Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Interdisciplinary Learning in a Design Thinking Course: Going to Class for a Reason

New technologies such as MOOCs make it crucial for universities to find the added value of bringing students to campus. Designing lectures that foster more interactions could address this issue, as student engagement and interaction are harder to replicate remotely and are seen as key to acquire both disciplinary and transversal skills. In this paper we present and evaluate an example of a novel interdisciplinary course on design thinking offered at our institution, which was designed to provide such active in-class learning experience. Our results show that in-class collaborative activities, cross-team peer-to-peer feedback and interdisciplinary work was well appreciated by students, yet it still remains a challenge to find equilibrium between theory, hands-on activities and reflexivity.
Eca-Router : On Achieving Endpoint Congestion Aware Switch Allocation in the On-Chip Network

As the critical pipeline stage in on-chip routers, switch allocation assigns output ports to input ports and allow flits transiting through the switch without conflicts. Previous works strive to design efficient switch allocaiton strategies by maximizing the matching at each cycle, with the information from the current cycle or multiple cycles in time series. However, those works have not taken endpoint congestion into considerations. Tree-saturation, caused by endpoint congestion, can degrade NoC performance due to the congestion fanning out from the original point to upstream routers. In this paper, a novel router design, Eca-Router, is proposed to relieve the impact of endpoint congestion by switch allocation optimization. Eca-Router detects endpoint congestion by recording the destinations of packets in switch allocation. Endpoint congestion is decided in switch allocation once there are multiple input ports competing for the same output port and the packets in these input ports contain the same destination. During switch allocation, requests that contribute to endpoint congestion will be given lower priority to be allocated, and starvation control is also introduced to ensure allocation fairness. Evaluation results show that Eca-Router is efficient in reducing packet latency.
Human Identification Using Human Body Features Extraction

A system that can mark personnel attendance becomes an increasingly important tool. It usually requires human action in order to detect the presences of a person such as putting finger on a scanner in a fingerprint detection system. There are some systems that do not need human action (such as the use of camera in a face recognition system) but it requires human face database. This paper introduces human identification system implementing human body feature extraction, which can track the presence of persons in a room. The system uses camera as input device to remove the special action needed. Features on human body was chosen because they tend to be easier to detect and serve as a strong identity to mark person who enter a room, yet we do not need to train body features first. There are two main processes in this system. First, the system detects person entering a room. Second, it also detects the same person leaving the room. This application was implemented using image processing techniques such as human detection using HOG descriptors, HSV color conversion, and template matching. Tracking failures from this application could happen because this system still could not handle some special cases.
A 16-Gb, 18-Gb/s/pin GDDR6 DRAM With Per-Bit Trainable Single-Ended DFE and PLL-Less Clocking

The graphic DRAM standard GDDR6 is developed to overcome the limitation of previous standards GDDR5/5X for achieving high-speed operation. This paper introduces 16-Gb GDDR6 DRAM with a per-bit trainable single-ended decision feedback equalizer (DFE), a reference impedance (ZQ)-coded transmitter, and a phase-locked loop (PLL)-less clocking to overcome I/O speed limitation by the DRAM process. Furthermore, this paper optimizes clock- and power-domain crossings and adopts split-die architecture to improve signal integrity (SI). This GDDR6 operates 16 Gb/s/pin with 1.15 V and achieves 18 Gb/s/pin with 1.35 V in the DRAM process.
Reinstating Dlib Correlation Human Trackers Under Occlusions in Human Detection based Tracking

Human tracking in video feeds has been a popular research area, which consists of different state of the art approaches including tracking-by-detection, feature tracking and tracking by recognition/re-identification. Dlib correlation tracker is a state-of-the-art tracking-by-detection implementation, which can be used for human tracking except under occlusions. That is, it fails to continuously track a person if that person disappears due to occlusions and reappears immediately. In order to overcome this challenge, we propose a state estimation approach to extend the capabilities of Dlib correlation tracker under occlusions along with the use of human detection. The proposed approach has proven to give better results than directly using Dlib correlation tracker with human detection and was evaluated in real-world experiment scenarios. The approach can further be improved for wider use cases and a wider variety of trackers for human tracking.
Compact Modeling and Design of Magneto-Electric Transistor Devices and Circuits

A Verilog-A based model for the magneto-electric field effect transistor (MEFET) device is implemented and a variety of logic functions based on this device are proposed. These models are used to capture energy consumption and delay per switching event and to benchmark the MEFET with respect to CMOS. Single-source MEFET devices can be used for conventional logic gates like NAND, NOR, inverter and buffer and more complex circuits like the full adder. The dual source MEFET is an enhanced version of the MEFET device which functions like a spin multiplexer (spin-MUXer). Circuits using MEFETs require fewer components than CMOS to generate the same logic operation. These devices display a high on-off ratio. , unlike many magneto-electric devices. , and they operate at very low voltages. , resulting in lower switching energy. Benchmarking results show that these devices perform better in terms of energy and delay. , for implementing more complex functions. , than the basic logic gates.
Stabilization of a Class of Cyclo-Passive Systems Using Alternate Storage Functions

This note is devoted to the stabilization of a particular class of nonlinear cyclo-passive systems, namely, gradient-like systems. In order to accomplish the control task, we explore alternate representations of those systems with the aim of identifying (new) storage functions. Then, those storage functions are used to design a passivity-based controller that addresses the regulation problem without the necessity of solving partial differential equations.
Decoupling Expressiveness and Body-Mechanics in Human Motion

Modern motion capturing systems can accurately store human motion with high precision. Editing this kind of data is troublesome, due to the amount and complexity of data. In this paper, we present a method for decoupling the aspects of human motion that are strictly related to locomotion and balance, from other movements that may convey expressiveness and intentionality. We then demonstrate how this decoupling can be useful in creating variations of the original motion, or in mixing different actions together.
Development of Kinect-based Upper-Limb Assistance Device for the Motions of Activities of Daily Living

There are many people around the world who cannot raise their arms by themselves due to the diseases or accidents, even though they can control their fingers. It will make the patients lose the function of activities of daily living (ADL) of upper-limb. In our previous research, an upper-limb assistance device had been proposed, which can be controlled by several control interfaces. However, those control interfaces require the users to operate by the other hand. According to the object position, the upper-limb motions do not have a certain trajectory. In this article, we focused on developing a Kinect-based upper-limb assistance device. We proposed the motion detection system based on Microsoft Kinect 2 to detect the human motion, and further control the device and assist the user’s upper limb. Based on the human intention, the assistive motions can be realized without preprogrammed trajectories. The motions of the device and user were confirmed by the simulation and experiment. By using the system, the users may overcome several motions in ADL without the preprogrammed trajectories.
Optimizing Power Consumption vs. Linearization in CMFB Amplifiers with Source Degeneration

In this work, a low power consumption approach to achieve linear Differential Difference Amplifier CMFB circuits is introduced. A solution based on source degeneration in the input differential pairs is proposed to reduce input range linearity limitations as well as CMFB input pair large current consumption by accurately selecting both current and degeneration resistance to maximize linearity. The proposed technique has been implemented in a CMFB amplifier of a fully differential OTA, resulting in a THD of 0.026% when using degeneration resistances of 48 $\mathbf{m}\Omega$ and 53.3 $\boldsymbol{\mu} \mathbf{A}$ of bias current. Moreover, linearity improvement using the proposed technique has shown better figures than increasing only the bias current.
A Human Body Heat Driven High Throughput Thermal Energy Harvesting Single Stage Regulator for Wearable Biomedical IoT Nodes

A human body heat driven thermal energy harvesting regulator with high output power at low input voltages and small form factor, suitable to power compact and feature-packed wearable biomedical Internet of Things (IoT) nodes, is developed in this paper. The system demonstrates <inline-formula> <tex-math notation="LaTeX">$2.3{\times }$ </tex-math></inline-formula> higher throughput than prior art at low input voltages with a maximum conversion ratio of <inline-formula> <tex-math notation="LaTeX">$83 \times $ </tex-math></inline-formula>, in order to support complex and power hungry IoT operations like onboard processing and data transmission. The peak load supportable by the system is extended by utilizing maximum power extraction and minimizing converter losses in a single-stage compact power management unit. A fixed-frequency technique, independent of input voltage variation is proposed to maximize output power when load demands. A digital switch controller with ultra low power digitally controllable delay circuit is implemented to perform zero current switching to improve efficiency. The system, fabricated in 180-nm standard CMOS process, operates from open circuit input voltages ranging from 25 to 210 mV while supplying a regulated 1 V output. The functionality of the system is demonstrated in real-time by integrating the regulator with an emulated cardiac monitoring IoT node. The system delivers a peak power of 1.03 mW at open circuit voltage of the transducer, <inline-formula> <tex-math notation="LaTeX">${V} _{\text {TEG}}$ </tex-math></inline-formula>, of 210 mV, generated at body to ambient temperature difference of ~8 °C. The total area occupied by the system is 0.13 mm<sup>2</sup> and has an end-to-end peak efficiency of 65% at <inline-formula> <tex-math notation="LaTeX">${V} _{\text {TEG}}$ </tex-math></inline-formula> of 50 mV in measurement.
Color Based HDR Image Retrieval using HSV Histogram and Color Moments

In this work, we address the problem of efficiently and accurately retrieving High Dynamic Range (HDR) images and displaying the retrieval results in Standard Dynamic Range (SDR) devices. The proposed approach considers a set of global descriptors for modeling an HDR image using vector of features that combines two color attributes: the color histogram based on the Hue-Saturation-Value (HSV) space and the color moments. The dissimilarity between images is measured using Manhattan distance. To evaluate the effectiveness of our retrieval system and conduct our tests, we created a collection of various HDR images. Experimental results demonstrate that the proposed method yields satisfactory results. It can accurately retrieve the similar HDR images. Moreover, we applied our retrieval method on Low Dynamic Range (LDR) version of the same dataset. The comparisons confirm that the features extracted from HDR images are more effective than those extracted from LDR ones.
Towards a Framework for Quality Measurement in a Tertiary Care Hospital: the Case of King Hussein Cancer Center

Following the famous institute of medicine report To Err is Human the healthcare policy makers sensed an urging need to introduce and implement quality and patient safety strategies in the US and worldwide. In response to that wave the importance of quality measurement was magnified in the light of "You cannot improve what you cannot measure". In this work we present the experience of King Hussein Cancer Center KHCC in developing and implementing an institutional approach toward quality measurement. This approach is supported by real life examples to improvement initiatives that were supported by data along the way. We also illustrated the managerial framework KHCC adopted in terms of leadership support, stakeholders engagement and patient centrality
On the Gold Nanowire-Based Hyperbolic Metamaterials

Transmission features of filter comprised of gold nanowire-based hyperbolic metamaterial was touched upon. The chosen wavelength range of operation was 400- 1400 nm. The results were obtained in respect of transmission characteristics corresponding to different values of the angle of incidence and thickness of hyperbolic metasurface. It was found that the transmission property remains greatly dependent on these parameters. The existence of stop band was attained in the near-infrared (NIR) regime of electromagnetic spectrum. Also, relatively higher transmission was found to be in the visible range. This suggests the use of such complex structured mediums in filtering applications.
Effects of SNR on removing ECG noise from EMG signal using DSWT

The electromyography (EMG) signal records the electrical potential difference across the skeletal muscle in every part of the body whereas the electrocardiography (ECG) signal measures the electrical potential difference across only the heart. For example, when the EMG signal is collected from muscles in the torso, the ECG signal coming up from the heart activity can interfere with the EMG signal. In this way, the EMG signal can be contaminated with the ECG interference during data collection. In this paper, we propose the improved optimal thresholding method based on discrete stationary wavelet transform (DSWT) in various 5-level SNR used for removing the ECG interference from the contaminated EMG signal. No prior studies have evaluated the performance evaluation of mean absolute error (MAE) and correlation coefficient (CC) for this proposed method to estimate the optimal threshold value in SNR 5 levels. The results show that the ECG interference is removed from the contaminated EMG signal by using the optimal wavelet thresholding method based on DSWT at various 5 levels of SNR. A proposed removal technique is better than the traditional thresholding method.
Audio Amplitude-Level Quantification Vector for Identification of Audio Post-Processing Operation

Audio tampering is typically followed by post-processing operations to mask the artifacts potentially perceptible by human ears and blur the traces of tampering. However, research on the issue of audio post-processing identification is still a blanket. This paper mainly introduces a method to identify audio post-processing operations. A new audio feature - Audio Amplitude-Level Quantification Vector (AQV) is proposed, then the probability distributions of AQV of audio are calculated and extracted as audio features which are then used for identification of various audio processing. During the detection, the K-Nearest Neighbors (KNN) classifier is applied for classification. Experimental results show that the proposed AQV method can not only verify the authenticity of the speech audio, but also have a significant effect on identifying different types of post-processing operations.
Multi-objective optimization for elastic dynamic load balancing in IoT networks

The pervasive presence of IoT networks in our modern life grabbed the attention of the research community to investigate ways to enhance its reliability and stability. Many of these systems are considered as mission critical due to the criticality of the applications it handles. Health related IoT networks are among those. In IoT networks GateWays (GW) can be a major bottlenecks and single point of failure. GWs handle all the traffic flowing from the sensors to the backend cloud. In this paper we propose an optimized automated workload migration mechanism between closely ad-hoc mobile GWs to enhance the mission critical IoT network's reliability and efficiency. We exploit mobile devices to work as a temporarily GW for close by IoT networks. The proposed system comes with a multi objective GW selection mechanism to select the best GW to suit each IoT network. As a demonstration of the system effectiveness, simulation results showed the effectiveness of the proposed system in minimizing the IoT network power consumption expanding the network's lifetime.
Fuzzy-Based Channel Selection for Location Oriented Services in Multichannel VCPS Environments

Location-oriented services in vehicular cyber-physical system (VCPS) have witnessed significant attention due to their potentiality to address traffic safety and efficiency related issues. The multichannel communication aids these services by tuning their overall performance in vehicular environments. Related literature on multichannel communication focuses on interference as channel quality measure. However, uncertain mobility and density of vehicles significantly affect channel quality apart from interference. The static quantification of channel quality is not suitable due to the dynamic characteristics of the channel quality parameters. In this context, this paper proposes fuzzy-based channel selection framework for location-oriented services in multichannel VCPS environments. A system model is presented for deriving channel access delay (CAD) using Markov chain model. The channel quality is estimated using CAD and signal-to-interference ratio (SIR). The fuzzy logic-based channel selection framework is developed considering fuzzification and defuzzification of CAD and SIR. The comparative performance evaluation attests the benefit of the framework as compared to the state-of-the-art techniques in VCPS.
Analysis of the Possibilities for Improvement of BlockChain Technology

Bitcoin and Smart Contract are the first major applications of the BlockChain technology. But, with increasing the number of transactions, the process of verification on every transaction is very slow. This is the reason for a third major innovation called a BlockChain scaling. The scalability is a process of taking certain steps in accelerating the performing of transactions in this new technology. In this paper we analyze the possibilities for BlockChain scalability and we examine the advantages and disadvantages of the proposed solutions.
Temporal-Contrastive Appearance Network for Facial Expression Recognition

Facial expression recognition(FER) is a challenging task even for human since individuals have their own way to express their feelings with different intensity. In order to extract commonality of facial expressions from different individuals, personality effect of individual needs to be minimized as much as possible. In this paper, we present a temporal-contrastive appearance network (TCAN) that utilizes the temporal feature to remove the personality effect. The high level feature is extracted from a video consisting of a sequence of frames through a proposed by convolutional neural networks (CNN). In order to let our CNN framework be able to extract similar features from adjacent frames, special loss function is introduced. Moreover, the neutral and peak expression frames are identified through comparison of distances among frames. Then, facial expressions can be classified by the so-called contrastive representation between neutral and peak expressions. We conducted our experiment in the most widely used databases (CK+ and Oulu-CASIA) for facial expression recognition. The experiment results show that the proposed method outperforms those from the state-of-the-art methods.
Implementation of an Embedded Testbed for Indoor SLAM

Simultaneous Localization and Mapping (SLAM) is a vital task for autonomous robots moving in unknown environments. It is a rigorous and complex problem where the signals of different modalities originating from sensors as diverse as laser range finders, IR sensors, ultrasonic transducers, odometers and inertial measurement units are used in its different variants. Expensive sensory arrangements and significant computational resources are desired in all the variants of SLAM and multi-processing hardware-software arrangements are essentially needed. Consequently, the overall system becomes quite elaborate and expensive. Another common issue is the odometry error; this error accumulates over time and the robot looses track completely or distorts the map. Generally, a notebook computer is used as the computational resource; as it is frequently placed and removed from the robot, the odometry information is significantly affected. Therefore, it is desired to do the sensor calibration with fixed and complete system hardware. In this work, a testbed development for SLAM is reported. In this design, a low-cost and light-weight FPGA SoC development board is used as the main platform along with a low-cost LIDAR sensor; both fixed on the robot and controlled wirelessly. This fixes the whole hardware configuration and hence the weight of the robot, therefore the calibration results do not alter over time. Moreover, a HW-SW co-design approach is adopted to achieve a highly capable but very compact computational resource. Post-processing of the sensory data with rigorous calibration models has resulted in reasonably precise robot's odometry and environmental perception. The complete system is tested in indoor environment and satisfactory localization and mapping results are achieved.
On the Melting of Ohmic-Heated Nanowires and Electrical Contacts

The temperature rise in a nanowire or an electrical contact made from specific materials, due to ohmic heating by a DC electrical current, may lead to uncontrollable thermal runaway with ensuing melting. Thermal runaway stems from a steep decrease with increasing temperature of the thermal conductivity of the conducting material, and subsequent trapping of the ohmic heat in the contact or the wire. This paper evaluates the temperature for the onset of thermal runaway in a nanowire and in a bulk electrical contact. A possible implication for memory devices using ohmic-heated nanofilms or nanowires is also discussed.
Mixed Delay Constraints at Maximum Sum-Multiplexing Gain

Coding schemes are proposed for Wyner's soft-handoff model and for the sectorized hexagonal model when some of the messages are delay-sensitive and cannot profit from transmitter or receiver cooperation. For the soft-handoff network we also provide a converse. It matches the multiplexing-gain achieved by our scheme when the multiplexing gain of the delay-sensitive messages is low or moderate or when the cooperation links have high capacities. In these cases, the sum-multiplexing gain is the same as if only delay-tolerant messages (which can profit from cooperation) were sent. A similar conclusion holds for the sectorized hexagonal model, when the capacities of the cooperation links are large. (0)