Dickson scheme is used to level up the received voltage. At the transmitter side, the energy signal (ES) is sent through high power unmodulated continuous wave centered at the carrier frequency, while the information signal (IS) is sent via low power subcarriers around the carrier frequency. Such power allocation increases the harvestable power while reducing interference to external wireless networks. Moreover, different system measurements, including harvested energy from the received signal and data rates, are presented, where the split receiver scheme showed a significant improvement compared to the combined scheme in terms of harvestable power.
SIW Cavity-Backed Self-Diplexing T-shaped Slot Antenna

In this paper, a self-diplexing antenna is presented that uses Substrate Integrated Waveguide (SIW) technology for Wireless LAN (WLAN) applications. A single-layered square SIW cavity is designed with two parallel ports excited by microstrip feed lines. T-shaped slots of unequal lengths are etched on top conducting layer of the cavity to radiate at the desired frequencies. These slots shift the resonant frequency by perturbing TE120 mode of the SIW cavity to the desired frequencies of operation. Isolation between the ports is better than 20 dB evoking self-diplexing behavior of the antenna. The antenna radiates unidirectionally at 5.22 GHz and 5.83 GHz with gains of 6.07 dB and 5.42 dB. The frequency of operation can be tuned by changing the dimensions and positions of the respective slots.
Perils and Applications of IoT Security in Military Operations

The increase in the adoption of IoT in the world has also seen an escalation of its use in military operations. With the military being a sensitive field and a lot of military assets being dependent on the Internet, it is important to investigate the shortcomings in this area. This article discusses various perils and concerns in IoT security especially in the military sector and proposes a framework in the same domain for providing a possible approach to the current problems. Furthermore, to keep researchers updated in this field, the article also illustrates current research work in the same area and provides feedback related to strengths and weaknesses for that specific endeavor.
Towards Modeling Auditory Restoration in Noisy Environments

Real-world sounds are often interrupted by various kinds of noise. The target signal of the mixture sounds is often degraded or lost. While the human auditory system can extract the target signal from the mixture and restore the degraded or lost parts simultaneously, current computational models often simplify the complex scenarios, which leads to two individual tasks, audio inpainting and speech enhancement. In this work, we take a pioneering step towards modeling auditory restoration, that is to restore the target speech signal, in which there are missing parts in the target signal and the target signal is interfered by background noise. Different from the speech enhancement task, we attempt to fill in the missing gaps with the existence of background noise. Different from the auditory inpainting task, there is some noise in our input signal and the positions of the missing gaps are unknown. In other words, we attempt to reduce interference and restore missing gaps simultaneously. We propose Hourglass-shaped Convolutional Recurrent Network (HCRN) trained with Spectro- Temporal loss to restore the target signal from the incomplete noisy mixture. Moreover, instead of restoring non-human sounds, we focus on speech restoration, which poses more challenges on reconstruction. Both the quantitative and qualitative performance show that our proposed method can suppress the background noise, identify and restore the missing gaps of the salient signal with the unreliable context information. Our code is available in https://github.com/aispeech-lab/HC $\text{RN}$.
Overview and analysis of voltage sag mitigation measures

Voltage sag is an unavoidable power quality problem in the power system, and it is also an urgent problem for sensitive industrial users. In order to maintain the reliable and economical operation of the system, the management of voltage sags has always received extensive attention from researchers. This article analyzes the current voltage sag mitigation measures and its performance. And on this basis, in terms of topological structure, technical characteristics, adaptation scenarios and Development status, the mitigation measures for voltage sags such as FCL, MPC, superconducting energy storage technology, PET, STATCOM, AC/DC hybrid system, and distributed power supply are summarized in this paper. with PET as the key research object, PSCAD simulation was carried out to simulate the voltage sag of the power grid, which verified the isolation effect of the AC/DC hybrid connection system on the voltage sag under the action of PET.
Highly efficient simultaneous adsorption of Cd(II), Hg(II) and As(III) ions from aqueous solutions by modification of graphene oxide with 3-aminopyrazole: central composite design optimization

In this work, graphene oxide (GO) chemically modified with 3-aminopyrazole (GO-f) was synthesized for the simultaneous adsorption of three heavy metals, Cd(II), Hg(II), and As(III), from aqueous solutions. The 3-aminopyrazole functionalized GO adsorbent acts as a good host for welcoming the incoming guest and the abundant nitrogen-containing functional groups on the adsorbent surfaces play an important role in heavy metal adsorption. The synthesized adsorbent was characterized by FT-IR, EDX, SEM and TGA measurements. In order to study the influence of pH, adsorbent dose, and initial ion concentration on the adsorption process, the central composite design (CCD) was employed. The quadratic model was used for analysis of variance and indicated that adsorption of metal ions strongly depends on pH. Time-dependent adsorption can be demonstrated by the pseudo-second-order kinetic model, and the adsorption process was modeled by the Langmuir isotherm for the adsorbent. Owing to the large specific surface area, hydrophilic behavior, and functional moieties, the adsorbent showed excellent removal ability with a maximum adsorption capacity of 285.714, 227.273, and 131.579 mg g−1 for cadmium, mercury and arsenic ions, respectively. Thermodynamic analysis revealed that the adsorption of Cd(II), Hg(II) and As(III) ions was spontaneous and endothermic. Moreover, the competitive adsorption capacities of the heavy metal ions were lower than the noncompetitive ones. Also, the same affinity order was observed for noncompetitive and competitive adsorption: Cd(II) > Hg(II) > As(III). A desorption study revealed the favorable regeneration ability of the adsorbent powder, even after three adsorption–desorption cycles.
CHARM: An Improved Method for Chinese Precoding and Character-Level Embedding

The numerical transformation of text is a key step in natural language processing tasks, among which the word embedding model is the most representative. However, the word embedding model is insufficient in representing unregistered and low-frequency words, and the character-level embedding model makes up for it. Most Chinese character-level models focus on the independent use of Chinese character features such as strokes, radicals, and pinyin, or the shallow correlations between some features, while the inherent correlations among different features such as pronunciation, glyph, stroke order, and word frequency are not fully utilized. Through the statistical analyses of various features of Chinese characters, this paper proposes a precoding method based on Character Helix Alternative Representation Model (CHARM), which can realize the reversible mapping of Chinese characters or words to English-like sequences, and the advantage of this method is verified in three tasks: text classification, named entity recognition and machine translation. Experimental results on several test sets show that the model performs well, and can be a replacement character-level corpus for the original Chinese text.
Reference Oversampling PLL Achieving −256-dB FoM and −78-dBc Reference Spur

This article presents a low jitter, low power, low reference spur LC oscillator-based reference oversampling digital phase locked loop (OSPLL). The proposed reference oversampling architecture simultaneously offers a low in-band phase noise, a wide-bandwidth, and a low spur. In addition, this article proposes an LC digitally controlled oscillator (DCO) for the proposed OSPLL to achieve a fast frequency update and fine frequency resolution, while its varactor switching timing is set optimally for low jitter using the proposed DCO tuning pulse timing control scheme. The proposed OSPLL was fabricated in a 28-nm CMOS process. The integrated rms jitter of the PLL was measured at 67.1 fs for an output frequency of 4 GHz. The in-band phase noise of the PLL was −129.2 and −132.5 dBc/Hz at 1- and 5-MHz offset frequencies. The measured reference spur of the PLL was −78.1 dBc. Total PLL power consumption was 5.2 mW, resulting in −256.3-dB PLL jitter-power FoM, while occupying 0.17-mm2 area.