Miracle-dz/UniFAR-FTUs huggingface dataset:
Bangladesh Metropolitan Crime Area Prediction Using Decision Tree

Today's world faces many problems with crime, affecting the day to day livings and bringing general socio-economic progress to a standstill. Different types of crimes happen daily and nightly. If it cannot be carefully noticed or managed, it would be a great disaster for any country. Therefore, this paper was aimed at predicting metropolitan Bangladesh at different crime rates in different times. In this paper, different types of machine learning techniques could be used, but Decision Tree was used based on crime quantity to forecast Bangladesh's metropolitan area and finally analyze the result depending on the algorithm's result. In this paper we focused on Metropolitan Police Aare like DMP, CMP, KMP, RMP, BMP, SMP, Railway where D means Dhaka, C means Chittagong, K means Khulna, R means Rajshahi, B means Barisal, S means Sylhet, and MP means Metropolitan Police.
Research on Computer Aided Diagnosis System for Neurocritical and Critical Diseases

Emergency medical care is closely related to people's lives, is related to the well-being of the people, and can also reflect the level of health services in a region. The ability of good emergency medical services has become an important indicator to measure the capacity of social governance. In today's society, people's needs and requirements for emergency services are getting higher and higher, and emergency medical care is playing an increasingly important role in our daily lives. The emergency treatment process is mainly composed of three parts, namely the pre-hospital emergency process, the hospital emergency process and the intensive care treatment process (ICU). This article applies neural network computing and medical image processing technology to the emergency medical system.
Blockchain for the Scalable Issuance and Verification of Private Academic Information

Blockchain technologies are attracting the interest of different actors in various sectors and, among them, the education field, which is also studying how to apply these technologies to improve the traceability, accountability, and integrity of its information. Although different initiatives and projects were launched and are being developed, there are still issues not fully resolved, such as privacy, compliance with data protection regulations and scalability. This research proposes a novel design of a system to issue, store and verify different types of academic information, which allows complying with applicable regulations, protecting the privacy of users’ personal data, as well as addressing the problem of scalability and performance.
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly find the tiling dimensions and select among alternative implementations of the causal and dilated 1D-convolution operations at the core of TCNs. We benchmark our approach on a commercial PULP device, achieving up to $103 \times $ lower latency and $20.3 \times $ lower energy than the Cube-AI toolkit executed on the STM32L4 and from $2.9 \times $ to $26.6 \times $ lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target.
Design and Development of Smart Airbag Suit for Elderly with Protection and Notification System

Human fall is the second leading cause of death by accidental or unintentional injury which leads to coma, paralysis, and brain trauma. The existing method has a hip protection airbag suit which prevents to get an injury in the trochanteric region and prevent to get fractures. The drawback of this methodology, it protects only the hip region of patients, without protecting important parts like the head, thoracic cavity. After a fall - related accident, senior patients mentally, emotionally week and getting ‘fear of repeated fall'. To rectify those problems, our innovation presents the use of an airbag suit with a certain sensor to protect the life of elders and also gives an alarm message to care-takers. After identifying hazardous slant, the micro controller must start a sign which can enact the engine driver to expand an airbag consciously positioned kind of a suit. The airbag garment is stretchable fabric that is made up of nylon or polyamide cloth filled with nitrogen or argon gas which will inflate in case of sudden fall, to avoid severe injury in the head, thoracic region, vertebrate column, and pelvic region. With help of tri-axis accelerometer sensor and through GSM the emergency notification sends to the care-taker.
Design and implementation of Strong Physical Unclonable Functions on Field Programmable Gate Array

Cryptography study provides confidentiality, availability, authentication, and integrity of information by creating ciphers using a secret key. Such operations are possible unless and until the key is securely placed at some memory location. Once the key has been stolen, no matter how powerful the cryptographic algorithm is, the data will not remain secure. One more important thing is today Electronic Industries are facing several security issues like cloning of the ICs, reverse engineering, overbuilding, and physical tampering. Cryptography cannot help us to secure hardware. Hardware security is a completely different field. Physical unclonable functions (PUFs) are hardware security primitives that are used for device authentication and cryptographic key generation. Physically unclonable function (PUF) utilizes intrinsic properties of IC fabrication variances to provide a unique response of every device. PUF inherently provides security properties. PUF response can be utilized for authentication or IP core protection. This paper has proposed the popular PUF implementation on the Xilinx Zedboard FPGA.
Student Performance Appraisal System

In the past few years, various appraisal systems have been analyzed and studied in order to keep track of educational activities. This paper enlightens the evaluation scheme of student's performance by using a machine learning algorithm that helps us to achieve better results when a large number of data is to be handled and managed for the storage of student's data. Filtration is going to be done over the analysis system on the idea of grades or the marks of the students. Our aim is to design a system of records with user-friendly Graphical user interface which can assists student in addition to the evaluation of their performance. Although in recent days various institutions have been come up with their own appraisal systems with machine learning and advanced data analytics techniques, there are some issues regarding data and results of the students of previous years. This paper attempts to introduce such a system, which will not only work for performance appraisal but also tries to avoid network congestion and unnecessary traffic over a wireless network. K-means clustering provides versatility in filtering the student's records based on their grades will be a big factor to handle and recollect the data load from the Cassandra database which acts as a storage house of our current proposed system. Nonetheless, results which are defined as an outcome of this project are more accurate and easier to understand.
Nonequilibrium thermodynamics of porous electrodes for lithium-ion batteries

Lithium-ion batteries are increasingly important, both in portable electronic devices and as grid stabilization for intermittent renewable sources. The varied applications involve varying requirements for safety, lifetime, and energy/power density. The broad requirement space leads to a large design space, requiring either extensive and costly experimentation or effective models. To be predictive enough to facilitate design, models must be based on underlying physics. However, battery models commonly make assumptions known to be false, such as describing phase separating materials with Fickian diffusion. In this thesis, we build on existing battery models by modifying key parts to better capture fundamental phenomena including transport and reactions in phase separating materials. First, we introduce a model of lithium transport and surface reactions within particles of graphite, which has phase separation and is the most common anode material in lithium-ion batteries. We demonstrate key features of the model, including a sensitivity to its electrochemical reaction kinetics as well as its ability to capture both single particle and porous electrode experimental data. Second, we connect a model of electrochemical kinetics that is well-established in the chemistry community to nonequilibrium thermodynamics and apply it to materials with phase separating electrodes. We demonstrate that, although it shares some characteristics with a commonly used phenomenological model, it makes distinct predictions which agree with certain experimental results. Finally, we unify these single-particle models within a volume-averaged model to describe battery behavior at the scale of full porous electrodes. The developed model and simulation software have already been applied by other researchers to help explain behavior of batteries with phase separating materials. Thesis Supervisor: Martin Z. Bazant Title: E. G. Roos Professor of Chemical Engineering Professor of Mathematics
Diagnosis of Ophthalmic Diseases in Fundus Image Using various Machine Learning Techniques

Many ophthalmic diseases occur due to damage in the retina, optic disc, and macula of the eye. This can cause permanent vision loss or blindness. Ophthalmology has begun to work extensively on identifying eye diseases in morphological datasets using image-based diagnostic techniques. A conventional technique fails to predict these disease categories because this technique is time-consuming and required clinical experts to predict them. This review surveys the different methods used for the detection of ophthalmic diseases such as cataracts, age-related macular degeneration, hypertensive retinopathy, myopia, etc.
Quad-Port Circularly Polarized MIMO Antenna for K band Applications

In this paper, a compact (25×25×1.6 mm3) four-port Hammer shaped patch antenna for K band (18-27GHz) applications is presented. Hammer shaped antenna work as band pass filter (BPF)) filtenna works like a communicating antenna for interweave CR (cognitive radio). The proposed antenna has| S11| less than −10 dB impedance bandwidth of 14.20% (20.86-24.05GHz) & 14.31% (20.96-24.19)at port 1 & port 4 respectively. The −3dB ARBW of 2.3 GHz (20.3-24.05 GHz) confirms the circular polarization attributes. Maximum isolation between the four radiating elements is less than −22dB and envelope correlation coefficient (ECC) less than 0.05 and the value of DG obtained between ports 1–2, 1-3& 1–4 value range of 9.999 - 10 dB, 9.979 – 9.999 dB & 9.90 - 9.99 dB respectively is observed. Four-port MIMO patch antenna has been investigated in terms of simulated gain, return loss, radiation efficiency, radiation pattern & total active reflection coefficient (TARC). The proposed antenna is analyzed using HFSS software by ANSYS version 17.
The Moderating Effect of Compatibility Factor in The Usage of E-Government Services Among Malaysian Citizens

The primary aim of the study was to investigate the role of E-government services among citizens of Malaysia. Unified theory of acceptance and use of technology (UTAUT) was used in this descriptive study for testing how the Malaysian citizens perceive technology usage in E-government services utilizing e-applications. The purposive and snowball sampling was applied in choosing the respondents. Self-administered questionnaire was used in gathering primary data and 381 usable responses were obtained. The approach of PLS was applied for the data analysis. The outcomes show support to all UTAUT hypotheses. From the outcomes, Compatibility (C) appears to partially moderate the relationship of Performance Expectancy (PE), Social Influence (SI), Facilitating Condition (FC), Effort Expectancy (EE). Overall, the citizens appear to be well adapted to the utilization of electronic technology for general E-government services. Nonetheless, the impact of electronic technology being a tool appears to be restricted within E-government, similar to the finding of past works. This implies the possible presence of other factors that decrease the mobile apps usage for services, aside from technology. The knowledge regarding citizen’s behaviour within the progressive domain of E-government is expanded in this work. In practice, this study assists E-government practitioners in comprehending the citizen’s needs.
Medicinal Plant Species Classification Using Neural Network Classifier

There is a growing need for proper classification identification of medicinal plants as they played a very important role in our life. However, identification of such biological entities are rather challenging as they are not govern by any mathematical function. In this paper, the classification of six different species of medicinal plants are discussed through various morphological, color and texture features. The image data set is comprised of a total of 90 sample leaves images of six different species of medicinal plants that are acquired from suburbs of Assam, India. All features were used separately for the classification task and in combination for improved precision. For effective leaf recognition, the Back Propagation Neural Network (BPNN) classifier is used. The test results on 90 leaf images of six different varieties highlight the proposed method to yield 98.88% accuracy. The results shows that the proposed method is accurate in identifying leaves with different morphological, color and texture features.
Design of Composite Microstrip-Monopole Antenna With 180$^\circ$ 1 dB Beamwidth Based on Complementary Sources Concept

A new perspective based on complementary sources radiation is presented for analyzing a widebeam composite microstrip-monopole antenna. The typical cavity model theory shows that the radiation patterns of the microstrip antenna could be calculated by using two parallel equivalent magnetic currents, and the E-plane beamwidth of the traditional microstrip antenna is mainly determined by the array factor. Loading the monopoles through the metal patch of the original microstrip antenna can construct complementary sources. Two monopoles are symmetrically loaded and two pairs of complementary sources are excited. Interestingly, the two pairs of complementary sources are aligned with in-phase magnetic current radiation sources and antiphase electronic current radiation sources, which indicates two heart-shaped radiation patterns pointing in opposite directions. The total far-zone radiated fields synthesized by the two heart-shaped radiation patterns can present wide E-plane beamwidth performance. An example antenna working at 4.4 GHz is designed for verification. With the introduction of a differential feeding scheme for a symmetrical radiation pattern, the E-plane radiation pattern can cover the upper half plane, i.e., $180^{\circ }$, with a fluctuation of less than 1 dB and an antenna gain of over 3 dBi.
Single-Phase Five-Level Quasi-Switched Boost T-Type Inverter

This paper presents a new combination of a quasi-switched boost network and a single-phase five-level T-type inverter for enhancing the boost factor of the converter. The new modulation scheme for the single-phase five-level quasi-switched boost inverter is carried out to reduce the complexity of inserting shoot-through state without affecting the output voltage amplitude. The comparison of the proposed inverter with the single-phase five-level buck-boost inverters is presented to demonstrate the effectiveness of the proposed inverter. The simulation and experimental results are given to validate the accuracy of the proposed topology.
Concept of an Intuitive Human-Robot-Collaboration via Motion Tracking and Augmented Reality

Human-cobot interaction is one of the main aspects of the 4th industrial revolution. One goal of current robotic research is to enhance safety and efficiency by designing the collaboration in a more intuitive way. The following work introduces two concepts to improve human-cobot collaboration on the basis of deep learning and augmented reality to achieve a more efficient and pleasant working environment. The first concept uses meta learning and Gaussian process to predict the movement of the human worker. The second concept enables the worker to see the next assembly step of the robotic arm via augmented reality.
SRAM Gauge: SRAM Health Monitoring via Cells Race

By shrinking transistors’ dimensions and, consequently, reducing the operating voltage in nano-scale CMOS technologies, the stability of SRAM cells has become a major reliability concern. SRAM cells’ robustness against undesirable bit-flips is commonly measured by Static Noise Margin (SNM). Degradation in SNM is mainly because of the gradual variations in transistors’ parameters due to aging. This work proposes a built-in SRAM health sensor capable of monitoring the SNM of individual SRAM cells in a memory block. The sensor is composed of extra non-operational sensor cells with different predefined SNMs. These sensor cells are put in a race with operational SRAM cells to determine their strength. The precision, sensing range, and robustness of the proposed sensor against process variation are adjustable at the cost of small area overhead. In our simulation setup, with the area overhead of 0.29%, the sensor monitors a wide range of SNMs from 275mV to 325mV, with a precision of 5mV.
Event-Based Set-point Tracking Based on a State Observer

We propose an event-based framework for set-point tracking involving three linear time invariant systems, a real, a reference and an observer system. To be actually event-based using only the knowledge of the output of the real system, we show that the two systems (reference and observer) involved in the event generator have to take into account the output observation error. The resulting setup is proved to be able to follow a Lipschitz predefined set-point trajectory with no Zeno phenomenon.
Bio-based amines through sustainable heterogeneous catalysis

The production of amines from biomass is a growing field of interest. Particularly the amination of bio-based alcohols receives a lot of attention. In this review, we discuss recent progress in the development of efficient heterogeneous catalysts. The substrate scope for the production of bio-based amines is not limited to (hemi)cellulosic alcohols. Other platform chemicals that originate from different biomass fractions, such as lignin, oils, chitin and protein, are also suitable feedstock for the production of amines. This comprehensive review first provides an overview of the available bio-based feedstock candidates. The following section is devoted to the sustainable reaction routes that are available to carry out the desired amination reactions. Next, state-of-the-art technologies are summarized for each substrate class, focussing on heterogeneous catalysis. Special attention is dedicated to the sustainability of the discussed reaction routes. Finally, a critical discussion is provided, together with current challenges and future perspectives regarding the industrial production of bio-based amine chemicals.
Analysis of Orbital Angular Momentum Channels in Bent Inverse Raised Cosine fiber

Orbital angular momentum (OAM) has gained a widespread interest in diverse areas especially in telecommunication due to its capability to elevate the capacity transmission and substantially improving the spectral efficiency of optical communication in optical fibers. In this work, we numerically investigate the bending impacts on OAM channels transmitted through inverse raised cosine few mode fiber (IRC-FMF). The obtained results show that the investigated OAM-Fiber supports 8 OAM data carriers, already presented in straight case, even under tight bend (i.e. at bending radius Rb=4 mm) with maximum loss of 0.0001 dB/m. This confirms the great tolerance of IRC-FMF to bending condition and confirms the capability of such fiber to handle robust OAM channels even under realistic environment.
Design and Simulation of Medical Assistance Robot for combating COVID-19

This The number of applications for a humanoid robot is at an all-time high, and one such area where humanoid robots could be used is the hospital sector. The Covid-19 pandemic which started in 2019 has accelerated the research in this field. This paper proposes a novel design and simulation of a semi-humanoid robot which could be used in a hospital environment. The robot includes a 5 degrees of freedom robotic arm which could be used for pick and place functionality. The pick and place functionality of the robot is viewed in MoveIt. The robot is simulated in a hospital environment using gazebo to view how the robot interacts in it.
Design of Intelligent Tourism Etiquette Training Framework based on Attitude Detection System

Tourism industry is an open and extremely external industry. When carrying out tourism work, it is highly required to frequently communicate with people from different walks of life. At this time, the importance of etiquette is highlighted. Staff in the communication, the performance of the etiquette norms, visitors from all over the country can have a communication role. In this paper, the advantages of these technologies are complemented by multi-mode information fusion, and a series of targeted design optimization are carried out combined with the use scene of flexible exoskeleton, and a complete set of flexible exoskeleton posture detection process is proposed.
An Efficient Follow-the-Leader Strategy for Continuum Robot Navigation and Coiling

Efficient path planning for hyper-redundant continuum and snake-like robots is a challenging task due to limited sensing capabilities, high computational loads, multiple possible solutions, and non-linear models. This letter presents a new approach to snake robot navigation and coiling, with an algorithm that enables online step-by-step position adjustment with a follow-the-leader strategy, significantly improving the performance of the robot when compared to previous methods. The proposed algorithm is demonstrated on a 16-degree-of-freedom snake-like robot for inspection and maintenance tasks in nuclear facilities.
Dielectric Characterization of Anisotropic 3D-Printed Biodegradable Substrates Based on Polylactic Acid [Application Notes]

3D-printed dielectric substrates are manufactured using additive manufacturing (AM) technology. With CAD/CAM, rapid, accurate, and automatic manufacturing of objects with different characteristics and almost any geometry is possible. With 3D printing processes, objects are formed layer by layer, and complicated shapes can easily be created. For this reason, in the last decade, AM has seen a great boom in the development of RF and microwave circuits. Some applications that benefit from AM are metamaterials [1]-[3], antennas [4]-[5], and selective surfaces [6], among others. Most of the RF and microwave circuits reported in previous works are designed considering the dielectric substrate as an isotropic material; therefore, large differences are found between the measured and the simulated results [6]-[8]. These variations are largely due to the fact that the manufacturing standards generally include a mixture of materials inside the substrates with a periodic arrangement [9], and this favors dielectric anisotropy in the substrates. Consequently, the study of these materials is of great importance to the scientific community. This article is intended for designers who wish to tailor the dielectric characteristics of a substrate for microwave circuits, noting that anisotropy will manifest itself at lower frequencies when the internal structure of the printed material is modified.