Miracle-dz/UniFAR-FTUs huggingface dataset:
A Platform Architecture for Industry 4.0 Driven Intelligence Amplification in Logistics

The aim of this doctoral consortium paper is to introduce my doctoral research proposal in the field of enterprise computing. The scientific problem that I address in my research is the limited usage of real-time data, originating from Industry 4.0 (I4.0) technologies (e.g. smart IoT devices and sensors), by Small-and Medium sized Enterprises (SMEs) in the logistics industry. I argue that the development of an industry platform for real-time data streaming and analytics would allow SMEs to benefit from such data and help them streamline their operational processes and overall performance. The main contribution of my research is a reference architecture for such a platform, geared for the needs of SMEs, and incorporating: 1) a logistics canonical data model to collect and harmonize I4.0 data, 2) an automatic schema matcher to map SME data to the logistics canonical data model, 3) autonomous data mining agents, 4) an adoption strategy based on the concept of intelligence amplification and 5) key performance indicators to measure adoption effects on operational and decisional performance.
Decision Fusion Rules in Ambient Backscatter Wireless Sensor Networks

Ambient backscatter (AmBC) communications cap-italize on ambient radio-frequency (RF) signals to enable communications among ultra-low-power devices, thus representing a promising cost-effective solution for wireless sensor networks in the Internet of Things. In this paper, we study the scenario where single-antenna AmBC sensors are employed to perform decision fusion over multiple-access fading channels. Specifically, AmBC sensors detect the presence/absence of a phenomenon of interest and transmit their decisions to a multiple-antenna fusion center reader (FCR), by reflecting part of an incident RF ambient signal. In this scenario, we derive fusion rules at the FCR by considering both the cases of instantaneous and statistical channel state information, as well as their corresponding low-complexity alternatives. Numerical simulation results are provided to compare the proposed fusion rules and highlight the relevant trends.
Resource-Aware Task Scheduling and Placement in Multi-FPGA System

With the development of high performance computing, Field Programmable Gate Arrays (FPGAs) are widely used for task acceleration. Partial reconfigurable technology provides the possibility of multi-task concurrent computation on FPGAs. However, it is still difficult to achieve high performance multi-task acceleration in multi-FPGA systems due to the resource contention in limited hardware resources and reconfiguration overhead. In this paper, we propose a two-stage task scheduling approach in multi-FPGA systems to optimize task execution efficiency. At the first scheduling stage, we select an appropriate computing unit for each task considering the subtask similarity and resource requirement similarity to reduce the possibility of reconfiguration and resource contention. At the second scheduling stage, we coordinate the subtask scheduling and placement to make full use of the hardware resources of FPGAs and improve the acceleration performance. Experiments show that our two-stage scheduling approach significantly reduces the tasks makespan and improves the utilization of resources compared with other traditional methods.
Sparse Model-Agnostic Meta-Learning Algorithm for Few-Shot Learning

Few-shot meta learning aims to obtain a prior from previous experiences, which is well used for new tasks during meta-test phase. The model-agnostic meta-learning (MAML) algorithm in [3] achieves this goal by finding a proper initial parameter at meta-training phase and this initialization could be quickly adapted to a new task by using the gradient descent at meta-test phase. However, it suffers from the problem of high computational complexity. In this paper, we propose a sparse model-agnostic meta-learning (SMAML) algorithm to alleviate the complexity problem and enhance the efficiency, where $L_{1}$ regularization is considered as an extra constraint. Experimental results on classification show that our well-placed modification indeed improves the computational efficiency in comparison to the original MAML algorithm.
Towards WCET Estimation of Graph Queries@Run.time

Recent approaches in runtime monitoring and live data analytics have started to use expressive graph queries at runtime to capture and observe properties of interest at a high level of abstraction. However, in a critical context, such applications often require timeliness guarantees, which have not been investigated yet for query-based solutions due to limitations of existing static worst-case execution time (WCET) analysis techniques. One limitation is the lack of support for dynamic memory allocation, which is required by the dynamically evolving runtime models on which the queries are evaluated. Another open challenge is to compute WCET for asynchronously communicating programs such as distributed monitors. This paper introduces our vision about how to assess such timeliness properties and how to provide tight WCET estimates for query execution at runtime over a dynamic model. Furthermore, we present an initial solution that combines state-of-the-art parametric WCET estimations with model statistics and search plans of queries.
An Intelligent Congestion Control Method in Software Defined Networks

Congestion control tends to be a quite significant problem with the increment of internet traffic. Conventional networks treat packets drop as an indication of congestion and employ the end devices to mitigate congestion. This may impose restrictions on network performance with the absence of comprehensive view of network. To solve these problems, this paper proposes an intelligent congestion control algorithm based on the global characteristics of software defined network architecture. The controller monitors the global operating state of network and evaluates instant congestion quality of backup paths in network by fuzzy logic. Combing with reinforcement learning, the controller evaluates the rerouting decisions under current network circumstances and chooses the optimal forwarding path which meets the traffic load requirement to forward flows. Experiments shows that the mechanism can significantly enhance network performance especially when network is under heavy load.
DeepTelos Demonstration

DeepTelos is defined as a set of rules and constraints that enable multi-level modeling for the Telos metamodeling language. In its ConceptBase implementation, rules and constraints are realized by Datalog clauses. We start with demonstrating first the core functions of Telos, use of simple rules and constraints, then the meta-level rules and constraints defining DeepTelos. A couple of examples show how the DeepTelos rules and constraints are compiled to simple rules and constraints and then realize the desired multi-level modeling environment. The main example is taken from the Galileo satellite domain.
User Interface Code Retrieval: A Novel Visual-Representation-Aware Approach

Code reuse has been perceived to be an effective tool for user interface development that is known to be complex and messy. A fundamental problem in user interface code reuse is how to effectively retrieve working code from existing code repositories, which renders functional interfaces similar to a programmer’s input sketch. Existing generic code retrieval techniques are normally inadequate for user interface code due to its associated visual information. In this paper, we put forward a novel approach that retrieves matching candidate code of an interface sketch based on their visual-representation trees. To better capture sub-region similarities, we propose to decompose a visual-representation tree into an ordered list of paths (or sequences). We then devise a novel algorithm to measure the similarity between two sequences by leveraging various visual information and another algorithm to calculate the similarity of two ordered lists by considering the relative positions of different widgets. To evaluate our solution, we carefully design an experiment to generate the similarity scores of 6,750 user interface pairs based on real users’ judgement. Based on this ground truth, we show that our method is able to generate accurate similarity scores, outperforming several state-of-the-art competitors.
Non-Radioactive DNA Hybridization Experiments for the Undergraduate Laboratory: The Southern Blot Analysis

Department of Biological Sciences B-316 Lilly Hall Purdue University West Lafayette, Indiana 47907 (317) 494-8083, (317) 494-0876 (FAX) Susan Karcher received her B.A. in Biochemistry from the University of Wisconsin, Madison, her M.S. and Ph.D. in Chemistry from the University of California at San Diego. Dr. Karcher is an instructor in the Department of Biological Sciences at Purdue University, where she teaches an advanced molecular genetics laboratory. She has also taught introductory molecular biology. Her research interests include plant molecular biology, interactions between pathogenic microbes and their host plants, and
A 76-81 GHz CMOS PA with 16-dBm PSAT and 30-dB Amplitude Control for MIMO Automotive Radars

This paper presents a 77 GHz power amplifier (PA) for multiple-input-multiple-output (MIMO) automotive radar with high output power, reconfigurable gain, and 0/180º phase shifting capability, by using TSMC 65 nm CMOS general purpose (GP) technology. The PA has a variable gain amplifier (VGA) design merged with a phase shifter, a power splitter, two-way double-neutralized PA cell, and a balun-based power combiner. Measurement results show that the saturation output power (PSAT) is 16.1 dBm with maximum PAE of 12.8% at 79 GHz, a peak power gain of -5-25 dB is tunable at 77 GHz, and 0/180º phase shifter is achieved over 76-81 GHz. The power consumption of the PA is 356 mW.
Exploring Benefits of NVMe SSDs for BigData Processing in Enterprise Data Centers

Big data processing environments such as Apache Spark are prominently deployed for applications with large scale workloads. New storage technologies such as Non-Volatile Memory Express Solid State Drives (NVMe SSDs) provide higher throughput comparing to the traditional Hard Disk Drives (HDDs). Therefore, NVMe SSDs are rapidly substituting HDDs in modern data centers. In this paper, we explore whether it is critically necessary to use NVMe SSD for a large workload running on the Spark big data framework. Specifically, we investigate what are the influential factors of application design and Spark data processing framework to exploit the benefits of NVMe SSDs. Our real experimental results reveal that some applications even with large workloads cannot fully utilize NVMe SSDs to obtain high I/O throughput. Interestingly, we find out that characteristics of Spark data processing framework such as shuffling (i.e., the volume of transition data generated by an application), and parallelism (i.e., the number of concurrently running tasks) has very crucial impacts on the performance of big data applications running on NVMe SSDs.
A Network-flow Based Optimal Scheduling for Data Downloading from Satellites to ES in LEO Satellite Networks

In recent years, satellite networks technology plays a more and more important role in military and commercial fields. To meet the different types of applications, a majority of countries and communication companies around the world have launched a large number of LEO satellites to space. For those satellites, the major task is to download their data which is collected from the space to earth stations (ESs). In order to maximize the throughput of data downloading from satellite to ESs, previous research work is extensively focused on how to efficiently allocate download time to satellites according to the contact time between ESs and satellites. However, seldom of them have considered the use of Inter-satellite links (ISLs) to offload data from heavily loaded Satellites to the light ones. In this paper, our goal is to optimize data downloading from satellites to one ES by using our ISLs routing method, which is based on the network flow model to offload data from the satellites that has surplus data to the satellites that has surplus contact time with ES. In our scheme, we allocate the download time to each satellite that has contact time with ES during the orbital period. Then, the satellite-to-satellite graph is constructed basis of the interaction time among satellites. After that, two virtual nodes that are used to avoid transmission conflict and congestion are introduced to each satellite. Finally, a network flow model is formulated based on ISLs among satellites and preflow-push algorithm is applied to find the global optimal solution. Extensive simulations show that our scheme can greatly improve efficiency of data downloading from satellites to ES
A 500mV, 118nW, ∑Δ-Modulator ADC for Audio Detection in 28 nm FD-SOI

In this work a 500 mV supply voltage, low power, inverter-based $\Sigma\Delta$ -modulator in 28 nm FD-SOI is presented. The discrete-time $\Sigma\Delta$ -modulator is optimized for low power and is to be used in audio detection applications. The switched-capacitor integrators are designed using inverter-based amplifiers with a DC cancelling scheme to suppress DC offset. An optimization of the inverter-based amplifiers is shown taking advantage of the simple structure. The 2nd -order $\Sigma\Delta$ -modulator achieves a $\mathrm{SNDR}_{\mathrm{peak}}=46\mathrm{dB}$ with an OSR of 32 and power consumption of 118nW.
Energy Management Methodology for Fusion Grid

This paper proposes an energy management methodology for a Fusion Grid concept. The Fusion Grid concept integrates a Long-Term Evolution (LTE) base station and a power system based on photo-voltaic (PV) power generation and a battery energy storage (BES). The Fusion Grid concept can be scaled up to an off-grid system. The system studied in this paper provides connectivity in the form of an LTE mobile network and electricity access in the form of a charging station for mobile devices. The energy management and control methods are applied as part of the system. The methods are aimed at the effective use of the installed BES capacity and reducing the total life cycle costs (LCC) of the system. Furthermore, the implemented methods will prevent overdimensioning of the system, thereby having an effect on the system capital expenditure costs (CAPEX). The developed methods are evaluated in a simulation environment with quantification of their effects on the life span of the BES.
Analytical Solution for Inverse Kinematics Using Dual Quaternions

This paper presents a new solution for inverse kinematics (IK) by using dual quaternions (DQ) as operators and combining them with the Paden–Kahan subproblem to determine the analytical solution. Kinematics under the structure of screw theory makes the process mathematically efficient and gives geometric meaning. Both derived results and simulation outgrowths, when compared with the generally used numerical solution and matrix-based analytical solution, show that our method is faster and does not suffer from the numerical instability caused by being near to a singular configuration. The method has been proven to have the advantages of reducing the computational load and promoting precision, and it can provide multiple choices for IK.
Range/Doppler Sidelobe Suppression in Moving Target Detection Based on Time-Frequency Binomial Design

An ambiguity function is widely used in radar and communication. It is highly desirable that an ambiguity function should have low range- and Doppler- sidelobe as the good resolution in both range and Doppler domain. In this paper, a method which combines time-domain binomial design and frequency-domain binomial design via a Point-wise Minimum Processor (PMP) is proposed. The time-domain binomial design is designed by [7] to clear the range sidelobe in a very large Doppler area and the frequency-domain binomial design is proposed by this paper to avert the Doppler sidelobe in a very large range area. It is shown that the new method produces a "thumbtack-like" ambiguity function with a good range/doppler sidelobe suppression and results in good distinction of two moving targets.
Heart Disease Detection Architecture for Lead I Off-the-Person ECG Monitoring Devices

With the rise of smart-watches and other wearables, off-the-person electrocardiography is gaining momentum as high-quality Lead 1 ECG signals can now be acquired from a persons hands or arms. Although several heart disease detection algorithms have been described in recent years, they are not designed considering Lead 1-only setups. This work bridges this gap with an architecture for a robust Lead 1 real-time heart disease detection system and an FPGA-based implementation. The proposed system is based on a signal processing pipeline composed of: ECG signal denoising; heartbeat detection and segmentation; extraction of dynamic morphological features; and heartbeat classification (standard and different abnormal heartbeats). Resorting to the only database from MITs Physiobank with Lead 1 annotated recordings, InCarTDb, the proposed pipeline resulted in a 4-class model with a classification accuracy of up to 96.5%. Moreover, when implemented in a Zynq-7 ZC702 Evaluation Board, the proposed architecture requires less than 30% of the FPGA resources and a total power consumption of 192 mW at a clock frequency of 35 MHz.
Voltage References for the Ultra-Wide Temperature Range from 4.2K to 300K in 40-nm CMOS

This paper presents a family of voltage references in standard 40-nm CMOS that exploits the temperature dependence of dynamic-threshold MOS, NMOS and PMOS transistors in weak inversion to enable operation over the ultra-wide temperature range from 4.2 K to 300 K. The proposed references achieve a temperature drift below 436 ppm/K over a statistically significant number of samples after a single-point trim and a supply regulation better than 1.7 %/V from a a supply as low as 0.99 V. These results demonstrate, for the first time, the generation of PVT-independent voltages over an ultra-wide temperature range using sub-1-V nanometer CMOS circuits, thus enabling the use of the proposed references in harsh environments, such as in space and quantum-computing applications.
Automatic System for Sheepskin Quality Control with Convolutional Neural Network

This paper tackles the non-conventional problem of industrial inspection for sheepskin quality checking, which is a sub-field of the leather garment industry. Image processing methods are used in the production lines of several industrial fields for quality checking and production. However, the number of automatic systems is still very limited due to the complexity of the problem, which requires to be addressed. This paper proposes a new neural network-based algorithm to perform the automatic detection of defective parts on sheepskins. The presented method is tested on a newly created database of sheepskins.
Feasibility Evaluation: Virtual Laboratory Application Based on Virtual Reality for Lathe Engine Training Simulation

Work accident data at the Production System Laboratory (PSL) in Industrial Engineering Department, Diponegoro University shows that human error in the manufacturing process practicum most occurs when operating a lathe, where there are 11 types of errors with a total number of 42 times. Even though the machining training had been done before, but many obstacles faced by students resulted in the training that was considered to be less effective and efficient, causing students to be unprepared when carrying out the manufacturing process practicum. To facilitate learning that students must master before entering the manufacturing process practicum, namely lathe work and bench work, a virtual laboratory application was developed which was then integrated with the Oculus Go device to run applications in virtual reality. The design of this application is carried out using the user centered design (UCD) method in accordance with the measurement aspects of assessment based on their needs, namely visualization and information aspects, animation aspects, reliable aspects, and usability aspects. The results of the assessment are based on the testing of the virtual laboratory application by expert respondents and media user respondents indicating that the application designed has fulfilled the functional and non-functional needs of the user, so there is no application revision.
Custom-Designed Electrically Small Huygens Dipole Antennas Achieve Efficient, Directive Emissions Into Air When Mounted on a High Permittivity Block

System-on-Chip (SoC) applications include embedded systems and mobile computing platforms. They will play major roles in fifth generation (5G) wireless systems, notably with the many associated IoT (internet-of-things) devices. Their wireless functions are enabled, for example, by on-chip antennas (OCAs), i.e., the system elements that connect them to devices in their external environments. These antennas generally reside on a high permittivity dielectric, such as silicon, which unfortunately causes most of their emitted power to be directed into the dielectric rather than into free space. This feature is quite detrimental, i.e., it leads to a severe degradation of an OCA’s radiation efficiency as a transmitter of information to an external receiver and similarly to its poor performance as a receiver since little of its pattern resides in free space, severely limiting its ability to capture power from an external transmitter. Similar issues exist for sensors and communication devices residing on a human body. While many integrated antenna styles have been developed in attempts to deal with these issues, their complexities and remaining inefficiencies remain a bottleneck to their widespread adoption. It is demonstrated analytically and numerically that Huygens radiating systems provide a unique solution to these high permittivity substrate problems. Custom-designed electrically small Huygens dipole antennas that lie on the interface between air and a high permittivity block are reported that efficiently emit the majority of their radiated power into the air region rather than into the dielectric.
Model-Driven Design of City Spaces via Bidirectional Transformations

Technological advances enable new kinds of smart environments exhibiting complex behaviors; smart cities are a notable example. Smart functionalities heavily depend on space and need to be aware of entities typically found in the spatial domain, e.g. roads, intersections or buildings in a smart city. We advocate a model-based development, where the model of physical space, coming from the architecture and civil engineering disciplines, is transformed into an analyzable model upon which smart functionalities can be embedded. Such models can then be formally analyzed to assess a composite system design. We focus on how a model of physical space specified in the CityGML standard language can be transformed into a model amenable to analysis and how the two models can be automatically kept in sync after possible changes. This approach is essential to guarantee safe model-driven development of composite systems inhabiting physical spaces. We showcase transformations of real CityGML models in the context of scenarios concerning both design time and runtime analysis of space-dependent systems.