Miracle-dz/UniFAR-FTUs huggingface dataset:
Parylene-Based Cuff Electrode With Integrated Microfluidics for Peripheral Nerve Recording, Stimulation, and Drug Delivery

A novel Parylene C-based peripheral nerve interface that combines both electrodes and microfluidic channels in an adjustable cuff was designed, fabricated, and characterized. This minimally invasive interface incorporates a drug delivery system for targeted delivery of lysing agents and neurotrophic factors to the nerve surface to locally disrupt the epineurium and allow fascicular selectivity. Multiple platinum (Pt) electrodes were embedded in the microfluidic channels for neural stimulation and recording. The lyse-and-attract cuff electrode (LACE) uses a simple locking mechanism that is adjustable for close contact with nerves of varying diameters. Devices were fabricated using standard Parylene microfabrication techniques resulting in low variability and high device yield. A procedure for the implantation of the LACE was developed and successfully demonstrated in vivo around rat sciatic nerves. The adjustable locking mechanism demonstrated adequate holding strength and fit around the nerves. Benchtop electrochemical characterization of the thin-film Pt electrodes showed that the electrodes possessed high charge storage capacity (>1 mC/cm2) and low impedances ( $< 2~\text{k}\Omega $ at 1 kHz) suitable for neural stimulation and recording. As expected, embedded electrodes demonstrated higher impedance values. Acute neural recording from the rat sciatic nerve verified the capability of the LACE to record evoked neural activity (compound action potentials). Controlled and localized microfluidic infusions were achieved at low flow rates ( $< 1~\mu \text{L}$ /min). Finally, infusion experiments in vivo demonstrated targeted drug delivery to the sciatic nerve fascicles. This multifunctional peripheral nerve interface has the potential to enhance implant-tissue integration in vivo and provide reliable chronic performance not available in the existing extraneural or intraneural interfaces. [2018-0192]
FPGA Based Implementation and Area Performance Analysis of Sigma-Delta Modulated Steepest Algorithm for Channel Equalization

FPGA is now a common approach for implementing a wide range of DSP systems from simple to complex. Sigma-delta modulation (SDM) technique in combination with short word-length systems is attractive for almost all DSP applications. In this work, we design an adaptive channel equalizer on MATLAB and FPGA using sigma-delta modulation techniques to implement an improved steepest descent algorithm. Further, for functional validation and area performance analysis, the design is compared with its corresponding multi-bit implementation. The area-performance analysis validates the SDM as a useful technique for word length reduction.
Image Segmentation Method Based on Hierarchical Region Merging's NCUT

Image segmentation is the foundation of image analysis and scene understanding. In this paper, a novel image segmentation method based on hierarchical region merging's Ncut is proposed. The image is divided into several regions using SLIC firstly. The region represented with superpixels are clustered by Graph-based Ncut algorithm, and then the clustered regions are further merged by hierarchical region merging algorithm. The Experiment shows that our method can reduce the cost of segmentation time and improve the segmentation results.
Recommendation of Points-of-Interest Using Graph Embeddings

The rapid growth of Location-based Social Networks (LBSNs) has lead to the generation of massive datasets which are collected in an exponential rate. The collected information may be used to facilitate users' needs with recommendations related to their past preferences. Many recommendation models were introduced in the literature, which learn by the history of users and provide recommendations for Points-of-Interest. Unfortunately, most of them ignore the relation existing among the temporal properties, the spatial attributes and the periodicity of the check-ins. In this work, we present a novel methodology, named JLGE, that combines all aforementioned factors into one unified approach which facilitates POI recommendations. In particular, the model jointly learns the embeddings of six informational graphs i.e., two unipartite (user-user and POIPOI) and four bipartite (user-location, user-time, location-user, and location-time) into the same latent space and personalize the recommendations based on these embeddings. We have experimentally evaluated the accuracy of our model using two real-world datasets in terms of the top-n POIs recommendations. The performance evaluation results indicate a significant improvement in accuracy, in comparison to another state-of-theart graph-based approach.
Design and Integration of Fiber Fronthaul with Soft PHY for OAI-BASED RAN

For high-speed communications need, Fronthaul, a new mobile architecture related to RAN-based networks, has been widely used in 4G/5G mobile network. Compared with the traditional architecture of the baseband station, Fronthaul is divided into baseband unit (BBU) and remote radio head (RRH). Due to the separation, the interface between BBU and RRH requires more effort to handle the data rate transmission. In this paper, we propose the fiber Fronthaul to achieve the high bandwidth requirement. The BBU includes the SoftPHY OAI running on Intel I7-6700 CPU and the data collector implemented by Xilinx VC707. The RRH is implemented by Xilinx ZC706. And the RF front-end is implemented by Analog Devices ADFMCOMMS5-EBZ. The interface between the SoftPHY OAI and the data collector is PCIe, and the interface between the data collector and the RRH is optical fiber. It is important that we design the synchronization scheme to synchronize and calibrate the real-time clock (RTC) of different RRH. Therefore, the proposed fiber Fronthaul with SoftPHY OAI can achieve the long distance, stability and high speed transmission.
A Review of Factors Influencing the Implementation of Secure Framework for in-House Web Application Development in Malaysian Public Sector

Every year, web applications have expanded their presence in more areas in financial organizations, health organizations, public sector, retail and accommodation. Security is important in data protection so as not to be infringed by unauthorized parties. If the vulnerabilities found are not amended, it leads to cyber-attacks such as Structured Query Language Injection Attack (SQLIA) performed by certain parties which enable them to gain unauthorized data access. To cater security issues, variety of security frameworks for secure software development life cycle (SDLC) were introduced. Secure SDLC is created by integrating security-related activities to an each phase of in used development methodologies such as waterfall model or agile model. However, the application security problem continues to grow. Strict, complicated and heavyweight frameworks are underutilized due to several factors. The factors that influence the implementation of secure SDLC identified in public sector (the scope is State Secretary Offices in Malaysia) are inadequate development timeline, improper development team size and less awareness of team members' workload. It is agreed that integrating security at earlier (requirement and design) phase is the most effective and cheapest way to develop secure web application. Hence, an adaptive secure SDLC model is proposed to integrate security activities using Fuzzy Analytic Hierarchy Process (FAHP) focusing on the influence factors as the main criteria and meet the international and local secure frameworks standards. The proposed model will recommend adaptive security activities as a guideline to be applied at earlier phases of SDLC to help eliminate/ minimize the web application vulnerabilities and increase the application security and implemented as a proof-of-concept prototype at selected Malaysian public sector for in-house web application development.
Improved Nonparallel Hyperplanes Support Vector Machines for Multi-class Classification

In this paper, we present an improved nonparallel hyperplanes classifier for multi-class classification, termed as INHCMC. As in the nonparallel support vector machine (NPSVM) for binary classification, the ε-insensitive loss function is adopted in the primal problems of multi-class classification to improve the sparseness associated with the nonparallel hyperplanes classifier for multi-class classification (NHCMC) where the quadratic loss function is used. Experimental results on some benchmark datasets are reported to show the effectiveness of our method in terms of sparseness and classification accuracy.
Unpredictable Bits Generation Based on RRAM Parallel Configuration

In this letter, a cell with the parallel combination of two TiN/Ti/HfO2/W resistive random access memory (RRAM) devices is studied for the generation of unpredictable bits. Measurements confirm that a simultaneous parallel SET operation in which one of the two RRAMs switches to the low-resistance state is an unpredictable process showing random properties for different sets of cells. Furthermore, given a device pair, the same device switches during subsequent write operations. The proposed cell is also analyzed under different current compliances and pulse widths with the same persistent behavior being observed. The features of the proposed cell, which provide data obfuscation without compromising reliability, pave the way for its application in physical unclonable functions for hardware security purposes.
Influence of Secondary Constructions on Transverse Forces of Linear Induction Motors in Curve Rails for Urban Rail Transit

In this paper, the influence of the flat and cap secondary, which are usually applied in the linear metro system on transverse forces of linear induction motors, is calculated and analyzed. First, the cap-aluminum plate and lamination back iron of the cap secondary caused relative difficulty in the analytical calculation. Then, the equivalent of the cap-aluminum plate and lamination back iron is presented, and two equivalent coefficients are obtained. The two types of the asymmetry are discussed and the different magnetic motive forces are presented. Second, the analytical model and the method of the linear induction motor are presented. Third, for a cap secondary in practice, the two equivalent coefficients are calculated and discussed. Then, the flat and cap secondary are applied, and the different transverse forces are calculated and analyzed. For two types of the secondaries, the different eddy currents and air-gap flux densities are compared. Finally, the test workbench is built and the analytical result of the transverse force is experimentally validated. It shows that cap secondary has less influence on the railcar when the transverse displacement occurs.
Manchu Word Recognition Based on Convolutional Neural Network with Spatial Pyramid Pooling

Manchu character recognition is important in protecting and researching Manchu culture and history. Previous methods of Manchu character recognition are mainly based on conventional machine learning using shallow artificial selection features, thus recognition results are unsatisfactory. The method with convolutional neural networks achieves high accuracy on optical character recognition as the convolution operators can automatically extract deep structure features. The convolutional neural network needs input images with the fixed size, but as a kind of phonemic language, the Manchu word has an arbitrary length. So it is needed to normalize the size of images if applying conventional convolutional neural network directly on Manchu word recognition. This normalization process will restrain the promotion of Manchu character recognition accuracy. This paper utilizes the spatial pyramid pooling layer instead of the last max-pooling layer in a convolutional neural network, and proposes a classifier for recognizing the arbitrary size Manchu word without segmenting the word. Without need of normalizing image sizes, the proposed model obtains the better recognition accuracy. The experiments indicate that the proposed Manchu word recognition models achieve the highest accuracy of 0.9768, higher than the conventional convolutional neural network. Furthermore there is no normalization on input images with arbitrary sizes in recognizing process. The proposed Manchu word recognition models outperform conventional counterparts in both accuracy and flexibility.
WiFi-Based Adaptive Indoor Passive Intrusion Detection

Passive intrusion detection, which is an emerging technique to detect whether there exists any intruders in monitored area, is widely used in home security and smart home, etc. Up to now, various indoor fine-grained passive human intrusion detection systems using WiFi signals have been proposed. However, those existing detection systems mostly rely on elaborate off-line training process, which hampers fast deployment of wireless devices and also reduces system robustness. To response those problems, in this paper, we propose APID, a system for adaptive indoor passive intrusion detection, which enables adaptive, device-free human intrusion detection in indoor environments using channel state information (CSI) of WiFi signals. Firstly, APID evaluates dispersion of CSI amplitude, which is not affected by the mean amplitude. Secondly, APID extracts CSI amplitude dispersion ratio between two adjacent time windows as sensitive metrics for intrusion detection. Then, the hypothesis testing is utilized to achieve no-calibration human motion detection. Finally, we implement APID on the commodity WiFi devices and evaluate it in two typical indoor scenarios. The experimental results show that APID can achieve an average detection accuracy of more than 96%.
Predicting Worker Disagreement for More Effective Crowd Labeling

Crowdsourcing is a popular mechanism used for labeling tasks to produce large corpora for training. However, producing a reliable crowd labeled training corpus is challenging and resource consuming. Research on crowdsourcing has shown that label quality is much affected by worker engagement and expertise. In this study, we postulate that label quality can also be affected by inherent ambiguity of the documents to be labeled. Such ambiguities are not known in advance, of course, but, once encountered by the workers, they lead to disagreement in the labeling – a disagreement that cannot be resolved by employing more workers. To deal with this problem, we propose a crowd labeling framework: we train a disagreement predictor on a small seed of documents, and then use this predictor to decide which documents of the complete corpus should be labeled and which should be checked for document-inherent ambiguities before assigning (and potentially wasting) worker effort on them. We report on the findings of the experiments we conducted on crowdsourcing a Twitter corpus for sentiment classification.
Low Leakage Electromagnetic Field Level and High Efficiency Using a Novel Hybrid Loop-Array Design for Wireless High Power Transfer System

In this paper, we first proposed a novel hybrid loop array (HLA) for low leakage electromagnetic field (EMF) level and high efficiency in a wireless high power transfer system. The proposed HLA effectively enhances the system efficiency and shields leakage EMF in a wireless power transfer (WPT) system using kHz range resonant frequency. The key originality of the proposed HLA is the combination of two types of loop coil; shielding loop coil (SLC) and amplifying loop coil (ALC). SLCs reduce leakage EMF, and ALCs significantly enhance the magnetic field from a Tx coil. The simulation and experiment results show that the proposed solution successfully overcomes the limitations of the existing solutions. Analytical modeling and design procedure are introduced and discussed. In addition, the experimental verification of the simulation result is included. We first designed and modeled an HLA considering the coupling effect of neighboring loop coils to evaluate its efficiency and leakage EMF. With the proposed HLA, we demonstrated a 9.36% improvement in the efficiency and 3 dBm reduction in the leakage EMF near the WPT system.
Weber Local Descriptor with Double Orientation Line Feature for Finger Vein Recognition

Weber Local Descriptor (WLD) is a simple and powerful local descriptor which consist of the differential excitation and the orientation. However, WLD has not been successful in finger vein recognition. In this paper, we propose a novel local descriptor for finger vein recognition, called Double Line feature Weber Local Descriptor (DLWLD), which improves two components of WLD. The differential excitation is redefined by bringing in Sobel operator, which can increase the discrimination of edge-texture. Meanwhile, the gradient orientation is replaced by double Modified Finite Radon Transform (MFRAT) orientation to obtain discriminative line feature. Moreover, cross-matching algorithm is utilized to better evaluate the similarity of features. The proposed descriptor is implemented to finger vein recognition in two public datasets. Experimental results demonstrate that DLWLD is better than WLD and other local descriptors.
User-Centered Design in Emerging Citizens Initiatives: A Study of E-Petition in Indonesia

E-petition, as a part of e-government application, has the potential to support citizens in voicing their aspiration to the government. E-petition has become an important part in e-participation implementation. It is widely used as an e-democracy tool. To be effective, e-petition should involve citizen participation. However, the absence of citizen initiatives has become an issue in developing e-petition. Besides, the current studies focus only on the developed country, where e-government has well-established. The case study of user-centered e-government in developing country is still under research. Therefore, this study aims to propose e-petition prototype by using User-Centered design (UCD) methodology in Indonesia. This paper has completed the users' in-depth interview and observation to analyze their needs and problems. As a result, this study has identified four main features that can emerge user to participate in e-petition, including a statistical diagram, push notification to the government, petition support organization and blog posting.
Biyoloji Öğretmenlerinin Laboratuar Kullanımı Öz-Yeterlik Algılarının İncelenmesi

Bu arastirmanin iki temel amaci vardir. Birincisi biyoloji ogretmenlerinin laboratuvar kullanimi oz-yeterlik algilarini belirlemek icin gecerli ve guvenilir bir olcme araci hazirlamak, ikincisi ise; biyoloji ogretmenlerinin laboratuvar kullanimi oz-yeterlik algilarini ogretmenlerin kisisel ozellikleri acisindan incelemektir. Arastirma betimsel nitelikte hazirlanmis bir alan arastirmasidir. Arastirmanin calisma grubunu 126 biyoloji ogretmeni olusturmustur. Arastirma verilerini toplamak amaciyla arastirmaci tarafindan “Laboratuvar Kullanimi Oz-Yeterlik Algi Olcegi” gelistirilmistir. Arastirma sonunda gecerlik ve guvenirlik calismasi yapilan Laboratuvar Kullanimi Oz-Yeterlik Algi Olceginin hazirlanmasinin yaninda, biyoloji ogretmenlerinin laboratuvar kullanimi oz-yeterlik algi duzeylerinin orta duzeyde oldugu belirlenmistir. Diger taraftan biyoloji ogretmenlerinin laboratuvar kullanimi oz-yeterlik algi duzeyleri ile cinsiyetleri ve kidemleri arasinda anlamli bir fark bulunurken, bu farkin bayan ogretmenler ve 10 yilin altinda kideme sahip ogretmenler lehine oldugu saptanmistir
Image Conversion in Multiple Domains Based on Gan

Generative Adversarial Networks can be used to generate clear images, but in different domains of the image conversion, for example, a picture from a man to a woman, or from hair to baldness, many methods use multiple models to transform input images rather than single model, which may cause the artifacts. There is no quantitative and qualitative way to evaluate the experimental results. Based on the idea of Generative Adversarial Networks, this paper can use a single model to convert multiple domains of images. After the conversion is completed, the pre-training is used to classify the images. The experimental results show that the method can realize image conversion between multiple domains and can better evaluate the experimental results.
An Efficient Reconfigurable Framework for General Purpose CNN-RNN Models on FPGAs

Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made great progress in machine learning community. Combining CNN and RNN can accomplish more general and complex tasks. Many specially designed hardware accelerators on FPGA or ASIC have been proposed for CNN or RNN, yet few of them focus on CNN-RNN-based models for general purpose applications. In this paper, we propose a complete design framework for deploying general-purpose CNNRNN-based models on FPGAs. We use Deephi Aristotle and Descartes IPs to build an efficient and reconfigurable hardware system with the support of Deephi's toolchains and Xilinx SDSoC environment. We also design a CNN-RNN-based co-optimization method which can find the IP configuration to achieve the maximum throughput under the given FPGA resources and neural network models. Our implementation on the Xilinx ZU5EG FPGA achieves the throughput of 690.76GOPS and the energy efficiency of 86.34GOPS/W on LRCN network.
Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network

In today's world automatic license plate recognition play an important role in monitoring and organizing vehicles. In this paper, we propose a method of detecting and recognizing the license plates of vehicles in an automatic way in our country. The system can be used to collect toll, in car parking and find stolen vehicles. We have used different image processing techniques like resizing image, image binarization, connected component analysis, image enhancement and noise filtering. Our system is composed of four main modules, such as detection of the license plate area, extraction of license plate. Then, segmentation of characters and words and finally recognition of the characters and words. As Bangladesh Road Transport Authority (BRTA) imposed a common standard for vehicle license plate, the size and aspect ratio of all license plates are same. We have used a threshold value to detect and extract the license plate based on our analysis. Afterward, for character segmentation, we used connected components. Later, we used deep learning tool ‘the Convolutional Neural Network’ for character recognition. Due to the lack of a standard data set, we have developed a customized dataset of Bangladeshi number plate for the implementation of our method. The accuracy of our proposed system is 95.42%.
All-Optical Signal Processing Techniques for Flexible Networks

This tutorial highlights challenges and opportunities in achieving efficient flexible optical networks. Optical signal processing may potentially increase network flexibility because of its functions’ transparency, tunability, and reconfigurability. We review recent advances in high-speed optical signal processing techniques that might enable flexible networks. Various optical approaches that enable key functions are discussed, including format conversion, increases in spectral efficiency, and phase-sensitive operations. We also discuss the potential utilization of basic enabling technologies, such as optical frequency combs and optical nonlinear devices.