Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Integrated Solutions for High Resolution Terahertz Imaging (Invited)

This paper is an overview of the advances of the integrated sub-THz/THz imaging technology. The challenges to implement fully integrated imaging systems at this frequency range are discussed and solutions to overcome them are presented. We review the design of a 320 GHz transmission based coherent imaging transceiver fabricated in 130 nm SiGe BiCMOS technology. The optimum design of the terahertz radiator in this work is discussed and the measurement results are provided. Next, we present the implementation of a fully integrated reflection-based FMCW imaging radar which operates at 170 GHz. In order to provide a wideband chirp, we provide a design technique to maximize the tuning bandwidth of the VCO. We also review a FMCW radar at 220 GHz with 62GHz of bandwidth, where we use the Inverse Synthetic Aperture Radar technique to reconstruct high resolution 2D and 3D images. We conclude the paper by a brief overview of the other available THz imaging techniques, and suggest emerging methods for a real-time THz imaging system.
A Collaborative Process Scheme in Strategic Information Systems Planning

Strategic information system planning (SISP) has become an pivotal issue for enterprise management, especially in today's digital era. However in its implementation, the SISP process requires a variety of acting roles and the ability to share resources among the parties involved, so the success rate will also depend on the collaboration scheme being built. Examine the main aspects of the SISP collaborative scheme is objective of this paper. The results indicate that researchers are paying attention to the importance of collaboration in the SISP process, but at the same time indicating the unavailability of a holistic collaborative scheme with detailed aspects to be considered. Collaborative leadership as a basis in addition to collaborative principles such as share knowledge, share the vision, share viewpoint, active partnership approach, active communication and active participation and activities supported by the willingness of data/information and past experience, is the result of this research. Research results may be useful to other researchers and practitioners interested in collaborative schemes in the SISP process and can be a reference for them.
New Approach for Intelligent Street Lights Using Computer Vision and Wireless Sensor Networks

Smart city is expressed in many different ways by people today. In recent years, smart cities and green technology have become one of the most important and promising items on the world agenda to prepare a better future. The concept of smart city has received great interest in the context of urban development policies. The goal of a smart city is to better use public resources while improving the quality of services provided to citizens while reducing the operational costs of public administration. In this study, wireless sensor networks for smart cities are developed based on smart street light applications. The street lights are equipped with wireless sensor kits to monitor the temperature, humidity and carbon dioxide information of the environment and to monitor the changes in the regions. In addition, the mobility of the environment is analyzed with the camera attached to each sensor kit. The sensor kit was developed using Raspberry pi programming card. A sensor kit for this card has been developed. A wireless sensor network has been created by adding Xbee wireless modules on the sensor kit. The obtained data is collected in a database. The collected data is displayed to the user through an interface.
A Seven-Level Hybrid Inverter with DC-Link and Flying Capacitor Voltage Balancing

Hybrid Multi-Level Inverter(MLI) topologies are gaining attention over conventional ones due to their extended applications in the power, industrial and renewable energy sectors. MLIs are optimized which can provide compact size, reduced cost and higher efficiency compared to the conventional types. In this paper, a hybrid structure of novel three-phase seven-level inverter is proposed. This topology is a combination of cascade-connected two-level cells and H-bridge cells with flying capacitors (FCs). The operating principle and the balancing technique for the DC-link capacitors and FCs are also presented. The generation of various output voltage levels using Sinusoidal Pulse Width Modulation (SPWM) is presented. The comparative analysis is carried out to show the number of components in the proposed circuit configuration and their voltage ratings are considerably lower compared to the conventional and recently proposed topologies. The performance of the proposed topology is verified with simulation studies and also in the experimentation. The simulation and experimental results validate the effectiveness of the proposed topology and the control technique.
Photographic Image Synthesis with Highway Residual U-net

Photographic image synthesis is a new research focus in the field of deep learning, which uses the known image description information to generate the image approaching to the real scene. In this paper, a novel photographic image synthesis method based on the highway residual U-net (HRU) is proposed. The proposed highway residual blocks (HRBs) are embedded between the levels of the U-net and the proposed HRBs effectively filter the information transmitted by each level of the U-net and speed up the convergence of the network. In addition, the resize-convolution layers in HRU are used to replace the deconvolution layers to reduce the checkerboard artifacts in the synthesized images. HRU can be trained end-to-end with the image description map and the corresponding photographic image. Extensive experiments on Cityscapes dataset and GTA5 dataset demonstrate that images synthesized by the presented approach are considerably more realistic than other synthetic approaches.
EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices

With the proliferation of the smart Internet of Things (IoT) devices based on Android system, malicious Android applications targeting for IoT devices have received more and more attention due to the concern of privacy leakage and property loss. However, existing malware detection approaches based on static or dynamic analysis are not scalable to the evolvement of malware and cannot extract enough valid semantics in application programming interface (API) level, failing to detect new malware. In this paper, we propose EveDroid, a scalable and event-aware Android malware detection system, which exploits the behavioral patterns in different events to effectively detect new malware based on the insight that events can reflect apps’ possible running activities. Unlike existing approaches using API calls as features directly, we propose to use event group to describe apps’ behaviors in event level, which can capture higher level of semantics than in API level. In event group, we adopt function clusters to represent behaviors in each event so that behaviors hidden in events can still be captured as time goes on, which enables EveDroid to detect new malware in the event level. The function clusters can generalize API calls into vectors based on their API composition to capture new API calls, which makes EveDroid scalable to malware evolving. Moreover, a neural network is specifically designed to aggregate the multiple events and automatically mine the semantic relationship among them. We train the system and evaluate its ${F}1$ -measure on a dataset of 14 956 benign and 28 848 malicious Android apps released in different years. The experimental results show that EveDroid outperforms other malware detection systems.
Neural Machine Translation with Dynamic Selection Network

Neural Machine Translation (NMT) has made remarkable progress in recent years, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs. However, the attention based NMT models have two shortcomings: First, due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. Second, with the extensive application of NMT model in empirical research, its long-term weaknesses in dealing with scarce and extra vocabulary have become increasingly prominent. To address this problem, we employ our dynamic selection network which consists of context gate that dynamically controls the amount of information flowing from the source and target contexts, and dynamic vocabulary that additionally considers copying words directly from the source. Experiments are conducted on three machine translation tasks, English-to-German IWLST 2014, English-to-Vietnamese IWLST 2015 and Turkish-to-English WMT 2017. Experiments show that the proposed model outperforms the traditional NMT model with a large margin.
Multiple Organ Localization in CT Image Using Triple-Branch Fully Convolutional Networks

Organ localization is an essential prerequisite for many computed tomography (CT) image analysis tasks such as organ segmentation, lesion detection, and image registration. However, it is a challenging problem due to various causes such as the low-contrast organ boundaries, the large variations of organ appearance, the truncated organs, and the abnormalities. In this work, we propose an automatic method for multiple organ localization in CT image using a novel triple-branch fully convolutional network (FCN). First, the input CT image is extended to a three-channel image that contains both the original image and the enhanced organ structures using a density enhancement filter. Then, the extended three-channel image is fed to a 3D triple-branch FCN to parallelly predict the organs’ presence probability curves along axial, coronal and sagittal directions. Finally, the bounding boxes of the target organs are computed based on these presence probability curves. The triple-branch FCN is fully implemented in 3D manner, thus it can take full advantage of the spatial context information in CT image to perform accurate organ localization. Only several basic convolutional neural networks (ConvNet) operations (such as convolution, pooling and softmax regression) are involved in this triple-branch FCN, thus it is easy-implemented and flexible to serve as a preprocessing module in other medical image analysis tasks. By enhancing the structural information in the CT image using the density enhancement filter, the performance of the triple-branch FCN can be further improved. The proposed method is evaluated on a challenging public dataset with 201 clinical abdominal/torso CT images to locate 11 body organs. As the experimental results showed, the proposed method achieves higher localization accuracy than the current state-of-the-art methods.
A Sub-1V Analog-Assisted Inverter-Based Digital Low-Dropout Regulator with a Fast Response Time at 25mA/100ps and 99.4% Current Efficiency

To mitigate large voltage droop caused by sub-ns dynamic current transitions in system on chips (SoCs), this paper proposes a fully integrated analog-assisted inverter-based digital low dropout regulator (LDO) to obtain a fast response time with 160mV droop at 25mA/100ps featuring 99.4% current efficiency, and 16mV DC load regulation in sub-1V operating range by using a dynamic-step quantizer and a trip-point controller. The proposed quantizer is implemented with an inverter-based flash ADC to achieve high speed without consuming large power while the trip-point controller corrects the DC error of the inverter-based ADC. Besides, the assistant analog LDO is employed to provide fine-grain regulation and remove ripple from the output voltage.
Energy-Efficient Phase-Aware Load Balancing on Asymmetric Multicore Processors

Comparing to the traditional symmetric design, single-ISA asymmetric multi-core processors were shown to deliver higher performance at lower energy costs. In such systems, load balancing is a challenging problem. Traditional algorithm is based on the load contribution prediction according to the task priority and the CPU utilization, ignoring the different characteristics of phases within a single task. Such approach might lead to suboptimized load balancing decisions, especially when concerning energy efficiency. In this paper, we propose a novel phase-aware load balancing method. Based on empirical data, proposed method could distinguish different execution phases. Comparing to HMP load balancing, the evaluation results show that the energy cost can be saved by 4% using the proposed approach while the CPU frequency is dynamically changed. If the CPU frequency is fixed, the energy cost can be saved by 10%.
Discharge Voltage Time Series Classification of Lithium-ion Cells Based on Deep Neural Networks

Cells among lithium-ion battery packs often exist inconsistency due to production variability in industrial process, which the inconsistency may lead to the reduction of the available capacity and premature failure of the battery packs. To relieve the inconsistency of cells among a battery pack and screen cells with similar electrochemical characteristics for a series-cell configured battery pack, we present a new method for lithium-ion cell selection using discharge voltage time series classification (DVTSC) based on deep neural networks (DNN). In addition, we selected 61177 cells with capacities between 2.06 Ah and 2.10 Ah as experimental samples. The samples were preprocessed to obtain a labeled training set for the DNN model, which the size of the labeled training set is 3000, including 2000 training samples and 1000 test samples. Then we compared two classical baseline methods and four deep neural networks methods for time series classification. The results show that FCN has the best performance on three metrics and can effectively screen abnormal cells in the discharge voltage time series on an unlabeled test set.
Maritime situational awareness forensics tools for a Common Information Sharing Environment (CISE)

CISE stands for Common Information Sharing Environment and refers to an architecture and set of protocols, procedures and services for the exchange of data and information across Maritime Authorities of EU (European Union) Member States (MS’s). In the context of enabling the implementation and adoption of CISE by different MS’s, EU has funded a number of projects that enable the development of subsystems and adaptors intended to allow MS’s to connect and make use of CISE. In this context, the Integrated Systems Laboratory (ISL) has led the development of the corresponding Hellenic and Cypriot CISE by developing a Control, Command & Information (C2I) system that unifies all partial maritime surveillance systems into one National Situational Picture Management (NSPM) system, and adaptors that allow the interconnection of the corresponding national legacy systems to CISE and the exchange of data, information and requests between the two MS’s. Furthermore, a set of forensics tools that allow geospatial & time filtering and detection of anomalies, risk incidents, fake MMSIs, suspicious speed changes, collision paths, and gaps in AIS (Automatic Identification System), have been developed by combining motion models, AI, deep learning and fusion algorithms using data from different databases through CISE. This paper briefly discusses these developments within the EU CISE-2020, Hellenic CISE and CY-CISE projects and the benefits from the sharing of maritime data across CISE for both maritime surveillance and security. The prospect of using CISE for the creation of a considerably rich database that could be used for forensics analysis and detection of suspicious maritime traffic and maritime surveillance is discussed.
A 5GHz 245fsrms 8mW Ring Oscillator-based Digital Frequency Synthesizer

The phase noise of ring oscillator-based frequency synthesizers is limited by jitter accumulation. In this paper, we present a synthesizer architecture that uses a low noise passive multi-phase generator and digital background calibration to synthesize a 245fsrsm 5GHz output from a 54MHz crystal oscillator while consuming only 8.2 mW.
Quadrotor Design for Outdoor Air Quality Monitoring

Air quality has direct impact on the human health. Its high pollution levels can cause health problems. So, it is highly important to monitor air quality to prevent environmental pollution and improve human quality of life. In environmental applications, one of the most promising directions is measurements with unmanned aerial vehicles (UAVs). The use of UAV has great interest due to their advantages over ground vehicles like high maneuverability and easy deployment. The main scope of this study, is to present a custom UAV design capable to perform environmental monitoring missions. While in computational fluid dynamics simulations was investigated UAV’s propulsion system influence on gas measurement solution placement.
Virtual Classroom: An ADHD Assessment and Diagnosis System Based on Virtual Reality

Attention deficit hyperactivity disorder (ADHD), i.e., children’s hyperactivity, is a common neurodevelopmental disorder in childhood. ADHD is mainly characterized by having difficulty in staying focused, behavioral impulsivity and hyperactivity, and is often accompanied by conduct disorders, learning disabilities or learning difficulties. Traditional therapies generally rely on doctors and parents who can observe and assess patients’ behavior through behavioral scales; however, these therapies are time consuming and ineffective in quantifying behavior. Basing on virtual reality technology, this study integrated multiple sensor technologies, such as eye movement sensors and EEG sensors, and developed an assessment and diagnosis system for ADHD. This system constructed a virtual classroom environment and integrated some tasks, such as audio test, Continuous Performance Task (CPT) and Wisconsin Card Sorting Test (WCST), to judge the subject’s sustained attention, abstract reasoning ability and cognitive ability. Distraction elements were also added to the experiment by analyzing the attention shift of the test taker to diagnose ADHD. Physiological data, such as head movements, eyes movements, and EEG, were used to supplement test results to assess the subject’s sustained attention and attention shift.
An Improved Real-Time Approach for Video based Angular Motion Detection and Measurement

Angular Velocity(AV) estimation is necessary for controllers used for rotation motion monitoring. The velocity estimated is used for feedback and is very crucial in speed control for a variety of applications such as spacecraft tumbling detection, propeller speed control etc. Linear motion detection and linear velocity estimation has been exploited vastly. But, when it comes to angular velocity estimation of a randomly moving object with varying angular speeds, computation is very complex. Hence, computation of AV by non-contact based methods are in huge demand. The need for detection of the direction and value of rotation of a tumbling spacecraft in the Earth's orbit is a huge necessity. Accurate AV estimation has applications in spacecraft docking in space. In the proposed work we have developed an improved algorithm for real-time estimation of rotation using live video feed. The AV of the object can be measured by the camera feed of the rotating bodies as grabbed by the video camera. Angle is computed on every frame with respect to a reference and monitored for a certain number of frames to give the instantaneous AV. The number of frames processed by the camera is dynamic and is estimated prior to the angle calculation. Comparison of performance of AV estimation by the proposed algorithm with state-of-the-art Lucas Kanade(LK) object tracking method based AV estimation is also reported. The proposed approach shows significant improvement in estimating velocities whose motion is circular in nature. Additionally, this work advocates a novel approach towards an effective real-time angular velocity calculation by thresholding techniques, blob identification, and centroid detection.
A Comparison Study of Compensation Topologies for Capacitive Power Transfer

The compensation topology as the key factor affecting the performance of the capacitive power transfer (CPT) system has been extensively studied. This paper compares the characteristics of the double-sided LC, LCL and LCLC compensation topologies for the CPT system. According to Kirchhoff's voltage law (KVL), the output characteristics of the three topologies are calculated. Considering that the system frequency varies in practice, the output current, current gain and output power of three topologies at different operating frequencies are analyzed. The 1.5kW CPT systems with three different topologies are modeled and simulated respectively to verify the analysis. Meanwhile, this paper compares the characteristics of the LCLC compensation topology and its four-coil configuration. Though the four-coil configuration can be equivalent to the LCLC compensated CPT system under certain conditions, the differences are found in the aspects including the voltage gain, the current gain, and the output power at different operation conditions. A 1.5kW double-sided LCLC CPT prototype is built to verify the theoretical analysis.
Zenithal People Detection Based on Improved Faster R-CNN

With the development of computer vision, zenithal people detection has attracted much attention in our society. Recent algorithms have shown effective performance which are mostly based on machine learning and hand-crafted features. However, the detection of the surveillance with large and various pedestrian flow still has limited success due to the head irregularity and occlusion. To address this problem, in this paper, we propose an improved zenithal detection network based on Faster R-CNN. We extend the network to a more discriminative and effective one by adopting a “joint-loss” function and presenting a new combination of the anchors according to the characteristic of human head feature. Note that the new anchors proposed are more appropriate to the head detection task. Experimental results demonstrate that the proposed framework has substantially better accuracy of state-of-the-art methods.
Boundstates Computation for Double Quantum Well Structure with Pöschl-Teller Potential for MWIR Photodetector Design

Lowest two quantum states of a double-well-triple-barrier structure are numerically analyzed for Pöschl-Teller potential profile. Kane-type band nonparabolicity of first order is considered for rational replication purpose, and effective mass mismatch between well and barrier layers are included in the computation following the BenDaniel Duke boundary conditions. Transmission coefficient is plotted as a function of input energy flux, and peaks are identified for eigenstate determination in presence and absence of external electric field. Results are compared with that obtained for ideal rectangular geometry, and better quasi-peak characteristics speaks in favor of the proposed structure for photodetector application, where tailoring of subband energy can be achieved by means of structural parameter variations.