Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressed Sensing via a Deep Convolutional Auto-encoder

The nonlinear recovery is not promising in accuracy and speed, which limits the practical usage of compressed sensing (CS). This paper proposes a deep learning-based CS framework which leverages a deep convolutional auto-encoder for image sensing and recovery. The utilized auto-encoder architecture consists of three components: the fully convolutional network acts as an adaptive measurement matrix generator in the encoder; while in the decoder, the deconvolution network and refined reconstruction network are learned for intermediate and final recovery, respectively. Different from most previous work focusing on the block-wise manner to reduce implementation cost but result in blocky artifacts, our adaptive measurement matrix is applicable to any size of scene image and the decoder network reconstructs the whole image efficiently without any blocky artifacts. Moreover, dense connectivity is leveraged to combine multi-level features and alleviate the vanishing-gradient problem in the refined reconstruction network which boosts the performance on image recovery. Compared to the state-of-the-art methods, our algorithm improves more than 0.8 dB in average PSNR.
Data Augmentation using GAN for Multi-Domain Network-based Human Tracking

This paper presents an on-line data augmentation method for discriminative Convolutional Neural Network(CNN)-based human tracking. Different from randomly sampling around the object, we propose a novel hard negative mining method based on Generative Adversarial Networks (GAN). In order to increase distraction and decrease the redundancy of negatives, the samples with similar appearance with positives generated by GAN generator are treated as hard negatives. Moreover, we integrate this hard negative mining with on-line updating mechanism into multi-domain network (MDNet)-based tracking framework, which makes the network become more discriminative as the learning proceeds. Experimental results on existing tracking benchmark demonstrate the effectiveness and robustness of our proposed method, especially for the long time tracking.
A Wide-Range Resonant Pressure Sensor With Enhanced Sensitivity Based On An Indirect Coupling Scheme

In this paper, a novel resonant pressure sensor based on an indirect coupling scheme is presented. An in-house fabricated circular piezoelectric microelectromechanical system (MEMS) resonator is used. The device is mounted on top of the diaphragm, and pressure is subsequently applied to the diaphragm's bottom surface. Finite element analysis is conducted to comprehend stress-displacement coupling between the diaphragm and resonator and the resulting frequency shift caused by pressure. The first flexural mode with a frequency of 152.3 kHz is used for sensing pressure. The sensor exhibits an exceptional combination of high sensitivity and wide bandwidth. The device records a pressure sensitivity of 11021.07 ppm/bar over 0 to 10 bar. The sensor showcases minimal hysteresis error for multiple measurement cycles. Methods for hysteresis mitigation are also demonstrated. This work introduces a conceptual framework for pressure sensors that offer good mechanical robustness, high sensitivity, and readout stability throughout a wide range.
Heart Rate Monitoring System During Physical Exercise for Fatigue Warning Using Non-invasive Wearable Sensor

Exercise provides many benefits for the body. For someone who has heart problems, exercise should be aligned according to the heart condition. This paper proposes to report a system of monitoring heart rate in real time based on Android by using photoplethysmograph (ppg) signals to provide early warning in anticipation of fatigue during running exercise. The contribution of this study is an algorithm to define the level of fatigue when a user running exercise. During exercise, the heart rate monitoring system in the application will record heart rate data in real time and give a warning message in the form of vibration on the user’s smartphone device when the heart rate is too fast or over the maximum limit. Monitoring heart rate using an Android application and easy pulse plug-in sensor. The easy pulse plug-in sensor is placed on the user’s fingertips to record heart rate data using photoplethysmograph (ppg) signals when the user do the exercise. The heart rate detection by the easy pulse plug-in sensor is done in the microcontroller, and then the heart rate data is sent to the smartphone via Bluetooth communication media. System testing is done by running exercise using a test scenario in the form of variations in age, running speed and length of running period to see the effect of physical activity on the number of heart rates. The test results show that the accuracy of heart rate detection is 95.923% and the android application can give a warning when the user’s heart rate more than the normal limit when running.
Development of HMM Based Automatic Speech Recognition System for Indian English

Automatic speech recognition system converts recorded audio speech signal into text output. Speech recognition has variety of applications in various domains. Hidden Markov Model (HMM) is widely used statistical approach in speech recognition system. The proposed work represents a speaker independent continuous speech recognition system for Indian English speakers using Hidden Markov Model Toolkit (HTK). Mel frequency cepstral coefficients (MFCC) are used as a feature vector. The results for automatic speech recognition system using HTK in different experiments are presented. These three different experiments includes cross-validation mode, without adapting HMMs and with adaptation of HMMs. Also the comparison in the accuracy of the recognized speech is discussed.
A Method Based on Background Subtraction and Kalman Filter Algorithm for Object Tracking

Today the world is facing major problem in terms of public security for which visual surveillance system is required. Object tracking gains lots of interest for active research in applications such as video surveillance, vehicle navigation, video compression etc. Different techniques are developed for object tracking purpose but that suffers from degradation in performance due to occlusions, complex shapes and illuminations. In this proposed work, discovery of the moving item has been finished utilizing straightforward background subtraction and a Kalman filter algorithm. Following algorithm has been actualized and tried on Matlab 2013a (64 bit) with working framework windows. Method compares and analyzes various performance measures with other algorithms.
Structured Compressive Sensing for Channel Estimation in OFDM System with ESPAR Antenna

Electrical steerable passive array radiator (ESPAR) antenna is promising a low-power multi-antenna system which has a comparable performance to the multiple-input multiple-output (MIMO) system. However, the main drawback of the ESPAR antenna adaptation into the current generation of orthogonal frequency division and multiplexing (OFDM) systems is its channel estimation. In the ESPAR-OFDM system, channel estimation is required for each antenna element received signal. Moreover, the input for the channel estimation process is the pilot tones combination from all antenna elements. Compressive sensing (CS) based channel estimation has been proposed as a solution for the ESPAR-OFDM channel estimation. However, the CS method still suffers from huge matrix multiplication complexity. This paper proposes a structured CS method which can overcome the matrix multiplication problem in the CS based channel estimation for the ESPAR-OFDM system.
Compact 3-D Printed Rectangular Waveguide Diplexer With Wide Stopband Response

This paper presents compact, 3-D printed rectangular waveguide diplexer with wide stopband response. The two channel filters are designed by using miniaturized TM cavities with wide stopband response achieved using the recently proposed spurious self-suppression method. The channel filters are then connected using a ridged waveguide T-junction to achieve diplexing operation. Moreover, presence of TZs in TM structures allows the achievement of better isolation between the two channels. The two channels have a center frequency of 9.6 GHz and 10 GHz respectively, with a FBW of approximately 2%. Both the channels have wide spurious free ranges up to 20 GHz. Additive Manufacturing (AM) 3-D printing technology is exploited to manufacture the proposed diplexer prototype. Measured results are in good agreement with the simulation.
Impact of Double-Row Height Standard Cells on Placement and Routing

In this article we propose a systematic method to study how increasing use of double-row height cells would impact placement and routability of a circuit. To facilitate our study, we handcraft 87 standard cells, each of which has two layouts of the same area, one designed with single-row height and the other designed with double-row height. Experimental results show that the compromise made by placement, later turned into routability degradation, for handling double-row height cells reaches its peak when a circuit has about 30% of the cells being double-row height cells if placement is done with a core utilization of 90%. It is about 40% if a core utilization of 80% is employed.
S3ntinel: An Extensible Static Analysis Framework for Android Applications

The lack of customizable Android static analysis tools has caused analysts to devote a large chunk of the company resources on developing custom tools that become irrelevant once the project is complete. Lack of sufficiently maintained documentation for the available static analysis frameworks has further added fuel to the fire. With more and more malwares being released into the Android market, there is an urgent need for a an extensible static analysis framework that can be utilized to develop tools on the fly without reinventing the wheel and thus saving a lot of time and money. The proposed solution attempts to fill that void.
A Wideband GaAs pHEMT LNA Multi-band 5G mmW Communication

This paper presents a millimeter-wave (mmW) wideband low noise amplifier (LNA) fabricated in $0.15-\mu \mathrm{m}$ GaAs pHEMT process. The $LC \pi$-type input matching network and gain-distributed technique are utilized to achieve the wideband low noise and high-gain performance. The proposed $L N A$ achieves a 3-dB bandwidth $(BW)$ of 25-43 GHz, with a small-signal peak gain of 24.8 dB. The lowest noise figure $(NF)$ is 2.1 dB, and the average $NF$ is 2.7 dB. The dc power consumption is only 79 mW under 2-V supplied voltage. This LNA demonstrates wide bandwidth, low noise, and high gain under a low dc power consumption.
A Hybrid HHO-AVOA for Path Planning of a Differential Wheeled Mobile Robot in Static and Dynamic Environments

This study presents a hybrid HHO-AVOA which is a novel optimization method that combines the strengths of Harris Hawks Optimization (HHO) and African Vulture Optimization Algorithm (AVOA) to address the path planning challenges encountered by differential wheeled mobile robots (DWMRs) navigating both static and dynamic environments, while accommodating kinematic constraints. By synergizing the strengths of both algorithms, the proposed hybrid method effectively mitigates the limitations of individual approaches, resulting in efficient and obstacle-avoiding navigation towards the target within reduced timeframes. To evaluate its efficiency, the proposed approach is compared against HHO and AVOA as well as other established methods which include whale optimization, grey wolf optimization and sine-cosine algorithms. Simulation results along with Monte Carlo analysis consistently demonstrate the superior performance of the hybrid method in both environments. In static scenarios, the hybrid algorithm achieves an average reduction of approximately 14% in path length and a 17% decrease in DWMR travel duration. In dynamic cases, it outperforms the rest with an average reduction of 27.6% in path length and a 27.2% decrease in travel duration. The algorithm’s low computational complexity is also exhibited via its fast convergence during path optimization which is a crucial attribute for real-time implementation, particularly in dynamically changing environments that demand quick decision-making. The superiority of the proposed hybrid method to balance the exploration and exploitation is also affirmed through a Wilcoxon rank-sum test with a 95% confidence interval.
The Data Communication Transmitter and Receiver for a 5.8 GHz AIPAA MPT-Sensor System by ICEt Technology

This paper presents the fabrication of an Active Integrated Phased Array Antennas (AIPAA) sensor system prototype utilizing Information, Communication & Energy transfer (ICEt) technology. Through the high-density integration of hardware components and software algorithms, this system enables adaptive adjustments in diverse scenarios, leading to optimal system performance. The proposed prototype demonstrates the capability to fulfill sensing, communication, and energy transfer tasks in various environments, including spacecraft exploration and river disaster warning systems. This research represents a significant step towards the development of advanced wireless sensor networks with ICEt technology for multifunctional applications.
Fairness Aware Regularization on a Learning-to-Rank Recommender System for Controlling Popularity Bias in E-Commerce Domain

Recommender systems aim to recommend items for certain users based on rating prediction, user preferences, or other parameters. Along with their advantages in guiding users to appropriate items, recommender systems potentially experience a popularity bias. It is a condition where popular items will often be recommended, while non-popular items will never be recommended. Controlling popularity bias is necessary to introduce new, but appropriate, items. It can be done by making a fair proportion of popular and non-popular items to be recommended to users. One of the methods is by applying fairness aware regularization. This method can increase the proportion of medium-tail recommendations of non-popular items, without sacrificing ranking performance on the recommender system. We combined this method with a learning-to-rank algorithm and applied it in a recommender system for e-commerce. Based on the testing results by using a dataset of Amazon review, the method is able to increase the proportion of medium-tail recommendation of non-popular items, up to 54.1%, on the measurement of Average Percentage of Tail (APT) and gain 0.281 on the measurement of Normalized Discounted Cumulative Gain (NDCG) score. In addition, the method is also able to introduce different medium-tail non-popular items to users.
Traveler's Recommendation System Using Data Mining Techniques

Nowadays, Recommender Systems are being used in multiple different domains. This paper mainly focuses on application of recommendation system in tourism. For planning a trip tourists usually searches for destination information like reviews, route from source to destination, start time, end time, address, accommodations, nearby attractions, etc. Sometimes tourists may consult travel agencies to plan their trip. Tourist has to plan their trip according to agencies available packages. Each tourist's interest and need are not considered when trip is planned by travel agencies. People in agencies convince tourist for their packages even if tourists are not interested. Generalized packages are offered by travel agencies and does not give freedom to tourist to choose their hotels, restaurants according to their choice and need. To solve this problems we propose a system in which tourists will define their need, interest, hobbies then system provide recommendations like best places to travel which are nearer to his current location, points according to season, hotels, route based on his/her interest, so that tourist can plan their own trip. This system is also providing which places are mostly visited by other tourists and reviews of the places by other tourist who has visited that place, so that it will help tourist to decide place to visit.
CNX-B2: A Novel CNN-Transformer Approach For Chest X-Ray Medical Report Generation

Medical imaging techniques are the most popular non-invasive methods to diagnose chest diseases. Chest X-ray scans are employed commonly to detect chronic obstructive pulmonary diseases and other respiratory diseases. Despite the significance of these diagnostic methods, the process of disease detection and the subsequent task of CXR report writing is tedious for radiologists. Therefore, Automated radiological report generation is a highly desirable task for radiologists. Previous studies were focused on the automated generation of medical reports to achieve greater quantitative scores rather than focusing on the quality of reports. Such approaches suffer from the problem of generating normal reports for CXR with diseases. Additionally, the absence of clear segregation between normal and abnormal samples in publicly available datasets its impossible to evaluate the performance of models in generating rare abnormal reports. To address these issues, we propose CNX-B2 which is a Convolutional Neural Network (CNN) combined with a Transformer approach to generate medical reports. The proposed encoder is designed to be both hybrid and efficient, capturing meaningful spatial features through inherent convolution biases. This enables the transformer-based decoder to robustly convert these features into coherent medical reports. Secondly, we also introduce a new radiological report dataset to evaluate model performances on abnormal reports separately. Our proposed model is further evaluated on the IU-Xray dataset, achieving competitive scores of 0.479 BLEU-1, 0.188 METEOR, and 0.586 CIDER.
An Angular-Stable FSS Unit Cell Design Using PNN Model and Genetic Algorithm

A frequency selective surface (FSS) unit cell using machine learning model with genetic algorithm is presented. A Probabilistic Neural Network (PNN) model with a genetic algorithm is used for unit cell optimization to find the best angular stability. The proposed unit cell is in 2-bit coding, four types of unit cells, with phase responses of $\pi$/2, 0, $-\pi$/2, and $-\pi$. These unit cells can achieve reflection characteristic from 24.25 to 29.5 GHz, which belongs to 5G new radio band n257 and n258. This structure has excellent reflection coefficient and angular stability. To demonstrate their ability to control scattering patterns, a periodic FSS on FR4 substrate is designed and fabricated. By measured results, the reflection angle is consistent with simulation prediction.
A Minimally Invasive Dual-Slot Antenna for Microwave Hyperthermia Treatment

This work proposes a minimally invasive dual-slot antenna operating at 2.45 GHz for microwave hyperthermia treatment. The proposed antenna is based on a substrate-integrated coaxial line (SICL) and is characterized by the radiation structure of two slots at the end of the SICL. The slots are well-spaced to mitigate the backward current so that a localized ablation zone can be achieved. Besides, the proposed antenna has a small cross-section area and hence minimal invasiveness. A prototype of the antenna is fabricated and measured to show its effectiveness. Comparisons between simulation and experiment results are presented and analyzed. As a result, the proposed antenna can serve as a promising candidate for minimally invasive microwave ablation.
Improvement of Whole-Slide Pathological Image Recognition Method Based on Deep Learning

The recognition and classification of whole-slide pathological images is the core technology of computer-aided diagnosis of cancer. This paper proposes a new data set construction method to improve the computer-aided diagnosis method based on deep learning. Taking the pathological image of breast cancer as an example, the image was preprocessed by the otsu algorithm, and the amount of calculation was reduced by removing the blank area caused by tissue slide production. The noise picture database was established, and the data set was established by two different division strategies. The pre-trained googlenet model is used to train it; then the trained model is used to classify and diagnose pathological images. The experimental results show that the model AUC of the improved data set reaches 0.8410. An improved whole-slide pathology image recognition method based on deep learning is expected to be more widely used in clinical practice, making cancer diagnosis more efficient.
Evaluating User Performance with Interactive Communication Media Algorithms in Networking Applications

This examine goals to become aware of and degree consumer performance with interactive communication media algorithms in networking packages. To gain the objectives, a chain of experiments were conducted the usage of diverse networking programs including disbursed systems and cloud computing. The consequences suggest that consumer performance is suffering from various aspects including verbal exchange media type, network latency, and records availability. Furthermore, the outcomes also display that the interactive communique media algorithms can drastically enhance person performance for diverse networking packages. These findings offer valuable insights into reducing latency and improving user overall performance in networking applications…
Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies

Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout. By minimizing electrode impedance of small electrodes (<10 μm) via surface modification, we could maximize the signal-to-noise ratio to electrically visualize the propagation of axonal EAPs and to isolate single-unit spikes. Due to the large amplitude of LFP signals, recording quality was high and nearly independent of electrode size. These findings should be of value in configuring in vitro and in vivo microelectrode arrays for extracellular recordings with high spatial resolution in various applications.