Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight PET Calibration Source

PET scanners are commonly calibrated with cylindrical sources, about 20 cm in diameter, in which positron-emitting radioactivity is mixed. The sources are heavy and may require massive shielding for storage. In this work we ask if scanner setup can instead be based on hollow, lightweight sources based on small beta sources and positron flight across the air, reducing the shielding requirements, and providing a nearly scatter-free source with negligible photon attenuation. A set of two positron-emitting (beta) sources was created by depositing a 0.7-mm layer of polyurethane resin, containing a total of 42.6 MBq 68Ge on two copper plates. We also created a second set of sources by depositing a 6-mm spot of 68Ge DTPA on each of two steel plates. These were positioned inside a hollow polyethylene cylinder with 21.3 cm outer diameter, 32 cm length and 7 mm wall thickness. The sources were at the cylinder’s two flat ends. Depending on the positrons’ direction after beta decay, the positrons either stopped in the beta source or flew across the air until they encountered the cylinder’s walls, causing the emission of annihilation radiation. The cylinder was positioned in an SiPM-based PET/CT scanner. PET scans and a time of flight (TOF) calibration were performed. Fused PET/CT images showed that positrons stopped both in the cylinder’s inner wall (33% and 18% of all positrons emitted by the first source and sets of sources, respectively) and in the air inside the cylinder (5% and 2%). Standard software was used to measure 211 ps TOF resolution, comparable to the value derived from traditional extended sources. Because the positron sources are small, they can be easily shielded, eliminating the need for more than 100 kg of shielding. A lightweight, hollow, scatter and attenuation-free source appears to be a practical alternative to the current state of the art.
Image Splice Detection Through Noise Pattern Analysis

Digital Images have become one of the most basic communication methods which makes it an essential part of human day today life. Most of the people hold mobile devices with them that are equipped with image capturing facilities. With the rise of the digital image forgery, the reliability and the confidentiality of the images were questioned. Digital image forensics was considered as an important field of research due to this reliability issue. Many of the images that are circulating through the web are being either tampered or forged. Image Splicing is one of the popular tampering techniques in used. This research has been carried out to detect the above identified technique with focusing on noise pattern variations that are visible in tampered images. The images are being segmented into non-overlapping blocks and then noise variances are estimated after a series of processes including wavelet transformations on luminance component of the image. Experimental results based on White Gaussian noise have proved the proposed detection method.
Useful of High Power Microwave Transmitters in Combined Radio Engineering Complexes and the Analysis of Electromagnetic Compatibility of the System

The article shows the possibility of using a single transmitter in a mobile combined radio engineering microwave system on a powerful traveling wave tube. The microwave transmission path is common to the two components of the radio engineering microwave system – troposcatter and radio relay. Reduction of the intrinsic noise of a powerful traveling-wave tube of the modernized design made it possible to obtain a higher linearity over a wide frequency range 4.4 … 6.2 GHz with an output power of 2 … 3 kW compared to a solid-state microwave amplifier of a cascade assembly. Calculations have shown the possibility of ensuring the electromagnetic compatibility of radio-emitting means that are part of such a microwave radio engineering system.
LDPC Decoder Power Consumption Optimization

The paper presents the results of developing a decoder architecture of low-density parity check code of the DVB-S2 standard for field programmable gate array. It supports several code rates and codeword lengths described in the appendix of the standard. The proposed decoder architecture has a partially parallel structure, which allows to achieve high decoding performance and efficient use of chip area. The presence of double-diagonal submatrices in the structure of the parity check matrices was also confirmed, and a method for eliminating them was implemented. In the work, an analysis of parameters affecting the static and dynamic power consumption in the field programmable gate array was made. Based on it, algorithmic and architectural methods for reducing consumption are proposed. The paper describes the developed architectures and methods to reduce chip power consumption.
Multi-modal Neural Network for Traffic Event Detection

Cities are composed of complex systems with Cyber, Physical, and Social (CPS) components. The advances in the Internet of Things (IoTs) and social networking services help people understand the dynamics of cities. Traffic event detection is an important while complex task in transportation modeling and management of smart cities. In this paper, we address the task of detecting traffic events using two types of data, i.e. physical sensor observations and social media text. Unlike most existing studies focused on either analysing sensor observations or social media data, we identify traffic events with both types of data that may complement each other. We propose a Multi-modal Neural Network (MMN) to process sensor observations and social media texts simultaneously and detect traffic events. We evaluate our model with a real-world CPS dataset consisting of sensor observations, event reports, and tweets collected from Twitter about San Francisco over a period of 4 months. The evaluation shows promising results and provides insights into the analysis of multi-modal data for detecting traffic events.
ADDP: Anomaly Detection for DTU Based on Power Consumption Side-Channel

With the rapid development of information technology and the Internet of Things, the common information technologies are being applied to the power grid. It brings about the tremendous improvement in productivity, yet breaks down the safety barrier, which makes the power grid a popular target for attacks. The Distribution Terminal Unit (DTU) is a critical part of the power grid because of the acts as access points in the substation and controls the grid equipment. However, DTUs are vulnerable to different types of attacks, which can cause significant property loss or even casualties To monitor the operation of DTU, we proposed a real-time monitoring system by analyzing the side-channel information of power consumption. To validate this idea, we design 3 types of attacks and collect the power information separately, then we choose representational power features and machine learning algorithm for detecting those attacks. We validate that it is feasible to detect attacks and achieves a detection accuracy above 99%.
Design and implementation of Phase Locked Loop on 180nm Technology node

This paper signifies the transient analysis of the PLL and is implemented in cadence tool using 180nm technology node. Frequency is expected to be in GHz range for present communication systems to increase the speed and therefore PLL is designed to produce the frequency in GHz range. The designed PLL consists of Phase frequency detector/Charge pump (PFD/CP), second order Low pass filter (LPF) and Schmitt trigger based current starved voltage controlled oscillator (CSVCO). PLL is designed to achieve a stable frequency output. The designed PLL produces 1.084 GHz with 2.382mW of average power consumption.
Offloading in Cloud and Fog Hybrid Infrastructure Using iFogSim

The information and communication technology has witnessed unprecedented changes with the introduction of IoT and the implementation of IoT applications is mainly dependant on cloud services like compute, storage, networking, etc. Fog computing has been introduced as a complement to the cloud infrastructure because in near future demands of the IoT devices will exceed the capabilities of the cloud. Our work focuses on the efficient utilization of the Cloud-Fog resources by distributing the application modules among Fog devices and cloud data centers. Placing the application modules on Fog devices improves performance parameters like response time, latency, energy consumption, etc. We have proposed two load balancing algorithms whose performance has been evaluated on the iFogSim simulator and their performance has been compared with the cloud-only approach. Our approach is generic which can be used in the vast majority of IoT applications.
Validation of the Industry-Standard ASM-GaN Model for Gate-Length Scaling

This paper validates the industry standard ASMGaN compact model in terms of capturing the effect of gatelength scaling. Pulsed IV measurements were performed to measure the variation in the IV characteristics of the GaN HEMT structure under test with gate length, in isolation from the effect of trapping. The model has been validated by comparing the simulation results of the model with the pulsed IV measurement results of three AlGaN/GaN Schottky HEMTs which differed only in terms of their gate lengths.
An Empirical Comparison on Malicious Activity Detection Using Different Neural Network-Based Models

The internet is growing at a rapid pace offering multiple web-based applications catering to the changing needs and demands of customers. Nevertheless, extensive use of internet services has potentially exposed the threats of data security and reliability. With technological advancements, cyber threats have also become more sophisticated with the blend of distinctive forms of attacks to cause potential damage. The increase in the number and variety of cyber attacks is inevitable; hence it is imperative to improve the efficiency of the cyber security systems. This research aims to compare different neural network models to distinguish malicious acts from non-malicious ones. The examined models are trained, validated, and tested using two datasets(cyber-physical subsystem dataset and KDD dataset). The performance of the studied models is measured using the confusion matrix. For the cyber-physical subsystem dataset, binary classification and multi-class classification are used for evaluating the models. In the KDD dataset, binary classification is the only classification approach because the dataset contains two classes, regular (normal actions) and harmful (malicious actions). In general, the results in binary classification are more encouraging than in multi-class classification. Among all the models, the PNN model achieves the best performance, while the GRNN model is the fastest one. Although PNN’s runtime is slightly higher than the GRNN model, we can claim that the PNN is the best model for our data because a trade-off between the performance and run time can be obtained.
Recognizing Malaysia Traffic Signs with Pre-Trained Deep Convolutional Neural Networks

An essential component in the race towards the self-driving car is automatic traffic sign recognition. The capability to automatically recognize road signs allow self-driving cars to make prompt decisions such as adhering to speed limits, stopping at traffic junctions and so forth. Traditionally, feature-based computer vision techniques were employed to recognize traffic signs. However, recent advancements in deep learning techniques have shown to outperform traditional color and shape based detection methods. Deep convolutional neural network (DCNN) is a class of deep learning method that is most commonly applied to vision-related tasks such as traffic sign recognition. For DCNN to work well, it is imperative that the algorithm is given a vast amount of training data. However, due to the scarcity of a curated dataset of the Malaysian traffic signs, training DCNN to perform well can be very challenging. In this demonstrate that DCNN can be trained with little training data with excellent accuracy by using transfer learning. We retrain various pre-trained DCNN from other image recognition tasks by fine-tuning only the top layers on our dataset. Experiment results confirm that by using as little as 100 image samples for 5 different classes, we are able to classify hitherto traffic signs with above 90% accuracy for most pre-trained models and 98.33% for the DenseNet169 pre-trained model.
Multiport Converters to Integrate Multiple Sources and Loads - A Review of Topologies

With the increase in penetration of variety of renewable energy resources in the grid, energy storage systems are needed to balance power flow and improve the reliability of the system. A compact multiport converter supporting bidirectional power flow is a suitable candidate to interface multiple sources and loads. This paper reviews various multiport converter topologies in the literature. The multiport converters can be classified as isolated, partially isolated and non isolated converters. The converter topologies in each category are identified and reviewed. Their benefits and drawbacks are highlighted.
In-Band RCS Reduction of a U-Slot Microstrip Patch Antenna

A systematic method for reducing the in-band radar cross-section (RCS) of a U-slot microstrip patch antenna is developed. The U-slot microstrip patch antenna is designed based on an empirical design technique and then analyzed using the theory of characteristic modes (TCM). The radiation modes and scattering modes of the U-slot patch antenna are distinguished by examining the CM indicators, including modal significances, modal currents, and modal fields. According to the designated frequency band and angular range for RCS reduction, a specific scattering mode is selected and loaded with lumped elements. Values of the lumped elements can be calculated theoretically in this method and a maximum RCS reduction of 20 dB is obtained.
Complementing Travel Itinerary Recommendation Using Location-Based Social Networks

We proposed in a previous work a recommendation system that, according to the requirements specified by its users, was capable of generating a few travel itineraries of distinct features (e.g. the most appealing itinerary, the shortest itinerary or the itinerary with the highest performance/price ratio) for user selection. We anticipated to complement the recommended travel itineraries by taking advantage of recent advances in location-acquisition and wireless communication technologies. With such technologies, people can share geo-spatial locations and location-based activities in the physical world via location-based social networks (LBSN), which makes it possible to help deduce a prospective traveler's dream destination using their check-ins published and shared on those websites. These user check-ins are informative in that a user's historical behaviors is a powerful indicator of his/her preferences. Therefore, we looked into and utilized user check-ins and social connections when building up a trip planner (such as our recommender) so as to better serve the travelers who may merely wander a travel website and seek the best vacation packages, flights, or hotel for their next trip. Our experiment verified the feasibility and efficiency of using user check-ins and social connections accessible from LBSNs in terms of discovering users' potential travel destination.
Research on Terminal Security Technology of Ubiquitous Power Internet of Things Based on PUF and SM3

Many constrained devices of Internet of Things (IoT) are operating under low power, and with limited computational and network resources. The devices cannot use standard security protocols to protect end-to-end security because they become the weakness of IoT. Narrow Band Internet of Things (NB-IoT) is of broad application prospects in production management, life-cycle asset management and smart power utilization of smart grid. Its characteristics and security demands of application domain present a challenge for the security of electric power business. In order to improve the high security of power network data transmission, a security encryption method for ubiquitous power Internet of things terminal based on physical unclonable function (PUF) and state secret algorithm SM3 is proposed in this paper. A self-controllable NB-IoT application layer security architecture is designed by introducing the domestic cryptographic algorithm SM3, extending the existing key derivation structure of LTE, and combining the physical unclonable function to ensure the generation of encryption keys between NB-IoT terminals and power grid business platforms. The developed IoT security transmission module is applied in the intelligent inspection robot of substation to realize transparent and encrypted transmission of video and thermal sensing data. The scheme of this paper realizes secure data transmission and bidirectional identity authentication between IoT devices and terminals. Experimental results validate its features, including lightweight, low communication costs and flexible key update.
Video Broadcast Services over 5G networks

The emerging technologies in the field of telecommunications have evolved into the paradigm of 5G. 5G technology is aimed to become sufficiently flexible to handle a wide variety of network services from various domains. One of the aspects envisaged by 5G is the virtualization of small cells, which allows enhanced mobile edge computing capabilities, thus enabling network service deployment and management near the end user. This paper presents a cloud-enabled small cell architecture and its corresponding use case for multimedia delivery over 5G networks developed within the 5G- ESSENCE project. Furthermore, it examines the inclusion at the edge of a proactive telemetry framework, as well as of an edge enabled eMBMS service to support 360 multimedia broadcasting in 5G networks.
Towards Secure Integration of Wireless Sensor Networks and Cloud Computing

In this paper, the system presents an integrated wireless sensor network (WSN) to monitor the information regarding the environment using cloud technology. The purpose is to guarantee a fast and a sage platform for information coming from the sensor nodes. The WSN will perform this task by collecting data from the environment and compressing and transmitting it back to the cloud gateway which will decompress the data and store it in a large cloud server. Not only the tasks that could be performed the WSN will presented here, but also the challenges facing it regarding security issues and cloud computing services. Therefore, a hierarchical structure of symmetric and asymmetric cryptosystems has been implemented regarding the context of data aggregation in WSN and a protocol that can handle the limited resources of the nodes and the real time needed for them to be applied to the cloud has been demonstrated. By the end of the paper it will be quite clear how the security protocols presented here can keep the whole energy of the network by saving the energy of the nodes when providing secure and reliable communication between the WSN and the cloud platform.
A Dual band Metamaterial Antenna for 5G and Higher Satellite band Applications

A highly compact (volume of only 8 × 8 × 1.6 = 102mm3) metamaterial antenna operating at 26 GHz (5G) and 33.2 GHz (higher satellite band) is presented. The structure of antenna is embedded with a symmetrical rectangular radiator and a metamaterial Rectangular Split Ring Resonator (RSRR). The symmetrical rectangular radiator controls the band at 26 GHz and metamaterial RSRR controls the band at 33.2 GHz. The antenna has S11<-10dB bandwidth of 1600 MHz, centered at 26 GHz and 600 MHz centered at 33.2 GHz. Highly compact size, good gain and radiation efficiency are obtained at the targeted 5G and higher satellite band frequencies.
Big Data Real-Time Clickstream Data Ingestion Paradigm for E-Commerce Analytics

E-Commerce websites track visitors browsing records to uncover their intent and preferences for effective online marketing. User clicks or clickstream is a series of page requests which generates a URL on each click event. Clickstream data is an information trail a user leaves behind while visiting websites. Click URLs can be graphically represented for clickstream reporting. In this work, we study Big Data real-Time clickstream data ingestion model in e-commerce Domain, which builds on top of standard Big Data tools like Kafka, Flume, Spark and Cassandra. In particular, we focus on high velocity, fault-tolerant streaming data acquisition pipelines in a distributed setup rather than mining and searching patterns in it.
Design and Evaluation of a Novel and Ultra-Compact Fully-TGV-based Self-Shielding Bandpass Filter for 5G Applications

Three-dimensional (3D) integration and interposer technology provide a promising solution for the continued minimization of modern electronic system. In this paper, through-glass-via (TGV) technique is used to form an ultra-compact bandpass filter (BPF). The proposed BPF is fully composed of well-designed TGV -based 3D array capacitors and 3D spiral inductors, and corresponding redistribution layers (RDL). A TGV-based shielding ring is utilized, which enhances the anti-interference property of the BPF, and also improves its thermal dissipation capability. The central frequency (f0) of the BPF is near 5 GHz, and the insertion loss and return loss are 2.25 dB and 15.8 dB, respectively. Moreover, the device footprint is only 0.91 × 0.58 mm2even including the shielding ring. A feasible fabrication flow is also proposed for the BPF, showing the optimization on process complexity and fabrication cost. The BPF is promising for the minimization of future 5G applications and system integration.
IoT Node-Node Secure Communication Using RIPEMD-128 and DES

Internet of things (IoT) is believed to be the greatest thing that came along with the birth of the internet 4.0. IoT security is a growing concern that have received a lot of research interest in recent times. IoT security covers these four critical areas: Device or hardware, Communications, Cloud, and Life cycle management. At each level of these security protocols are breaches that continue to threaten the continuous adoption and use of this pervasive technology that homes, organizations and Governments use towards collecting, analysing and decision making in most cases. The concept of Internet of Things-IoT describes the environment where devices connect with each other to communicate, collect, store sensor data to the cloud. The sensor data is then processed and analyzed towards describing valuable information. IoT solutions support domestic, industrial, and Governmental installations for the monitoring of environmental data, health monitoring, control and management of household appliances, weather recording and forecasting, and the like. Loosely speaking, Internet of Things - IoT concerns itself with the interconnectedness of embedded devices and the internet for the purpose of collecting, recording, analyzing and sharing of sensor data using sensors, actuators, and Radio frequency Identifiers - RFID. The Race Integrity Primitive Evaluation Message Digest (RIPEMD 128) and Data Encryption Standards (DES) were used to achieve the ultimate goal of providing improved security for end-end encryption for secured communications within IoT systems. The authors proposed a combined approach with the use of a cryptographic algorithm and a hash function to increase security for IoT node-node communications.