Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical solution of a non-linear ill-posed problem arising in inverse scattering

The author considers the problem of determining the shape of a two-dimensional obstacle from far-field scattering data. A model for sparse, discrete, error-contaminated data is introduced. This model has applications for both linear acoustic and electromagnetic inverse scattering problems. By parametrising the boundary of the obstacle, a one-dimensional problem, which is non-linear and ill-posed, is obtained. A numerical method is presented to deal with the ill-posedness, non-linearity and error in the data. This method yields a parametrised sequence of smooth approximate solutions. A statistical technique known as generalised cross validation is then used to determine an appropriate value of the smoothing parameter. Two numerical examples are given, showing that the method is quite robust.
How equipment impacts World Class Manufacturing

The world is becoming more demanding in terms of the way it is produced. Industries want to be more and more efficient, to reduce costs, such as by eliminating waste, which is responsible for expenses that can often be prevented and expenses of all kinds, from discarded material to lost production time. operators or process slowdown. In addition, the industry is looking for other ideals such as reducing environmental impact and controlling production to maximize quality, improve reliability and be able to address failures early, even predictively. To achieve the parameters of World Class Manufacturing (WCM), a significant effort is required on the part of the industries, which will be simplified, or its level of complexity will be increased depending on the equipment that was selected at the time: its cost and capabilities. This paper shows the most important parameters in the selection of equipment and its impact in the short and long term, thus, it is concluded that a bad selection effectively condemns bad production, unnecessarily complicating the producers and managers of the production floor, and how a correct selection allows, in addition to producing more efficiently, to adapt the equipment to the passage of industrial revolutions. Although it is true, that there are many variables, the important thing is to be aware of their impact and existence, allowing a more detailed analysis, and achieving a better decision.
Manifold Learning-Based Feature Transformation for Object Recognition

Recognizing objects is an important task for any computer vision application. Therefore many research works are carried out to make machines understand the objects present in a picture. This paper presents a novel approach to reduce the domain difference of source and target domain using manifold feature learning while preserving the locality and discriminative information of the source data. The method balances the distributional shifts of the domains using MMD metric and the target domain data is maximally varied inorder to avert projecting the data onto trivial dimensions. The manifold feature learning aligns the source and target subspaces by constructing a geodesic flow between the datapoints and integrates the subspaces along the flow. A thorough experimental analysis was performed on the method using benchmark datasets like Office-Caltech 10 using SURF, Decaf6 and NASNETLarge feature descriptors. Various existing methods has not properly exploited the feature aligning techniques for better structuring of the datapoints so that the data can be well positioned in the subspaces. Experimental results shows that the our proposed framework outperforms the traditional domain adaptation approaches on this dataset. Office-Caltech dataset achieved an accuracy of 88.58% for Decaf6 features and 48.21 %for SURF features and 97.24 % for NASNETLarge features respectively.
In-Memory Computing Based Hardware Accelerator Module for Deep Neural Networks

In recent years, AI/ML has been increasingly becoming a part of our daily lives and in the technology around us. With this increasing prevalence, they currently provide the most effective solutions to a wide range of image recognition, speech recognition, and natural language processing challenges. The demand for fast and efficient calculations of increasingly complex models has gained immense importance. One of the biggest technical limitations faced today is the Von Neumann bottleneck. However, the data movement between memory tiers can be reduced by incorporating in-memory computing modules into the architecture. The possibility of using specialized circuits to improve the efficiency of matrix multiplication was attempted on a small test neural network. We focus on using SRAM-based in-memory computing modules, that use binary weights and inputs which can offer a significant improvement to performance and efficiency without a heavy impact on the accuracy of the models.
High-gain feedback in non-linear control systems†

Abstract High-gain state and output feedback are investigated for non-linear control systems with a single additive input by using singular perturbation techniques. Classical approximation results (Tihonov-like theorems) in singular perturbation theory are extended to non-linear control systems by defining a composite additive control strategy, a control-dependent fast equilibrium manifold and non-linear change of coordinates. Those tools and an appropriate change of coordinates show that high-gain state feedback and variable structure control systems can be equivalently used for approximate non-linearity compensation in feedback-linearizable systems. Next the effect of high-gain output feedback is shown to be related to the strong invertibility property and the relative order of invertibility. For strongly invertible systems the slow reduced subsystem coincides with the dynamics of the inverse system when zero input is applied and with the unobservable dynamics when a certain input-output feedback-linear.
Implementation of STDP for Spintronics based SNN using 90nm CMOS Technology

Spiking Neural Network (SNN) is a sparse-driven network that can be used in low-power neuromorphic applications. Training such a network is still a challenge. In this paper, we have implemented and analyzed a biologically plausible unsupervised Spike Time Dependent Plasticity (STDP) algorithm for training SNNs. The implementation was done in 90nm CMOS Technology using Cadence Virtuoso circuit simulator. The implementation was verified using MNIST dataset for handwritten digits. The MNIST images were converted to time-dependent spike trains by rate encoding technique using the Google Colab platform. The parameter specifications were analyzed in Cadence Virtuoso. Dependency of each parameter on the circuit characteristics was obtained and simulations show that the time constant and value of the capacitor in the circuit are linearly dependent.
Machine Vision Using Cellphone Camera: A comparison of deep networks for classifying three challenging denominations of Indian coins

Indian currency coins come in a variety of denominations. Off all the varieties $\unicode{x20B9}1,\unicode{x20B9}2$, and $\unicode{x20B9}5$ have similar diameters. Majority of the coin styles in market circulation for denominations of $\unicode{x20B9}1$ and $\unicode{x20B9}2$ coins are nearly the same except numerals on its reverse side. If a coin is resting on its obverse side, the correct denomination is not distinguishable by humans. Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model. The digital images were generated by using cheap cell phone cameras. To find the most suitable deep neural network architecture, four were selected based on the preliminary analysis carried out for comparison. The results confirm that two of the four deep neural network models can classify correct denomination from either side of a coin with accuracy of 97%.
A Novel Ultrawideband Antenna with Band Notching Facilities at WLAN, C-band, and X-band

Ultrawideband (UWB) technology is the current and future requirement of fifth- and sixth-generation (5G and 6G) mobile networks because it enables larger data rates over short distances. This research proposes a novel arc-shaped triple-notch UWB antenna to accommodate the needs of the next generation of mobile networks. The arc-shaped radiator helps in achieving a better S11 parameter (S11 < ₋10 dB) throughout the UWB operating range. Wide bandwidth is achieved using the partial ground plane. Triple notches are created by two patch slots and one ground plane slot. The antenna rejects WLAN (5.1-6.2 GHz), C-band (6.7-8.1 GHz), and X-band (9.7-11.5 GHz). The designed antenna operated from 3.5-13.6 GHz, thereby helping in achieving a bandwidth of 10.1 GHz and fractional bandwidth of 118%. Throughout this UWB range, VSWR is observed to be in the range of 1 to 2. The antenna has a gain ranging from 3.7-4.9 dB throughout the operating bandwidth. The suggested UWB notch antenna offers the advantages of small size, improved impedance matching, and a simpler design that can be conveniently exploited for the aforementioned applications.
Real-Time Implementation of Unilateral Crosstalk Cancellation Based on Bone Conduction

Since a bone conduction (BC) sound can bypass the eardrum and ossicles, BC hearing aids are a great option for those with outer or middle ear problems. However, due to unwanted transmission of BC sound, one of our ears can hear the BC sound that should only be delivered to the other ear, known as the crosstalk sound. Crosstalk cancellation can help with this, although it is unclear how efficient this method would be if implemented in real-time. This paper describes our attempt to implement and evaluate a real-time unilateral crosstalk cancellation system as a solution to suppress crosstalk sound. Since the human head, including its anatomical components, varies from person to person, the system suppresses crosstalk by generating a “user-specific” cancellation sound that destructively interferes with the BC crosstalk sound. It does this by measuring impulse responses (IRs) between bone transducers and a sensor in the ear canal. Then, these IRs are used to estimate a “user-specific” crosstalk compensation (CTC) filter that allows the system to generate the cancellation sound. We implemented the system on the Bela Mini embedded audio platform and achieved a latency of 1.65 ms. We tested the system on eight normal-hearing participants and confirmed the reduction in crosstalk sounds at the sensor location.
A Novel RRAM-based FPGA architecture with Improved Performance and Optimization Parameters

FPGA is an integrated circuit that is designed by a designer and configured by hardware descriptive language. So, in FPGA, we can add some memory to store the data using RRAM. Resistive Random-Access Memory-based routing multiplexers enhance the characteristics of FPGA architecture. The circuit design characteristics of RRAM-based multiplexers can be identified by their delay parameters with change in input size when compared to SRAM implementations because it controls the set or reset process, and therefore it is electrical parameters. In proposed method is circuit optimization. The one-level RRAM- based multiplexers vary with input size and we used small-sized programming transistors in 1-Transistor 1-Ram based multiplexers. The programming transistors normally are in small size which is reliable for the best Product delay power. Also, RRAM multiplexers need a smaller footprint, and the reduction in the area could be more adequately great and the proposed architecture for the routing tracks needs to redefine the routing tracks. In this paper, we compared the various power consumption and optimization FPGA Architecture between RRAM and SRAM-based FPGA Architecture and the results outperformed our proposed RRAM method.
High-K Spacer Gate Stack Engineered, Dual Metal Underlap Junction-less GaN Gate All Around (HKS-GSE-DMUL-JGaNGAA) MOSFET for High Frequency Applications

High-K Spacer Gate Stack Engineered Dual-Metal Underlap Junction-less GaN Gate All Around (HKS-GSE-DMUL-JGaNGAA) MOSFET is proposed and postulated in this work for its applications in higher frequency domain. This has been accomplished by contemplating our proposed device with various devices in particular, High-K Gate Stack Engineered underlap Junction-less GAA (Gate All Around) MOSFET (Si) (HKS-GSE-JSiGAA), Junction-less Gate All Around (GaN) MOSFET (J-GaN-GAA) and Junction-less Gate All Around (Si) MOSFET (J-Si-GAA). It is so found that HKS-GSE-DMUL-JGaNGAA MOSFET exhibits superior performance characteristics in terms of various parameters (drain-current (Ids), Trans-conductance (gm), output-conductance (gd) and cut-off-frequency (fT)) over existing Junction-less device architectures, thus warranting as an apt device for applications in high-frequency domain. Owing to remarkable material properties, GaN has been lately the preferred choice for use in high frequency and power applications. It can also be inferred that HKS--GSE-DMUL-JGaNGAA MOSFET has other superior performance parameters also, which includes the ratio (on-current to off-current ratio, Subthreshold Slope, Channel Resistance, Early-Voltage, Frequency-Transconductance-Product and Transconductance-Generation-Factor. Owing to all the aforementioned performance parameters, our proposed device architecture, (HK-DMGS-JGAA MOSFET) warrants as the most near to ideal device-design for several analog and/or digital applications in higher frequency domain.
Locomotion Classification of Bipedal Humanoid Robot using Fast Fourier Transform

A bipedal strolling robot is a kind of humanoid robot. These robots interact with the environment and may encounter external disturbances such as collisions. In this paper, a simple and robust methodology to detect disturbances during unidirectional walking of a humanoid robot is proposed. The procedures incorporate complex deep learning ideas which may require extra equipment, or strategies where various sensors are required bringing about complex multi-sensor information combination. The paper provides two techniques that can be effectively used to classify the state of a robot using existing gyroscope and accelerometer sensors. The first classification approach uses Fast Fourier Transform (FFT). The adopted methodologies allow detection of instability during walking and the experimental results obtained that suggests suitability to effectively classify the motion of robot during walking.
Time-resolved visualization of electric fields during femtosecond laser ablation

We observe the electric fields caused by charge distributions during femtosecond laser ablation from a silicon (100) surface. Femtosecond electron pulses passing near the ablation site serve as a probe of the electric field generated by the emitted charges and countercharges on the sample surface. The density map of the electron pulse downstream from the sample contains information about the charge distributions. We invert this information by fitting the beam maps using a simple charge distribution model. Under the present excitation conditions (390 nm, 150 fs, 5.6 J/cm2), we observe the emission of 5.3×1011 electrons/cm2 within 3 ps of the excitation pulse, leading to self-acceleration of the emitted electrons to 2% of the speed of light. Preliminary experiments on a metal sample display even faster dynamics.
A Real-time Detection Algorithm Based on Nanodet for Pavement Cracks by Incorporating Attention Mechanism

In order to realize real-time detection of road crack defects on edge devices with low computational power, we propose a Real-time detection algorithm of Nanodet pavement cracks incorporating attention mechanism for a wide range of domestic road crack detection practical needs. Based on the Nanodet model, the algorithm in this paper introduces the attention mechanism into the feature extraction structure and assigns different weights to the features to improve the attention of the network crack target. It adopts the SimOTA dynamic label matching strategy to optimize the training process, so that the model can make full use of the data set. Besides, it uses the Hard-Swish activation function instead of the original ReLU function to speed up the network training convergence speed and improve the robustness of the algorithm, so as to achieve fast and accurate recognition of the key parts of pavement cracks. Through experimental validation on the pavement crack data set, compared with several classical target detection algorithms, there are different degrees of improvement in recognition accuracy and detection speed. In this model, the number of parameters is only 0.96M, the occupied memory is 7.6 MB, the floating point computation is 2.21G, and the average accuracy in the test set reaches 95.51%, the FPS is 123 fps. The model, which is 4.32% better than the original model, improves the detection accuracy of the model while maintaining the lightweight characteristics of the original model, and has good prospects for application deployment.
Design of dual-rate PID controllers

This paper is concerned with the computation of dual-rate high-order-hold PID discrete equivalent controllers. We assume that a continuous-time PID controller has been already designed in order to meet continuous time-domain and/or frequency-domain specifications. Based on the well-known lifting technique, dual-rate high-order-hold circuits and discrete equivalent controllers are proposed. The dual-rate controller is obtained by combining a dual-rate high-order-hold with two single-rate controllers, one at high frequency and the other at low frequency. They are related to the PD and PI action respectively. The effectiveness of the dual-rate sampling can be easily studied a priori at single-rate low frequency (constrained frequency) by analyzing its effects on the system when the ratio between sampling frequencies goes to infinity. An application example is presented.
DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features

Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing.
Securing Hardware Accelerator Against Reverse Engineering Attack

Hardware Accelerators (HAs) have a significant contribution to the fast and uninterrupted functioning of a System-On-Chip (SoC). HAs are mostly used as reusable Intellectual Property (IP) in the SoCs to speed up the design process. This makes HAs susceptible to several hardware attacks like IP counterfeiting, IP cloning, inserting hardware Trojan etc. Reverse Engineering (RE) the HA design is one of the primary steps while launching those attacks. Hardware obfuscation hardens the RE process without changing its functionality, thus hindering the attacker's effort. However, none of the existing hardware obfuscation techniques protect HAs. This paper proposes a robust hardware obfuscation technique for HAs to enhance the RE complexity. It is an in-synthesis process where obfuscation is performed by inserting key-controlled blocks during architectural synthesis. The proposed obfuscation is achieved in three different ways, i.e., by obscuring the primary inputs, inserting the dummy operations, and obscuring the intermediate sub-functions. Unlike state-of-the-art methodologies, the obfuscation points are decided through a novel algorithm. We have analyzed the feasibility of the proposed approach on six different HAs for three different key sizes. Moreover, it achieves enhanced security (~12 times) with lesser design cost (~10.8%) compared to one closely related approach.
Extensible Bidirectional Bending Soft Robot for Hand Functional Rehabilitation Training

Soft robot has the advantage of good flexibility, high safety and portability in hand function rehabilitation training. In order to solve problems such as poor fit between soft robot and human hand, insufficient DOF and low driving force, this paper designed a soft robot with bidirectional bending module and elongation module, which can realize independent or coupled motion of multiple DOF. Using the FEM analysis method, soft driver model was established and a variety of flexible materials were used to ensure that it can provide sufficient driving force. The sensor was used to track the bending characteristics. Experiment results have verified that soft robot can complete grip and gesture training, and meet the training requirements of patients at different rehabilitation stages. The fingertip force can reach 5.1N and play an auxiliary role in the daily hand rehabilitation training of patients.
Third order nonlinear optical susceptibility of Cu:Al2O3 nanocomposites: From spherical nanoparticles to the percolation threshold

The third order optical susceptibility of metal-dielectric nanocomposite films (Cu:Al2O3) has been determined by degenerate four wave mixing. The films have been synthesized by alternate pulsed laser deposition and consisted of Cu nanoparticles in an amorphous Al2O3 matrix. They have metal volume fractions, p, ranging from 0.07 to 0.45, and morphologies that range from spherical particles (diameter, φ∼2 nm) to a random network when close to the percolation threshold. In nanocomposites containing isolated oblate spheroids (p⩽0.17), the optical response at wavelengths close to that of the surface plasmon resonance (SPR) can be described in the frame of the Maxwell-Garnett effective medium theory. Above the particle coalescence threshold, in nanocomposites with higher Cu content (p⩾0.2), both the linear absorption in the near-infrared and the third order nonlinear optical susceptibility at the SPR are greatly enhanced, the latter achieving values as high as 1.8×10−7 esu. These results are discussed in terms .
Social Media, Emotions and Technology: Identifying Possibilities and Attitudes During the COVID-19 Pandemic

The disruption of the COVID-19 pandemic and its multiple challenges tested states, countries, and people. Regarding the latter, information, and communication technologies, together with social media platforms, became a resource that helped to reduce the shortcomings that arose because of the criticality at that moment. However, they also contributed to redirecting the emotional dimensions towards the digital space. This work proposes a novel approach to the case of Panamá, by estimating the emotivity through opinion mining. The study used as a reactive element the official announcements issued by the Ministerio de Salud (Ministry of Health) related to the pandemic. The resulting reactions were recorded for six months through a popular social media platform. The results indicate a strong and negative impact on people's sensitivity. In addition, the data acquisition methods used, their processing, and analysis are provided as valuable contributions to the Latin American context for similar studies.