Miracle-dz/UniFAR-FTUs huggingface dataset:
Handwritten Mathematical Expression Recognition Using Convolutional Neural Network

Recognizing mathematical expressions on raster images usually consists of two steps: detecting individual symbols and analyzing their spatial structure to form a coherent equation. In this work, we focus on the first step and propose a detection method that is able to locate small and difficult handwritten symbols. We use a deep convolutional neural network with robust detection performance. It is able to achieve a mean average precision score of 0.65 for 106 different mathematical symbols on the dataset we created. For structural analysis, we use the DRACULAE parser since it has high accuracy given that the symbols were correctly detected
PmA: a solution for people mobility monitoring and analysis based on WiFi probes

The analysis of people mobility in urban contexts is of key importance to tackle major issues in different fields, such as those related to urban planning, citizen safety, telecommunications services planning, public transport service deployment. Such an analysis should provide key indicators, such as crowd density per area of interest, people flows, recurrent mobility patterns, mobility heat maps, just to cite a few. In the present paper, we provide a solution named PmA, which relies on the monitoring of WiFi traffic, which is also solicited through the generation of probe requests. The main features of the solution are the followings: preservation of user privacy, extraction of key metrics on user return and permanence, computation of mobility heat maps. Extensive sessions of monitoring and analysis have been carried out in three different pilots: an university campus during classes, an international fair, and a roundabout.
A context agnostic air quality service to exploit data in the IoE era

The upcoming IoE paradigm is taking the IoT era to a new shift, and that because of the natural inter-connection of processes, people, devices and stakeholders. From the smart city perspective, the main goal is to make well-informed decisions, on the base of a variable number of sensors and sources, exposing different data with different protocols and structures. The urban contexts may change, and with them, the number of sensors deployed. The novel smart city service must go beyond an integration strategy, it needs an exploitation model to optimally retrieve useful and highly contextualized information. In this paper we focus on the development of a model which fuses together the IoE potential and machine learning techniques for the cognitive smart city: retrive useful intelligent information, optimally exploiting the infrastructure the specific physical context may offer. We propose an approach and related techniques for realizing context agnostic services, namely services that do not depend on the enabling infrastructure beneath. The purpose is to create an IoE-based self-contextualizing service, which potentially consider the entire range of data that is being collected in smart cities and use such data to provide highly-personalized information about each environment, i.e., information that best suits the context of each Smart City. To prove the proposed context agnostic service, we take into account the air quality observation issue: we provide two high-contextualized informative services to leverage data related to two different physical environments, thus building location awareness for different geographic areas and stakeholders. But still managed by the same application which can adapts itself. Finally we present the evaluation of this prototype to illustrate the benefits of our solution and the future work.
An 8-channel 2.1 μ W0.017 mm2 0.04% Gain Mismatch Bio-potential Recording AFE using Group-Chopping Technique

An 8-ch bio-potential recording AFE with less than 0.04% gain mismatch is presented. The proposed Group-Chopping Instrumentation Amplifier (GCIA) exploits group-chopping scheme with 8-phase non-overlapping clocks to achieve the lowest gain mismatch reported to date and improves between-channel CMRR among all channels, while mitigating dynamic offsets at once. The proposed GCIA is the first in literature to mitigate gain mismatch for more than 2 channels. Fabricated in $0.18 \mu \mathrm{m}$ CMOS, it consumes 2.1$\mu$ W/Ch. under 0.5V supply and achieves NEF of 2.1, while occupying 0.017mm2/Ch.
Performance Analysis of LoRa Modulation with Residual Frequency Offset

The rapid development of Internet of Things (IoT) puts forward much requirements for wireless communication technology. Low Power Wide Area Networks (LPWAN) are designed for low bandwidth, low power, long range and large number of connected IoT applications. As one of the LPWAN, Low Power Long Range Transceiver (LoRa) described as a Frequency Shift Chirp Modulation (FSCM), is widely concerned and studied. This paper analyzes the physical layer of LoRa, presents a basic mathematical signal processing model, and proposes a demodulation algorithm based on Fast Fourier Transform (FFT). The simulation performance of the demodulation algorithm is compared with the theoretical performance one of quadrature modulation incoherent detection. The results show that the performance derived through these two approaches is consistent. In addition, the performance of LoRa system with residual frequency offset under different spread factor (SF) is analyzed, and verified that the performance of the system decreases with the increase of SF. So we have to adopt better algorithm to correct frequency offset, otherwise the coverage will decrease significantly.
A 4MS/s 10b SAR ADC with integrated Class-A buffers in 65nm CMOS with near rail-to-rail input using a single 1.2V supply

We present a 10b differential SAR ADC integrated with unity gain (Class-A) voltage buffers, operating from a single supply voltage 1.2V and handling near rail-to-rail inputs. The two differential inputs are first compared and depending on the comparison result, the inputs are either swapped or not, after which these signals are buffered, sampled and converted. This way each of the two buffers needs to handle only half of the full-scale range which enables operation of the Class-A buffers at the ADC supply voltage while providing an overall near rail-to-rail (full-scale) input range for conversion. The buffered ADC can handle 2VP-P differential input and consumes 149μW at 4MS/s to achieve a state-of-the-art Walden FoM of 87fJ/conversion-step including buffers. The buffered ADC was designed in a 65nm CMOS process and occupies an active area of 0.04mm2.
A Distributed Broadcasting Protocol Based on Local Information Sensing in IoV

Internet of Vehicle (IoV) is playing an increasing important role in constructing Intelligent Transport System (ITS) of safety, efficiency and green. Safety applications such as Emergency Warning and Collision Avoidance require high reliability and timeliness for data transmission. In order to address the problems of slow response and local broadcast storm existing among waiting-based relay strategy of emergency message, a Local topology Information Sensing technology based broadcast (LISCast) protocol is proposed in this paper, making use of the advantage of probability-based forwarding scheme in redundancy inhibition. According to the beacons broadcasted periodically between vehicles, LISCast collects neighbor's number and distribution, from which the characteristic information such as effective candidate number and maximum distance are extracted. Through embedding them into the header of broadcast packets, the alternative receivers uniformly schedule rebroadcasting priorities in a distributed and adaptive way at the cost of little overhead due to absence of roadside unit. The simulation results show that LISCast improves the ability to adapt to dynamic topology by optimizing the performance of delay and redundancy upon the condition of guarantee the transmission reliability.
Drivers'Visual Search Behaviour: Eye Tracking Analysis Approach

Visual is one of the stimuli that is defined as driver distraction factors. Visual information in displays format is the most important attention property that distracts the driver attention when driving task. In this study, we reported how drivers look into and process information in visual displays along Ir H Juanda street Depok. Eye tracking techniques to analyze comprehension of visual displays presented by video formats. In this experiment, fixations and attention map were used to report drivers' attention. Research demonstrated that eye movements recording could be used in potentially recommended to provide important insights into driver's attention in getting visual information. In this case study conclude that traffic signs which contain road directions in the above and in the middle located of the road have greater attention than other signs which is located along Ir H Juanda Depok street.
Improving the Method of Interference Holography to Determine the State of Plasma Membranes

Due to the fact that blood in the human body plays a crucial role for the flow of vital metabolic processes -the study of red blood cells is of great scientific and medical interest. This is due to the fact that the erythrocyte morphology has changes not only in hematological diseases, but also reflects the state of the organism as a whole. The paper proposed a technical solution that uses the method of holographic interferometry to obtain a three-dimensional image of human erythrocytes. The paper also suggests optimization of the formation of the reference and object beam of rays in the device, which significantly improved its technical and economic characteristics.
RoboTICs: Implementation of a Robotic Assistive Platform in a Mathematics High School Class

The Mathematics program created by the Mexican Ministry of Public Education (SEP) aims to have high school students with active development of a critical, logical thinking, creativity, and broad general knowledge to be able to argue and structure their ideas practically and logically. Although there have been constant changes in educational programs to improve, Mexico has consistently placed itself in the last places of The Program for International Student Assessment (PISA), a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. One of the reasons given is that Mexican students are not sufficiently motivated during classes, resulting in a decline in academic qualifications. In the present report, we developed and implemented an experimental protocol that allows us to improve the levels of focusing on high school students during a math class using an NAO robot. Three different scenarios were studied: one with a mathematics teacher traditionally giving a complete class, another where the robot teaches the course and finally one where both the teacher and the robot cooperate to provide explanations and exercises. Electroencephalogram (EEG) readings were used in the students to observe levels of attention, while a group of Psychology students monitored their physical behavior through a body language analysis protocol.
Location-Based Greedy Pilot Assignment for Cell-Free Massive MIMO Systems

In recent years cell-free massive multiple-input multiple-output system has attracted considerable attention for promising large spectral and power efficiency. However, pilot contamination, a well-known challenge in cell-free massive MIMO, limits these aspired gains. Alongside, and since 2000, Location Based Services (LBS) have rapidly emerged and enabled users to know their current location information for various purposes. In order to reduce pilot contamination and improve system performance in cell-free massive MIMO, this paper proposes an efficient location-based greedy pilot assignment (LBGPA) algorithm. Specifically, by following the principle that two users in close vicinity of each other are not assigned the same pilot sequence and utilizing the advantage of greedy pilot assignment (GPA), the pilot contamination can be reduced significantly. Numerical experiments show that the proposed algorithm outperforms other conventional pilot assignment schemes in typical cell-free massive MIMO systems.
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.
Facial Expression Recognition Algorithm Based on LDN and SVM

This paper proposes a facial expression recognition algorithm based on LDN and SVM. The image in the JAFFE facial expression database is used as the experimental object. The image preprocessing is performed by the histogram equalization method. The LDN feature values of facial expressions are extracted, and then the facial expression feature histogram sequence is classified and trained, and the image was identified by using the trained classification model. Through the simulation test on the MATLAB platform, the experiment proves that the facial expression recognition method is feasible and effective.
Comparison of Two Methods Between TOPSIS and MAUT In Determining BIDIKMISI Scholarship

Bidikmisi is one of the scholarships provided to students in universities, including Mulawarman University. To get the scholarship, students must meet the requirements and standards set by the government. The problem is the number of applicants who ask for Bidikmisi scholarship to make the decision maker must be fair, fast, transparent and objective in deciding who is eligible for a scholarship. There are two methods in this study to compare the accuracy of the decision of the scholarship recipient; namely TOPSIS method and MAUT method. In some studies, The Technique For Order Of Preference By Similarity To Ideal Solution (TOPSIS) method has been used in the case of Bidikmisi scholarship acceptance. While the method of Multi-Attribute Utility Theory (MAUT) is a new method and not many researchers are using it. Therefore, in this study conducted comparison method between TOPSIS and MAUT. The test data of Bidikmisi scholarship acceptance test using 150 students in 2017 with National Selection of State University Entrance (SNMPTN), 100 students accepted and 50 unaccepted students. The result of accuracy is done by comparing the original data with both methods so that the accuracy of TOPSIS method is 48% and MAUT method is 94,667%. Based on the analysis of the two methods, an application has been developed that compares MAUT method and TOPSIS method for Bidikmisi scholarship selection.
On Six Directions Chain Code and Its Application of Bead Weaving

This paper presents a new six directions chain code based on the classical Freeman code. It can be applied to graphics that involves hexagonal cells or boundaries. Compare to 8 directions chain code, 6 directions chain code is distortion-free under rotations. This paper also discusses the weaving path problem in bead weaving especially for the ring bead cushion using 6 directions chain code. According to the description of the periodicity and regularity of the weaving path, the algorithm flow chart is provided. Computer simulation shows that 6 directions chain code can precisely picture the path of bead weaving. The main contribution of this paper are to create a new application field of six directions chain code and provide the theoretical basis for designing and manufacturing a bead weaving machine.
Two-Port Frequency-Domain Measurement of Material Parameters Using Network Analysis Method

The two-port transmission line method has been described and used to determine the complex (relative) permeability µ and permittivity ∊ of materials under test in the frequency range from 10 MHz to 6.5 GHz by means of a vector network analyser. The µ and ε were extracted from the measured scattering parameters or corresponding reflection and transmission coefficients using Nicolson-Ross-Weir algorithm. For high-frequency measurements, a segment of coaxial transmission line with 50 Ω of characteristic impedance was made and verified. Measurement results of a (representative) soft magnetic material are presented, and some phenomena (or effects) such as phase displacement fault and half-wave divergence are also discussed.
The Combinations of Loss Functions and Schemes for Mammographic Classification

The analysis of mammographic images effectively is a burning question recently. So the research of binary classification whether a whole mammographic image has masses or not is proposed in this paper. The two main issues are the class imbalance of INbreast database and the effect of classification. To address these, focal loss, center loss and three kinds of schemes are proposed. Focal loss is put forward to address class imbalance by multiplying a modulating factor. Center loss concerns about enhancement of discriminative power by handling inter-class features, that is similar to the first two schemes. Moreover, the third scheme is used to solve the class imbalance with $L_{1}$ norm. The experimental analyses demonstrate the effectiveness of the combination of diverse loss functions and schemes. The convergence rate increases to varying degrees.
Analytical Drain Current Model of UTBB SOI MOSFET with lateral dual gates to Suppress Short Channel Effect

In this paper, we present a 2D analytical modeling of UTBB SOI MOSFET by introducing a gap in the gate for which this new structure behaves like a dual gate MOSFET and compared the result with TCAD simulation. A 2D Poisson's equation is used for solving surface potential profile, electric field distribution, threshold voltage, DIBL and drain current of UTBB SOI MOSFET structure through parabolic approximation method. A comparative study for increasing negative voltage on control gate of this structure has been carried out. Here we observe surface potential profile, electric field distributions, threshold voltage, DIBL and drain current through applying negative voltage on the right gate of the proposed structure. Result reveals that this structure have higher efficacy to reduce short channel effect (SCE) due to the existence of step change in the surface potential distribution and for increasing negative control gate voltage this structure provides better performance for suppression short channel effect.
An mm-Wave Synthesizer with Low In-Band Noise and Robust Locking Reference-Sampling PLL

A two stage mm-wave frequency synthesizer with low in-band noise and robust frequency locking is presented in this paper. A type-I reference sampling PLL is utilized in the first stage to provide low in-band noise while achieving robust locking. A reshaping buffer is implemented to reduce the rising time on the reference clock, which relaxes the requirement on sampling capacitor and allows smaller capacitor size for equivalent in-band noise level. It generates a 9GHz intermediate clock as the reference for the second stage injection locked (IL) VCO for a 4 times frequency multiplication to generate 36GHz frequency. The wideband injection is assisted by a low power auxiliary frequency tracking loop. The chip was fabricated in a 45nm SOI CMOS technology and achieves 227fs jitter, -240dB FoM at 35.84GHz with total power consumption of 20.6mW.
Cybersecurity Analysis of Industrial Control System Functionality

The cybersecurity issue becomes increasingly important with the development of the Industrial IoT (IIoT) and Industrial 4.0 architectures. The instance of cyberattacks against infrastructures and Industrial Control System (ICS) in safety critical domains is increasing every year. How to alleviate this situation is a challenging topic. In many cases, threats to cybersecurity are only discovered after they have led to a disaster. In this paper, we are going to analyze those cybersecurity issues from the system functionality, before an attack happens. Except for considering confidentiality and availability, functional integrity and information integrity are involved as well. This allows a precise determination of cybersecurity issues during the analysis process (from the system function perspective). A safety critical ICS in a Nuclear Power Plant (NPP) is used as an example to show how to conduct the cybersecurity analysis step by step. The final analysis result is presented in Casual Fault Graph (CFG), which is illustrated by using the YBT (Why-Because-Toolkit). The attack models are illustrated to show the possible attack vectors in our analysis. Further, feasibility is provided based on these attack models for our specific system.
Reinforcement Learning based Background Segment Cleaning for Log-structured File System on Mobile Devices

With the adoption of Log-structured file system in mobile devices, the impact of background segment cleaning on system performance and storage lifetime becomes notable. Aggressive background segment cleaning solution generates excessive block migrations and impairs the endurance of NAND storage device, while a lazy solution cannot reclaim enough segments for subsequent I/O requests thus leading to the occurrence of foreground segment cleaning and prolonging I/O latency. In this paper, a reinforcement learning based approach is proposed to balance the trade-off. Through learning the behaviors of I/O workloads and the statuses of logical address space, the proposed approach can adaptively reduce the frequency of foreground segment cleaning by 68.57% on average, and decrease the number of block migrations by 71.10% over existing approaches.
A Novel Driver less SRAM with Indirect Read for Low Energy Consumption and Read Noise Elimination

The modern electronics gadget has influenced tremendously every aspects of life. The demand to add more and more functionality has forced to increase the performance of the processor. To ensure a robust data supply to the processor a high performance, stable and low power SRAM is also of utmost necessity. An indirect read SRAM cell is proposed here which eliminates the read noise insertion to increase the data stability. It also consumes 41% less energy compared to the conventional SRAM cell. The SRAM cell is designed to be written single ended using only one write access transistor. The cell reduces the energy consumption by reducing the short circuit current and also reducing the number of leakage path. The cell also has a high write speed since the storage data node is a floating node and not connected to the ground.