Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Outage-Constrained Rate of Skip-Sliding Window Codes

We consider binary skip-sliding window (SSW) codes which satisfy certain weight constraints over a skip-sliding window. When on-off keying is employed, these weight constraints ensure real-time energy content in the transmitted signal. For a given energy requirement and battery size at an energy harvesting receiver, we investigate the maximum achievable rate using SSW codes which avoid energy outage at the receiver. The SSW codes generalize sliding window constrained (SWC) codes and subblock energy constrained (SEC) codes; we show that SSW codes with window length equal to twice the skip-length can outperform both SWC and SEC codes in terms of outage-constrained rate.
Iris Feature Extraction and Matching Method for Mobile Biometric Applications

Biometric methods are increasingly penetrating the field of mobile applications, confronting researchers with a huge number of problems that have not been considered before. Many different interaction scenarios in conjunction with the mobile device performance limitations challenge the capabilities of on-board biometrics. Saturated with complex textural features the iris image is used as a source for the extraction of unique features of the individual that are used for recognition. The mentioned features inherent to the interaction with the mobile device affect not only the source image quality but natural deformations of the iris leading to high intra-class variations and hence reducing the recognition performance. A novel method for iris feature extraction and matching is represented in this work. It is based on a lightweight CNN model combining the advantages of a classic approach and advanced deep learning techniques. The model utilizes shallow and deep feature representations in combination with characteristics describing the environment that helps to reduce intra-class variations and as a consequence the recognition errors. It showed high efficiency on the mobile and a few more datasets outperforming state-of-the-art methods by far.
Automatic Recognition of Dialects of Himachal Pradesh Using MFCC &GMM

In the present globalized society, people from all over the world are coming and working together. The biggest hurdle in this process is the medium of interaction, as all individuals do not speak a common language. A way to circumvent this problem is to develop a machine based system for recognition of different languages and subsequently employing automatic machine translation mechanism for communication between people from diverse language or dialect groups. A MFCC and GMM based approach for recognition of dialects of Himachal Pradesh has been presented in this paper. Around seven dialects spoken in different regions of Himachal Pradesh have been used in this study. The results obtained shows that dialects spoken in adjacent regions have significant overlap and are difficult to distinguish. Accuracy for offline and online tests was respectively around 80% and 70%.
Condition prediction for oil-immersed cellulose insulation in field transformer using fitting fingerprint database

The results of frequency domain spectroscopy (FDS) will be affected by moisture which often leads to an unreliable condition prediction of oil-immersed cellulose insulation in transformers. In view of this, a solution is proposed in this study by adopting a fitting fingerprint database (FFD) technique, which can be utilized for condition prediction of aging and moisture content. In the current work, the relevant fingerprints that could characterize the insulation aging condition and moisture content are extracted from the FDS curves and the DC conductivity of the transformer oil. The FFD is then constructed by fitting fingerprints. The accuracy of the reported FFD technique is verified from tests on oil-immersed pressboard prepared in the lab and a transformer from the field. In this respect, the present contribution highlights FFD might be used as a powerful tool for condition prediction of oil-immersed cellulose insulation in transformers.
3D Segmentation of Pulmonary Nodules Based on Multi-View and Semi-Supervised

For large-scale CT images, the automatic segmentation of nodules is the foundation for diagnosis of various pulmonary diseases. Most existing methods have made great progress in pulmonary segmentation. But because of the similar structure between vessels and nodules in 2D, it lacks the ability to extract more discriminative features. The accuracy is still not satisfying. And the task remains challenging due to the lack of voxel labels and training strategies to balance foreground and background. To solve these problems, a 3D segmentation network of pulmonary nodules based on semi-supervised was proposed. Firstly, a framework of multi-view feature extraction was designed to solve the problem of high similarity between nodules and other tissues. It extracted features from three different views to improve precision. And three parallel dilated convolutions were added for multi-scale feature extraction. Hence, the spatial and semantic information of different sizes can be better obtained. Secondly, for the problem of identifying difficult samples, a hybrid loss function with an adjustment factor was proposed. It magnifies the loss of difficult samples, which will attract more attention from the network. And a new regularization term was introduced to avoid overfitting. The entire network was trained with a few labeled CT data set through an improved semi-supervised learning strategy, which was optimized with a new self-paced regularization. Experimental results show that the average sensitivity of the proposed method is 95.81%. It is superior to other methods in terms of precision and Dice index especially when the data set is not satisfied.
Benefits and Challenges of Model-Based Software Engineering: Lessons Learned Based on Qualitative and Quantitative Findings

Even though Model-based Software Engineering (MBSwE) techniques and Autogenerated Code (AGC) have been increasingly used to produce complex software systems, there is only anecdotal knowledge about the state-of-the practice. Furthermore, there is a lack of empirical studies that explore the potential quality improvements due to the use of these techniques. This paper presents in-depth qualitative findings about development and Software Assurance (SWA) practices and detailed quantitative analysis of software bug reports of a NASA mission that used MBSwE and AGC. The mission's flight software is a combination of handwritten code and AGC developed by two different approaches: one based on state chart models (AGC-M) and another on specification dictionaries (AGC-D). The empirical analysis of fault proneness is based on 380 closed bug reports created by software developers. Our main findings include: (1) MBSwE and AGC provide some benefits, but also impose challenges. (2) SWA done only at a model level is not sufficient. AGC code should also be tested and the models and AGC should always be kept in-sync. AGC must not be changed manually. (3) Fixes made to address an individual bug report were spread both across multiple modules and across multiple files. On average, for each bug report 1.4 modules, that is, 3.4 files were fixed. (4) Most bug reports led to changes in more than one type of file. The majority of changes to auto-generated source code files were made in conjunction to changes in either file with state chart models or XML files derived from dictionaries. (5) For newly developed files, AGC-M and handwritten code were of similar quality, while AGC-D files were the least fault prone.
Reporting Sleepy Driver into Channel Telegram via Telegram Bot

Road accident is one of the biggest causes of death. The case is dominated by a sleepy driver. Today, research related to the method of sleepiness detection has been developed. However, the detection accuracy in the previous study need to be improved. Moreover, the detection system only inform the driver itself if the driver is sleepy or drowsy while other person, such as family, or authorities did not receive this information. As a result, if accidents happen peoples would not be noticed. This has an impact on handling post-accident victims. These series could increase the risk and even bring casualties if do not taken care seriously. Therefore, it needs further development to broadcast sleepy driver information to other people such as families, the rescue center and even the highway police department. This study proposed a sleepy driver detection and information notification system implemented on smartphones. The system is capable to transmit personal information of a sleepy driver directly on a Telegram messaging app. It aims to identify a report regarding driver name, car type, and license plate and even current location. The advantage is proficient to evacuate victims as fast as possible if anything suddenly occurs to prevent undesirable situations. Some experiments that have been done prove that a system worthy to transmit information into Channel within Telegram Messenger precisely and obtain an accuracy of 89% using SVM.
FC- SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection

DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.
Automatic Detection of Ischemic Stroke Lesion from Multimodal MR Image

During ischemic stroke, the most common kind of blocking in an artery, the brain cells start to die due to lack of oxygen and nutrients. Such strokes need immediate diagnosis and treatment to minimize the brain damage. Recent studies have shown the potential of using magnetic resonance imaging (MRI) in diagnosing ischemic stroke. Reviewing hundreds of slices produced by MRI, however, takes a lot of time and can lead to numerous human errors. It is widely accepted by the medical practitioners that automated segmentation methods for ischemic stroke lesions could significantly speed up the beginning of a patient's treatment. The automated segmentation can locate the tissue with lesions and give an estimate of its volume, which helps in the clinical practice by providing a better assessement and evaluation of the risks of each treatment. These reasons highlight the need for a fully automatic ischemic stroke lesion segmentation approach using a flexible, fast and effective deep neural network. We have also investigated the use of zero-cantering and normalization of intensity for smooth variation of the intensity over the tissues as a pre-processing step. A relative study has been done to prove the efficacy of the proposed CNN model on Ischemic Stroke Lesion Segmentation Challenge 2015 database (ISLES2015).
Multi-Modal Fingerprint Presentation Attack Detection: Analysing the Surface and the Inside

The deployment of biometric recognition systems has seen a considerable increase over the last decade, in particular for fingerprint based systems. To tackle the security issues derived from presentation attacks launched on the biometric capture device, automatic presentation attack detection (PAD) methods have been proposed. In spite of their high detection rates on the LivDet databases, the vast majority of the methods rely on the samples provided by traditional capture devices, which may fail to detect more sophisticated presentation attack instrument (PAI) species. In this paper, we propose a multi-modal fingerprint PAD which relies on an analysis of: i) the surface of the finger within the short wave infrared (SWIR) spectrum, and ii) the inside of the finger thanks to the laser speckle contrast imaging (LSCI) technology. On the experimental evaluation over a database comprising more than 4700 samples and 35 PAI species, and including unknown attacks to model a realistic scenario, a Detection Equal Error Rate (D-EER) of 0.5% has been achieved. Moreover, for a BPCER ≤ 0.1% (i.e., highly convenient system), the APCER remains around 3%.
Improved List Decoding of Polar Codes by Shifted-pruning

In successive cancellation list (SCL) decoding, the list pruning operation retains the L paths with highest likelihoods. However, the correct path might be among the paths with low likelihoods due to channel noise. In this case, the correct path is eliminated from the list. In this work, we study the event of elimination of the correct path and we analyze where and how this event occurs. A modified pruning scheme named shifted-pruning over a set of low-reliability bit-channels named critical bits is proposed aiming to avoid the elimination of the correct path in additional decoding attempts after a decoding failure occurs. Shifted-pruning is realized by selecting the paths k + 1 to k + L out of the 2L ordered paths instead of the paths 1 to L. The numerical results for polar codes of length 512 and code rates 0.5 and 0.8 and list sizes L = 2, 8 and 32 show that the shifted-pruning scheme is a low-complexity equivalent to the bit-flipping scheme while it can outperform the bit-flipping method by providing 0. 25-0.5dB gain.
THE IMPACT OF HEALTH SYSTEM REFORM ON CLINICAL, PARACLINICAL AND SURGICAL INDICATORS AS WELL AS PATIENTS' SATISFACTION IN RASOUL-E-AKRAM HOSPITAL IN 2013 TO 2014

Background : The importance of reforms in the health care system and improve the quality of health care as one of the important results of it has always been noticeable. In this regard, the efficiency and effectiveness of hospital activities, proper functioning of staff and proper use of resources be evaluated by functional indicators. Hospital indicators show the status of hospital's performance and its society. In this article, we aimed to study the impact of health system reform on hospital indicators such as clinical, paraclinical and surgical indicators, Patients satisfaction in Rasoul-e-Akram hospital. Method s: This cross-sectional study was performed at Rasoul-e-Akram general hospital. The study population consisted of Rasoul-e-Akram general hospital indicators. In this regard, information collected by referring to the existing table's record from 2013 to 2014. To analyze the data, it was applied the paired t test to compare quantitative variables between the two time periods from 2013 to 2014. The repeated measure ANOVA test was also used to assess the trend of the changes in frequency of the items. The analyses were performed by the SPSS software. Results : The results of comparing surgery item between 2013 and 2014 showed increase in accessibility to surgical procedures. According to the obtained results, heath system reform in this hospital increased the amount of clinical and paraclinical reception. Furthermore patient satisfaction was also increased Conclusion : Implementation of the health reform plan had been succeeded in improvement of healthcare indicators in our center.
A Benchmark for Homework Tidiness Assessment

The homework tidiness assessment aims to auto evaluate the writing tidiness of homework, playing an important role in daily teaching. However, there is still no comprehensive basis for homework tidiness assessment. For this, a benchmark for homework tidiness assessment (HTA) is proposed. Firstly, a database named HTA 1.0 containing 1000 homework images is collected. Each image is manually annotated by multiple volunteers. Secondly, a comprehensive evaluation protocol is designed, using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and accuracy (Acc) as performance indicators. Finally, three deep learning models (i.e., LeNet, AlexNet and VGGNet) are applied as baseline methods and the results are reported and analyzed.
In-Band Scattering Reduction for a U-Slot Patch Antenna

A systematic approach for in-band scattering cross-section (SCS) reduction of a U-slot patch antenna is proposed in this letter. Characteristic mode (CM) analysis is conducted on the U-slot patch antenna to visualize the resonant behavior of the radiating patch. By examining the modal current distributions, an impedance-loading location for SCS reduction is determined. Value of the loading impedance is directly calculated from the proposed approach to cancel out the scattering-mode SCS and residual SCS. Prototypes of the U-slot patch antennas with reduced SCS and a reference antenna are fabricated. The experimental results show that a maximum in-band SCS reduction of 26 dB has been achieved with the proposed approach.
FPGA-Based Multi-Level Approximate Multipliers for High-Performance Error-Resilient Applications

This paper presents approximate multipliers which are efficiently deployed on Field Programmable Gate Arrays (FPGAs) by using newly proposed approximate logic compressors at different levels of accuracy. Our approximate multiplier designs offer higher gains of power-delay-area products (PDAP) than those of the state-of-the-art works at comparable accuracies. Furthermore, in terms of delay, occupied area, and dynamic power dissipation, our designs are much better than Lookup Table based multiplier Intellectual Properties that are available on an FPGA. Particularly, our proposed 8-, 16-, and 32-bit multipliers can deliver PDAP gains up to <inline-formula> <tex-math notation="LaTeX">$7.1\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$8.3\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$5.0\times $ </tex-math></inline-formula>, respectively. The effectiveness and applicability of our designs are also demonstrated by image processing applications such as image multiplication and sharpening. The experiments show that for the image sharpening, our <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> multipliers can deliver a good peak signal-to-noise ratio (PSNR) of 46.81 dB, a structural similarity index metric (SSIM) of 0.9989, and a dynamic power saving of up to 36.7% with regard to the exact multiplier. For the image multiplication, approximate <inline-formula> <tex-math notation="LaTeX">$16\times16$ </tex-math></inline-formula> multipliers can offer a high PSNR of 80.25 dB, an SSIM of 1.0, and a dynamic power saving of up to 58.15%. From these demonstrations, the proposed multipliers are expected to be appropriate with high-performance and low-power error-resilient applications.
Power consumption comparison of synchronized IoT devices running FreeRTOS and RIOT

The Internet of Things is becoming increasingly widespread, and with this growth arise new concerns and challenges such as IoT synchronization protocols and power consumption. Despite the advancement of research, it is challenging to find an IoT constrained device operating system (OS) that can fill all IoT needs. The purpose of this paper is to implement the synchronization protocol SiSP under IoT OSs and to compare these selected OSs according to their power consumption. Study results show that RIOT OS is a better choice than FreeRTOS due to its efficient power consumption.
Enhancing the Noise Robustness of the Optimal Computing Budget Allocation Approach

Since an optimal computing budget allocation (OCBA) approach maximizes the efficiency of the simulation budget allocation to correctly find the optimal solutions, various OCBA-based procedures, such as OCBA, OCBAm+, and MOCBA+, have been widely applied to solve simulation-based optimization problems. Recently, it has been found that the stochastic noise in a simulation model increases due to the increasing complexity of modern industrial systems. However, the OCBA approach may be inefficient for these practical problems. That is, it is very likely to waste a lot of budget on other candidates that are not truly optimal due to the abnormal simulation results, which occurs frequently in noisy environments. In this paper, we intuitively analyze the causes of this efficiency deterioration of the OCBA approach, and then a simple heuristic adjustment is proposed to enhance the noise robustness of the OCBA approach based on our analysis results. The proposed adjustment allows the OCBA approach to further consider the precision of the simulation results, thereby significantly reducing the wasted budget and increasing the efficiently. In addition, it can be applied to the existing allocation rules without modification and does not require additional computational costs. Many experimental results for the eight OCBA-based procedures clearly demonstrate the effectiveness of this adjustment. In particular, the results of practical problems emphasize its necessity.
Conditional Perceptual Adversarial Variational Autoencoder for Age Progression and Regression on Child Face

Recent works have shown that Generative Adversarial Networks (GAN) and Variational Auto-Encoder (VAE) can construct synthetic images of remarkable visual fidelity. In this paper, we propose a novel architecture based on GAN and VAE with Perceptual loss termed as Conditional Perceptual Adversarial Variational Autoencoder (CPAVAE), a model for face aging and rejuvenation on children face. CPAVAE performs face aging and rejuvenation by learning manifold constrained with conditions such as age and gender, which allows it to preserve face identity. CPAVAE uses six networks; these networks are an Encoder (E) and Sampling (S) which maps the child face to latent vector, Generator (G) takes the latent vector z as input along with age conditioned vector and tries to reconstruct the input image, a perceptual loss network Φ, a pre-trained very deep convolution network, discriminator on the encoder (Dz) smoothen’s the age transformation, discriminator on the image (Dimg) forces the generator to produce human realistic images. Here D and E are based on Variational Auto-encoder (VAE) architecture, VGGNet is used as perceptual loss network (Ploss), Dz and Dimg are convolutional neural networks. We represent child face progression and regression on the Children Longitudinal Face(CLF) dataset containing 10752 faces images in the age group [0 : 20]. This dataset contains 6164 and 4588 images of boys and girls respectively.
A Hybrid CNN–LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar

Many deep learning (DL) models have shown exceptional promise in radar-based human activity recognition (HAR) area. For radar-based HAR, the raw data is generally converted into a 2-D spectrogram by using short-time Fourier transform (STFT). All the existing DL methods treat the spectrogram as an optical image, and thus the corresponding architectures such as 2-D convolutional neural networks (2D-CNNs) are adopted in those methods. These 2-D methods that ignore temporal characteristics ordinarily lead to a complex network with a huge amount of parameters but limited recognition accuracy. In this paper, for the first time, the radar spectrogram is treated as a time sequence with multiple channels. Hence, we propose a DL model composed of 1-D convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). The experiments results show that the proposed model can extract spatio-temporal characteristics of the radar data and thus achieves the best recognition accuracy and relatively low complexity compared to the existing 2D-CNN methods.