Miracle-dz/UniFAR-FTUs huggingface dataset:
Progress in image-guided radiotherapy for the treatment of non-small cell lung cancer

Lung cancer is one of the most common malignant tumors. It has the highest incidence and mortality rate of all cancers worldwide. Late diagnosis of non-small cell lung cancer (NSCLC) is very common in clinical practice, and most patients miss the chance for radical surgery. Thus, radiotherapy plays an indispensable role in the treatment of NSCLC. Radiotherapy technology has evolved from the classic two-dimensional approach to three-dimensional conformal and intensity-modulated radiotherapy. However, how to ensure delivery of an accurate dose to the tumor while minimizing the irradiation of normal tissues remains a huge challenge for radiation oncologists, especially due to the positioning error between fractions and the autonomous movement of organs. In recent years, image-guided radiotherapy (IGRT) has greatly increased the accuracy of tumor irradiation while reducing the irradiation dose delivered to healthy tissues and organs. This paper presents a brief review of the definition of IGRT and the various technologies and applications of IGRT. IGRT can help ensure accurate dosing of the target area and reduce radiation damage to the surrounding normal tissue. IGRT may increase the local control rate of tumors and reduce the incidence of radio-therapeutic complications.
Mode Choice Behavior on Access Trips to Carsharing Vehicles

The introduction of a carsharing service reduces both private car ownership and total vehicle miles traveled. Due to the environmentally friendly modal shifts, policy makers are keen to increase market share of carsharing. While past research has focused on both the users of carsharing services and their general mode choice, little is known about access trips to vehicles of station-based carsharing services. However, because access to a mode is critical for its success, gaining insight into the way people experience access trips is an important step towards increased mode acceptance. In this paper we present model results based on a stated choice survey regarding access trips to carsharing stations conducted by users of a regional carsharing provider in Germany. After giving a brief overview of the conducted survey and the results of a descriptive analysis, we present the results of multiple multinomial and mixed multinomial logit models. Results of the multinomial logit models show that trip-related variables are the most important determinants for access mode choice, while only a few socio-demographic parameters are significant. We estimated mixed multinomial logit models to find out how consistent respondents answered across the choice situations. The results show that respondents were not always consistent across their choices and would, e.g. choose public transportation even if that entailed long distance travel or waiting times. Our findings are consistent with both research regarding public transportation access and the small pool of research regarding access of carsharing vehicles.
IoT Fog-based Image Matching Monitoring System for Physical Access Control through IP Camera Devices

Many people are concerned with physical security and seek support from resources provided by the latest technological advancements, such as security cameras with Internet access associated with biometric people recognition algorithms. However, an isolated camera has a low degree of effectiveness because it has a limited context of information. For instance, detection of missing persons is only effective if multiple cameras are aware of that person is missing. This work proposes a command and control center to allow the coordination of security cameras in a remote and flexible way. This flexibility is achieved because the user can remotely register the actions that each camera must perform, even with the system in operation. It also allows the sharing of a user’s information with multiple cameras. Our tests demonstrate that the system can be installed in an environment with few computational resources, as well as it reacts properly according to the configured parameters.
Early-stage Automated Accelerator Identification Tool for Embedded Systems with Limited Area

Designers are turning toward hardware specialization through the use of application-specific accelerators to provide energy-efficiency and performance. We propose an early detection methodology to identify computationally similar and synthesize-able kernels that are used to build Shared Accelerators (SAs). SAs are specialized hardware accelerators that execute very different software kernels but share the common hardware functions between them. SAs can provide increased coverage if both data flow and control flow similarities between - seemingly very different- workloads are detected. This work leverages abstract syntax trees (ASTs) generated from clang (part of LLVM) to discover similar kernels among workloads. ASTs provide a level of abstraction well suited to detect commonalities between kernels. Our methodology, ReconfAST, transforms the AST into a new clustered AST (CAST) representation that further removes unneeded nodes and uses a regular expression to match common node configurations. The approach is validated using Mach-Suite, a HLS-ified benchmark suite designed for accelerators in C. A good Shared Accelerator accelerates workloads—with similar speedup compared to dedicated accelerators—and reduces the resources required for FPGA implementations. SAs constructed with our ReconfAST methodology provide an average speedup of 1.6x. ReconfAST also reduces FPGA resources, we saw a maximum reduction of above 200% in DSP, 75% reduction for LUTs, and 40% reduction for FFs compared to smallest DAs.
A 12-mW 10-GHz FMCW PLL Based on an Integrating DAC With 28-kHz RMS-Frequency-Error for 23-MHz/μs Slope and 1.2-GHz Chirp-Bandwidth

A 10-GHz sub-sampling phase-locked loop (PLL) (SSPLL) with wideband low-noise frequency modulation for frequency-modulated continuous-wave (FMCW) radar applications is presented. It uses a low-power charge-integrating digital-to-analog converter (QDAC) to tune the voltage-controlled oscillator (VCO) in a two-point modulation architecture. A full background calibration engine corrects for the nonlinearities in the QDAC modulation path. Implemented in a 28-nm CMOS process, the SSPLL consumes 11.7 mW (of which less-than 0.5 mW from the QDAC) to generate a 23.6-MHz/ $\mu \text{s}$ sawtooth chirp-slope with 28-kHz rms-frequency-error for 1.21-GHz chirp-bandwidth.
Real-Time Semantic Web Data Stream Processing Using Storm

Semantic web technologies are increasingly used for the management of data flows. Several RDF flow processing systems have been proposed. The data at the entry of the system is big and generated continuously at a fast and variable rate. As a result, storing and processing the entire flow becomes costly and reasoning almost impossible. Consequently, the use of techniques allowing to reduce the load while preserving the semantics of the data, makes it possible to optimize the treatments even the reasoning. However, none of the SPARQL extensions include this functionality. Thus, In this paper we present a system for managing RDF data flows in real-time, the system contains two parts, the first manages the storage of RDF data and the second process the data that comes in real-time, and combines these news data with old ones to respond to requests from users, programs, and software agents. For validation, this approach makes it possible to detect events and automatically extract relations from them, in RDF format. To do this, the system analyzes Twitter messages in real-time simultaneously with the processing of RDF data stored in a triplestore.
Epigenetic Regulations in Neuropsychiatric Disorders

Precise genetic and epigenetic spatiotemporal regulation of gene expression is critical for proper brain development, function and circuitry formation in the mammalian central nervous system. Neuronal differentiation processes are tightly regulated by epigenetic mechanisms including DNA methylation, histone modifications, chromatin remodelers and non-coding RNAs. Dysregulation of any of these pathways is detrimental to normal neuronal development and functions, which can result in devastating neuropsychiatric disorders, such as depression, schizophrenia and autism spectrum disorders. In this review, we focus on the current understanding of epigenetic regulations in brain development and functions, as well as their implications in neuropsychiatric disorders.
Coarse-Grained Parallel Routing With Recursive Partitioning for FPGAs

Routing is a very time-consuming stage in the FPGA design flow, significantly hindering the productivity. This article proposes CPRS, a coarse-grained parallel routing scheme in a distributed computing environment. First, we partition entire routing region to guide the assignment of nets for parallel processing. The partitioning is a recursive fashion, and at each recursive partitioning, the region is partitioned into two subregions forming three subsets of nets. The first subset consists of potentially dependent nets and they are distributed in different subregions. The remaining two subsets consist of potentially independent nets and they are distributed in their own subregions. Second, we route the nets of first subset in serial and process the remaining two subsets in parallel. The parallel processing is a coarse-grained fashion, which is implemented by MPI parallel programming model. Finally, we explore the optimization of both partitioning and parallel processing to further improve the overall speedup of parallel routing. In addition, we adopt MPI message to synchronize the intermediate results between different cores in parallel routing for a feasible solution. Experiments use a set of commonly used benchmarks to demonstrate the effectiveness of CPRS. Notably, CPRS achieves about 18× speedup on average using 32 processor cores with minor loss of quality, compared with the VTR 7.0 serial router. There is about 1.6× improvement over the state-of-the-art parallel router.
Performance analysis of NOMA-based cooperative networks with relay selection

Non-orthogonal multiple access (NOMA) technique is an expert on channel differences exploiting. In this paper, a dual-hop NOMA-based cooperative relaying network where a best relay is selected as an active node to accomplish the communication between a source and a destination is discussed. We assume that both decode-and-forward (DF) and amplify-and-forward (AF) protocols are applied to the selected relay. The metrics that ergodic sum-rate and outage probability are investigated, and the closed-form expressions of the latter for DF and AF protocols are derived. Numerical and simulation results are conducted to verify the validity of the theoretical analysis, in which we can see that the NOMA based DF relaying is better than the NOMA based AF relaying and other existing NOMA-based cooperative communication schemes.
Handheld Localization Device for Indoor Environments

In this paper, we address the problem of human safety during the search of underground or unknown indoor environments in scenarios where it is required to share the information about the position of the personnel such as first responders or speleologists. We report on the developed localization device to estimate and transmit the user position and state through a custom communication system. The information provided by the device serves to monitor the user state, enable communication between the user and the base station located outside the environment being searched. The personnel’s position in the environment is thus provided to the mission supervisor in the case of an emergency. The device is based on low-cost off-the-shelf cameras for vision-based localization and low-bandwidth communication modules suitable for long-range communication in underground environments. Furthermore, the communication range is extendable by affordable transceivers that allow the dynamical building of independent communication networks in the operational environment. The performance of the communication system has been examined in an experimental scenario where five transceivers sufficiently cover 280 m of cave tunnels. The system can localize a human independently on any infrastructure supported by a report on the experimental deployment in an urban scenario with both indoor and outdoor areas.
A Fast Learning-Driven Signoff Power Optimization Framework

Modern high-performance System-on-Chip (SoC) design flows highly depend on signoff tools to perform timing-constrained power optimization through Engineering Change Orders (ECOs), which involve gate-sizing and $V_{th}$-assignment of standard cells. However, ECOs are highly time-consuming, and the power improvement is unknown in advance. Ever since the industrial benchmarks released by the ISPD-2012 gate-sizing contest, active research has been conducted extensively. Nonetheless, previous works were mostly based on heuristics or analytical methods whose timing models were oversimplified and lacked formal validations from commercial signoff tools. In this paper, we propose ECO-GNN, a transferable graph-learning-based framework, which harnesses graph neural networks (GNNs) to perform commercial-quality signoff power optimization through discrete $V_{th}$-assignment. Our framework generates tool-accurate optimization results instantly on unseen netlists that are not utilized in the training process. Furthermore, we implement a GNN-based explanation method to interpret the optimization results achieved by our framework. Experimental results on 14 industrial designs, including a RISC-V-based multi-core system and the renowned ISPD-2012 benchmarks, demonstrate that our framework achieves up to 14X runtime improvement with similar signoff power optimization quality compared with Synopsys PrimeTime.
Resource Allocation for OFDM-based Maritime Edge Computing Networks

Unmanned Surface Vehicles (USVs) can support various maritime applications, such as ocean environment monitoring and target tracking applications. Mobile Edge Computing (MEC) is a promising technology to improve the computing capacities and energy efficiencies of the USVs with latency sensitive computation tasks. In this paper, an OFDM-based maritime broadband MEC system model is developed, where a command surface vehicle provides edge computing service to the surrounding USVs, and the task offloading and resource allocation are formulated as an optimization problem with a goal of minimizing the USVs energy consumption, constrained by the task execution delay requirement and allowable transmit power. First, the optimal transmit power allocation scheme for each USV is obtained based on the water filling algorithm to improve the transmission rate of the OFDM-based wireless link. Second, the sub-problem of the task offloading transmission time allocation is solved by means of the CVX optimization toolbox. Simulation results show that the proposed offloading algorithm can effectively reduce the energy consumption of the maritime MEC system and adapt to a dense USVs MEC environment
An architecture for self-test of a wireless communication system using sampled IQ modulation and boundary scan

An architecture for system-level self-test of a wireless communication transceiver integrates the functional (parametric) self-test of the radio frequency subsystem, and the structural self-test of the digital subsystem. The digital subsystem is tested using extensions of the IEEE 1149.1 boundary scan standard to verify connections within circuit boards and between boards. The RF subsystem is tested using a loopback connection between the RF transmitter and receiver. An RF parametric self-test is performed using a digitally modulated signal (as opposed to a sinusoidal tone) as the test stimulus, and using samples from the receiver digitizer as test data. This loopback test scheme imposes a relatively small overhead on the RF system design.
ECG Signal Analysis for Classification of Congenital Heart Defects

Congenital heart defect (CHD) is the defect present in heart vessels at the time of birth. Electrocardiography (ECG) can prove helpful in early diagnosis of CHDs. Signal analysis techniques are used for detecting CHDs. The data of three classes i.e. normal, ASD, and VSD were gathered from hospitals of Rawalpindi. ECG signals were pre-processed by using Variational Mode Decomposition (VMD). Then six features were extracted from the pre-processed signal of each class. Features were then fed to classifiers to check maximum accuracy. Gaussian SVM achieved the highest accuracy of 99.7%. The greater accuracy and cheap system will help in proper and early diagnosis of congenital heart diseases.
Trajectory Design for Energy Savings in Redundant Robotic Cells

This work explores the possibility of exploiting kinematic redundancy as a tool to enhance the energetic performance of a robotic cell. The test case under consideration comprises a three-degree-of-freedom Selective Compliance Assembly Robot Arm (SCARA) robot and an additional linear unit that is used to move the workpiece during a pick and place operation. The trajectory design is based on a spline interpolation of a sequence of via-points: The corresponding motion of the joints is used to evaluate, through the use of an inverse dynamic model, the actuators effort and the associated power consumption by the robot and by the linear unit. Numerical results confirm that the suggested method can improve both the execution time and the overall energetic efficiency of the cell.
Layout Pattern Generation and Legalization with Generative Learning Models

VLSI layout patterns plays an important role in various research in Design for Manufacturing (DFM), such as optical proximity correction, lithography hotspot detection and so on. However, a large and diverse layout pattern library is usually not available during development stages due to the long and iterative technology life cycle, which brings potential difficulties to related research and slows down the development process. Although some previous works managed to enlarge pattern libraries with different solutions, there are still many challenges on generating complex DRC-clean two-dimensional patterns with specific styles. To address this problem, we explored the capability of generative machine learning models to learn the inherent distribution of a given set of non-trivial layouts for synthesizing diverse and realistic layout patterns with little manual guidance. For this purpose, we propose CUP, the CUpattern generation and legalization framework, which consists of two learning-based modules for pattern topology generation and design rule legalization respectively. Experiments show that CUP can generate diverse legal layout patterns which are comparable to actual design layouts in terms of resemblance in style and validity.
A Water Fraction Measurement Method Using Heuristic-Algorithm-Based Electrical Capacitance Tomography Images Post-Processing Technology

This paper presents a sectional water fraction measurement method using electrical capacitance tomography (ECT) image. To achieve a desired measurement accuracy, an optimal threshold-search method based on fast simulated annealing (FSA) algorithm is applied for image binarization; and to guarantee the imaging time consumption, fast fixed point continuation (FFPC) iterative algorithm cooperating with the compressed sensing (CS) theory was applied to the image reconstruction. In this study, the imaging time consumption and the measurement accuracy of water fraction are used to compare the proposed method and other methods. A numerical model is established with COMSOL Multiphysics to conduct the simulative validation of the proposed method firstly and physical experiments are conducted then. The experiment results show the average relative error of water fraction is lower than 15%, where the even as low as 3.68%.
Development of A Data Acquisition System for Remote Monitoring of Environmental Variables in The Apis Mellifera Bee Hive

This article is about a remote monitoring system of internal and external temperature, internal and external humidity, luminosity and wind speed in a bee hive to generate a basic information system for beekeepers. Next, the development of an intelligent hive for the monitoring of health in Apis Mellifera bees is disclosed through remote monitoring of internal and external temperature, internal and external humidity, luminosity and wind speed in the hive. The project consists of three stages: electronic instrumentation from digital and analog sensors that provide the information to the system, radio frequency communication in order to send the hive information to an IoT application for data analysis. This project is directed to the beekeeping sector of the municipality of Miraflores Boyacá (Colombia).
ABACUS: Address-partitioned Bloom filter on Address Checking for UniquenesS in IoT Blockchain

DAG-based blockchain systems have been deployed to enable trustworthy peer-to-peer transactions for IoT devices. Unique address checking, as a key part of transaction generation for privacy and security protection in DAG-based blockchain systems, incurs big latency overhead and degrades system throughput. In this paper, we propose a Bloom-filter-based approach, called ABACUS to optimize the unique address checking process. We partition the large address space into multiple small subspaces and apply one Bloom filter to perform uniqueness checking for all addresses in a subspace. Specifically, we propose a two-level address space mechanism so as to strike a balance between the checking efficiency and the memory/storage space overhead of the Bloom filter design. A bucket-based scalable Bloom filter design is proposed to address the growth of used addresses and provide the checking latency guarantee with efficient I/O access through storing all sub-Bloom-filters together in one bucket. To further reduce disk I/Os, ABACUS incorporates an in-memory write buffer and a read-only cache. We have implemented ABACUS into IOTA, one of the most widely used DAG-based blockchain systems, and conducted a series of experiments on a private IOTA system. The experimental results show that ABACUS can significantly reduce the transaction generation time by up to four orders of magnitude while achieving up to 3X boost on the system throughput, compared with the original design.
Implementation of the Advanced Traffic Management System using k-Nearest Neighbor Algorithm

With the rapid population growth in Indonesia, the development of traffic becomes congested; various ways have been done to overcome traffic jams, one of which is by using the Advanced Traffic Management System (ATMS), which is a development branch of the Intelligent Transportation System (ITS) and uses the k-Nearest Neighbor (k-NN) algorithm which is an algorithm that uses the object classification method based on learning data that is the closest distance to the object. Overcoming traffic jams is a problem that must be faced, especially at certain hours. Therefore, to overcome this, it is necessary to have traffic lights to drive in an orderly manner. To simulate, it can use one simulation software, namely SUMO (Simulation of Urban Mobility). Using simulation software to simulate traffic density at the intersection, it is hoped that it can visualize the traffic intersection state, breaking down the thickness. Using the Simulation of urban mobility software, it is expected to simulate traffic density to break down congestion at traffic intersections with data obtained from the k-NN algorithm data training. Measurements used using error rate measurements include Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as well as Time Series Prediction.