Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challenging. This work aims to enhance implantees’ ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses.
Audio Captioning Using Semantic Alignment Enhancer

Automated audio captioning (AAC) relies on the meaningful transfer of information from the encoder to the decoder. We propose a module called the semantic alignment enhancer to improve this process. This module converts the keywords extracted by the keyword extractor from audio features into captions. It then minimizes the distance between the text embeddings, output of the decoder, and the keyword caption embeddings, enhancing semantic similarity of alignment between audio features and text features. Additionally, to address the issue of data scarcity in audio captioning, we employ transfer learning. Specifically, we pre-train an audio encoder using the recently released large-scale audio-text paired WavCaps dataset. We use this pre-trained encoder to enhance the model's performance during the fine-tuning process. As a result, this approach enhances the alignment between audio features and textual information, which leads to improved performance in AAC tasks on the Clotho dataset compared to the baseline.
Does the Digital Payment System Affect the Poverty Score in India?

This research aims to study the relationship between financial inclusion efficiency (FIE) and poverty score. The data for this research includes 28states and 3 union territories of India. A data envelopment analysis (DEA) is a popular and effective tool revealed by literature research studies to assess countries' financial inclusion levels. This study used a panel data model with DEA wherethe Poverty Score (PS) is presented as the dependent variable, and the independent variables selected are Financial Inclusion Efficiency (FI_CRS), Number of ATMs (No_ATM), and Literacy Rate (Lit_rt). From the optimal weights of the DEA model, descriptive statistics, correlation matrix scores, and endogeneity test results, the results of the study show that there is a significant impact on poverty scores by financial inclusion efficiency. The Financial Inclusion Efficiency (FI_CRS) is significant and negative which signifies that there is a negative relationship between Financial Inclusion Efficiency (FI_CRS) and Poverty Score (PS). Thus, this research shows that the rise in fie resulted in a decrease in the Poverty Score of 28 Indian states and 03 union territories.
Sparse Channel Feedback for Energy-Efficient mmWave Massive MIMO Systems

mmWave massive multi-input multi-output (MIMO) systems with large-scale antenna array have potential to increase the spectral efficiency by orders of magnitude. One well-known drawback of this systems is that the power consumption of base station (BS) is considerable. To improve the energy efficiency and throughput, it is crucial that the BS acquires accurate downlink channel state information (CSI). However, this task is difficult since the CSI feedback overhead scales linearly with the number of antennas. In this paper, we propose an approach to maximize the energy efficiency of mmWave massive MIMO systems using a sparse channel feedback. Key idea of the proposed scheme is to choose a small number paths among the whole propagation paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding. From the performance analysis and the numerical results, we demonstrate that the proposed scheme achieves considerable energy efficiency improvement and feedback overhead reduction over the conventional CSI feedback-based schemes.
Modeling and Performance Evaluation of 6-Axis Robot Attitude Sensor

We propose a method for modeling a 6-axis robot attitude sensor which measures the orientation of the robot platform on which the sensor is installed. We avoid the technical difficulty in applying the unit step input for analyzing the sensor output in the time domain by employing a simple ramp input. We can still obtain a step response from the ramp input and moreover, provide an easy way of identifying the system transfer function of the robot attitude sensor. Based on the proposed method, we can accurately characterize the robot attitude sensor and provide a solution to reshape its output to meet any field requirements, such as those from 3D robot mapping, autonomous navigation, and robot motion control in the outdoor environment. We demonstrate the feasibility of the proposed method using the experimental result obtained by applying a ramp input to a sophisticated motion control device on which the robot attitude sensor prototype is installed.
Emulating Covert Data Transmission on Heterogeneous SoCs

Recent works have highlighted the vulnerability of System-on-a-Chip (SoC) platforms against frequency-based covert channels. An attacker might be able to leverage vulnerabilities in the SoC’s firmware, the operating system, or the design tools to gain access to the underlying hardware and perform frequency modulation. Given the diversity of threats and the constant evolution of SoC platforms, it is not practical to study this attack model using physical devices. To address this issue, we propose to employ advanced simulation techniques. Our work targets heterogeneous SoCs which feature a processor system based on the ARM architecture plus an FPGA. We employ the full system simulation of gem5, which allows us to create a complete virtual-system and study the interaction between its components. We present the emulation of three frequency-based covert channels in gem5 v22 by showing that it is possible to replicate the covert transmission of data between different elements of the SoC. To ensure the repeatability of our experiments all the sources are released as Open-Source.
1-kS/s 12-bit SAR ADC with Burst Conversion

The proposed 1-kS/s 12-bit SAR ADC performs burst conversion to compensate for the loss of the sampled analog signal due to leakage current in the capacitors of the capacitor-based DAC. The proposed SAR ADC is implemented using a 180 nm 1-poly six-metal CMOS process with a supply of 1.8 V. The proposed burst conversion for low-speed SAR ADCs improves the dynamic performance of ENOB from 10.82 bits to 11.87 bits for an input signal width a frequency of 472.16 Hz at a sampling rate of 1 kS/s. The sleep mode operation between the burst conversions reduces the average power consumption of the SAR ADC from 38.34 uW to 9.70 uW by reducing static power consumption.
Performance Analysis of Space–Time Line Code Systems with Phase-Shift Keying Modulation

The performance of space–time line code (STLC) systems employing phase-shift keying (PSK) modulation has not been studied, despite the distinct advantage of eliminating the need to estimate partial channel state information at the receiver. In this study, we comprehensively analyze the fundamental performance of STLC systems when utilizing PSK modulation, focusing on three essential aspects: 1) symbol error rate, 2) bit error rate, and 3) diversity gain and coding gain. The mathematical analysis is rigorously validated through numerical simulations, revealing system performance behavior concerning modulation order and the number of transmit antennas.
Potato Leaf Disease Detection: Comparative Study of the Performance of CNN Architectures - VGG19, ResNet50 and DenseNet201

In this study, the performance of the CNN architectures VGG19, ResNet50, and DenseNet201 is compared in the detection of diseases in potato leaves. Within the scope of this research, the Plant Village database (2152 images) was employed. To this database composed of healthy potato leaves, late blight-infected leaves, and early blight-infected leaves, data augmentation with Gaussian Blur was applied, resulting in a total of 2760 images. The CNN architectures were trained using transfer learning with different batch sizes (16, 32, and 64). Based on three parameters, it was determined that the CNN architecture with the best performance was DenseNet201 with a batch size of 16, achieving an accuracy of 99.64%, requiring a storage capacity of 79.9 MB, and a training time of 16 minutes and 16 seconds.
A Fast-Transient Right-Half-Plane Zero-Free Hybrid Buck-Boost Converter

A novel buck-boost converter (BBC) without right-half-plane (RHP) zero is proposed. The converter utilizes both flying capacitor and inductor, providing continuous output current with five power switches. Compared with previous hybrid converters, the voltage conversion ratio (VCR) of proposed converter is not limited. Operating in continuous conduction mode (CCM) with peak current-mode control, the proposed converter achieves significant transient response improvement. For lithium-ion battery application, the input and output voltage of proposed converter is 2.7V–4.2V and 3.3V, respectively. When the load current steps between 0A and 300mA, the settling time is only 20μs and the undershoot/overshoot merely 48mV.
Conformal Strongly Coupled Magnetic Resonance Wireless Power Transfer with Printed Double-D Coils

This paper presents a novel wireless power transfer coil structure that outperforms traditional printed spiral coils (PSCs) in power transfer efficiency (PTE) and transfer distance. The advantage of the high coupling coefficient of Double-D (DD) coils is combined with the conformal strongly coupled magnetic resonance (CSCMR) technique to achieve high power transfer efficiency while restraining coil size. Simulations show that the proposed WPT link can deliver 89.47% power transfer efficiency at a transmission distance of 55 mm, 1.88 times greater than the 47.68% PSC-based link with the same coil outer dimension. The proposed coil structure is compatible with standard PSC receivers for vertical placement, which can avoid the interoperability problem between the DD coil and the unipolar coil.
SKETCHCREATOR: Text-Guided Diffusion Models for Vectorized Sektch Generation and Editing

We present SketchCreator, a text-to-sketch generative frame-work built on diffusion models, which can produce human sketches given a text description. Specifically, sketches are represented in a sequence of stroke points, where our model aims to directly learn the distribution of these ordered points under the guidance of the prompt, i.e., text description. Uniquely, different from prior arts focusing on single-object sketch generation, our model can flexibly generate both the single sketch object and scene sketches conditioned on the prompt. Particularly, our model can generate the scene sketch uniformly without explicitly determining the layout of the scene, which is typically required by previous works. Consequently, the produced objects in a scene sketch are more reasonably organized and visually appealing. Additionally, our model can be readily applied to text-conditioned sketch editing which is of great practical usage. Experimental results on QuickDraw and FS-COCO validate the effectiveness of our model.
Machine Learning for Ensuring Sustainable Development: Predicting Employee Attrition in the Workplace

The present research provides a machine learning technique that uses Logistic Regression to predict employee turnover rates while accounting for critical parameters. The purpose is to prevent employees from leaving because it has a negative impact on organizational productivity, costs, and employee well-being. By analyzing data on employee demographics, work satisfaction, and other relevant variables, logistic regression is used to identify factors that impact attrition. In addition, the research investigates the effects of projecting employee attrition on long-term development goals like decent employment and economic growth, gender equality, excellent health and wellbeing, and reduced inequities. Organizations can promote long-term economic success while contributing to a more sustainable future by fostering a safe and productive workplace.
Power Efficient Long Range Drone Networking System for UAV Detection

In recent years, technological advancements in Unmanned Aerial Vehicles (UAV) have removed barriers to entry on drone usage for the public. While this has led to the growth of the drone industry it has also provided terrorists with easier access to drones. Rising threats of malicious drones have raised the importance of a Counter Drone System (CDS). Traditional CDSs use a variety of physical sensors and technologies to detect hostile drones and react efficiently, however, these methods have their own set of constraints, such as high power consumption and short operational range. This paper proposes a power-efficient long range CDS composed of a LoRa mesh network and on-device Machine Learning (ML). LoRa provides low-power and long range communication and the multi-hop feature of the mesh network expands the coverage of the network. Applying Tiny Machine Learning (TinyML) for detecting drone on the Micro-Controller unit is reasonable to design long range communication. On device Machine Learning has no round trip between server and device. The implemented system achieves a satisfactory communication range, a successful operation of the UAV detection and a power consumption which is similar to that of typical MCU.
A Driver Amplifier with Configurable Transformer-Based Matching Networks in 65-nm CMOS

A driver amplifier with the configurable transformer-based matching network (CTMN) is proposed in this paper. By configuring the resistor arrays and capacitor arrays of the CTMN through a digital circuit, parameters such as the gain, the bandwidth, the gain flatness and the ripple of the matching network can be reconstructed, and the adverse effects of process fluctuations, layout parasitic effects and model deviations can be mitigated. In 65-nm CMOS technology, a driver amplifier with CTMNs is realized and integrated in a receiver. Experimental results show that with CTMNs, from 18.2 GHz to 20.2 GHz the S22 can be improved by 3.5 dB, the S21 can be improved by 0.5 dB, the gain flatness can be improved by 0.1 dB, and the -3 dB bandwidth can be improved by 113 MHz.
Computation Offloading Quality of Service Improvement with Multi Layer Approach Based on Pure Edge Sim Simulator

Internet of Things (IoT) devices possess limited resources, encompassing computing speed of microcontrollers, memory capacity, battery energy sources, and diverse computational requirements. The prevalent system architecture comprises two tiers, wherein IoT devices connect to the cloud to transmit data or receive commands, relying on a stable internet connection. However, in environments with restricted internet coverage and stability, this situation can become problematic, potentially leading to a reduction in Quality of Service (QoS). A viable solution to address this challenge involves the utilization of computation offloading techniques. This approach involves recognizing computation characteristics through specific rules, determining whether computations should take place at the IoT device layer, the edge computing layer, or within cloud computing. By implementing a multilayer three-tier scheme, the computation offloading process can be effectively optimized. Empirical testing conducted using the Pure Edge Sim simulator demonstrates that the amalgamation of IoT, Edge, and Cloud layers yields the most favorable outcomes for enhancing QoS.
UNet-Based Polyp Segmentation: A Survey

Colorectal polyps are common intestinal diseases, in which image segmentation of colorectal polyps is crucial for diagnosis and treatment. With the development of deep learning, semantic segmentation technology has been widely used in colorectal polyp image analysis. Among them, the U-Net model is a widely used deep learning model, which is simple and effective and shows good performance in medical image segmentation tasks. This article mainly introduces the semantic segmentation technology of colorectal polyp images based on U-Net, including the basic structure, characteristics and advantages of the U-Net model, and its application in semantic segmentation of colorectal polyp images. At the same time, this article also reviews colorectal polyp image segmentation technology based on deep learning in recent years, focuses on the related research on the U-Net model and its improvements, and compares and analyzes various methods. In the methods and experiments section, this article introduces the structure, data set and experimental settings of the U-Net model in detail, as well as analyzes and compares the experimental results of U-Net-based colorectal polyp image semantic segmentation technology. Finally, this article summarizes the main contributions of the research and discusses prospects and challenges for future applications.
Power Fluctuation Suppression Method for WPT System Based on Additional Coupling Channels

At present, research on the suppression of power fluctuations in wireless charging systems mainly focuses on addressing lateral (X) and longitudinal (Y) offsets, while there is relatively little research on vertical (Z) offsets. This article proposes a new method to suppress power fluctuations in the wireless charging system of electric vehicles by using additional coupling channels to achieve closed-loop control. A rectangular coil mutual inductance calculation model has been established, accurately describing the relationship between two sets of coils. The higher-order components of the secondary side output are fed back to the primary side through additional coupling channels, and mutual inductance estimation and output calculation are achieved only at the power transmission end. On the basis of circuit analysis, the optimization design process of feedback loop is provided. Replacing the communication of closed-loop control with additional coupling channels, converting digital signals into analog signals, eliminating the stages of communication and data processing, and improving the real-time performance of the system.
An Automatic Offset Compensation Sense Amplifier Featuring High Readout Reliability for SRAM

With technology nodes aggressive scaling down, offset voltage has been emerging as one of the critical readout reliability issues of SRAM. This paper proposes a method named automatic compensation sense amplifier (ACSA), to tackle it by identifying the direction of offset and compensating automatically. This method obtains and stores the offset direction through one-time sensing, and then auxiliary transistors in corresponding branches are turned on for current compensation. The circuit implementation of the method is evaluated with SMIC 55-nm technology, operated at a supply voltage of 1.2 V. The proposed ACSA achieves a 66.19% reduction in voltage offset compared to conventional Current Launch SA (CLSA), and a 30.6% reduction compared to conventional Voltage Launch SA (VLSA).
Improving Lung CT Analysis through Fuzzy Dilated Convolution Attention

Computed Tomography (CT) is a versatile imaging modality that can detect and evaluate a wide range of diseases and conditions affecting organs in the thoracic cavity. Computer vision methods, particularly, deep learning architectures using attention mechanisms, have been extensively used for analysis of CT images. However, there is still a need for increased accuracy when it comes to pulmonary CT analysis owing to the complex structures visible in the images. In this study, we propose a novel convolution layer, called fuzzy dilated convolution and develop an attention module. We build an encoder-decoder network using the proposed module and train it on two large publicly available lung CT datasets - LUNA16 and LIDC-IDRI. The performance of the proposed network surpasses state-of-the-art segmentation models on both the datasets. The implementation is available at github.com/Swadesh13/Fuzzy-Dilated-Convolution.
Temperature Dependent Optimization for Specific On-Resistance for 900 V Superjunction MOSFETs: Numerical Calculation and Comparison

Considering the practical applications on superjunction (SJ) structure MOSFET under different temperature, a MATLAB based temperature dependent optimization approach for specific-on resistance (Ron,sp) under breakdown voltage (BV) of 900 V is proposed in this paper. Based on the constraints of two-dimensional (2-D) electric field distribution and impact ionization integral, the temperature dependent models of carrier mobility, impact ionization rate, intrinsic carrier concentration, and impurity incomplete ionization are adapted. MEDICI simulation is used for verification of the results, manifesting good agreement between theoretical calculations and simulation results at low aspect ratios (AR). Application instructions are provided based on our optimization in this paper, providing compromised design choices which satisfy BV and Ron,sp under different temperatures.
ImmunoFISH for Mice and Baboons Frozen Sections

1. Tissue 2. OCT 3. 4% formaldehyde 4. PBS 5. Triton 6. Goat serum 7. BSA 8. Primary antibody: 53BP1 #NB 100-304 rabbit from Novus 9. Second antibody: goat anti-rabbit Alexa Fluor® 488 Dye 10. Triton 11. Glycine 12. Mowiol 4-88 reagent (Calbiochem®) 13. Formamide 14. Tris HCl, pH 7.4 15. Telomeric PNA probe (TelC-Cy3 from PANAGENE, catalog number: F1002-5) 16. Tween-20 17. DAPI 18. Hybridization mixture (see Recipes) 19. Blocking reagent (Roche Diagnostics, catalog number: 11096176001) (see Recipes) 20. Wash solution I (see Recipes) 21. Wash solution II (see Recipes)
Ultra-Low Field Magnetic Resonance Image Enhancement based on Deep-Learning Method

In recent years, there has been growing interest in ultra-low-field (ULF) magnetic resonance imaging (MRI) technology aimed at addressing the limitations of high-field MRI systems, including high cost, immobility, and maintenance challenges. Under ultra-low background magnetic field conditions (<100 mT), MRI signals are weaker, and the overall signal-to-noise ratio of the k-space data used for imaging is poor. Additionally, the image resolution is often lower to shorten the scan time. These two factors significantly impact image quality. To address these issues, it is essential to enhance image quality during the image reconstruction and post-processing phases. This paper initially processes the k-space data into a low-rank matrix and performs preliminary noise reduction using the Singular Value Decomposition (SVD) algorithm. After image reconstruction, a deep convolutional denoising network is applied for denoising in the image domain. Finally, the denoised image data undergo super-resolution using bicubic interpolation, resulting in enhanced ULF MRI images. These methods, trained on 1.5T high-field MRI image data, are applied to 54 mT ULF MRI brain imaging. The processed image quality is significantly superior to the original images. This advancement is expected to enhance the value and significance of ULF MRI in clinical applications and promote the expansion of MRI technology.