Art Detectors Research Articles

Quantization Design for Resistive Memories With Multiple Reads

Due to the crossbar array architecture, the sneakpath problem severely degrades the data integrity in the resistive random access memory (ReRAM). In this letter, we investigate the channel quantizer design for ReRAM arrays with multiple reads, which is a typical technique to improve the data recovery performance of data storage systems. Starting with a quantized channel model of ReRAM with multiple reads, we first derive a general approach for designing the channel quantizer, for both single-bit and multiple-bit quantizations. We then focus on the single-bit quantization, which is highly suitable for practical applications of ReRAM. In particular, we propose a semi-analytical approach to design the multiple-read single-bit quantizer with less complexity. We also derive the theoretical bit-error probability of the optimal single-bit detector/quantization as the benchmark. Results indicate that the multiple-read operation is effective in improving the error rate performance of ReRAM. Moreover, our proposed multiple-read detector outperforms the prior art detector and achieves the performance of the optimal detector.

Vehicle Detection on Occupancy Grid Maps: Comparison of Five Detectors Regarding Real-Time Performance.

Occupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies between the fused sensors through the use of occupancy probabilities representing uncertainty. In this article, we propose to evaluate the applicability of real-time vehicle detection on occupancy grid maps. State of the art detectors in sensor-specific domains such as YOLOv2/YOLOv3 for images or PIXOR for LiDAR point clouds are modified to use occupancy grid maps as input and produce oriented bounding boxes enclosing vehicles as output. The five proposed detectors are trained on the Waymo Open automotive dataset and compared regarding the quality of their detections measured in terms of Average Precision (AP) and their real-time capabilities measured in Frames Per Second (FPS). Of the five detectors presented, one inspired from the PIXOR backbone reaches the highest AP0.7 of 0.82 and runs at 20 FPS. Comparatively, two other proposed detectors inspired from YOLOv2 achieve an almost as good, with a AP0.7 of 0.79 while running at 91 FPS. These results validate the feasibility of real-time vehicle detection on occupancy grids.

HSB-SPAM: An Efficient Image Filtering Detection Technique

Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.

Robust people indoor localization with omnidirectional cameras using a Grid of Spatial-Aware Classifiers

This paper describes a system for people indoor localization using omnidirectional cameras and machine learning that significantly reduces the database annotation requirements for the training stage. The most prominent works for people detection are based on machine learning techniques that requires large databases with bounding box annotations (that enclose the people). In this work, a novel multiple classifier system, called Grid of Spatial-Aware Classifiers (GSAC), is proposed, which only requires point-based annotations, allowing to create datasets much faster (and therefore speeding up the system deployment). On the other hand, omnidirectional images have a wider field of view than traditional ones, allowing to monitor a wider area, and thus reducing deployment costs. But in return, they present severe geometric distortions that degrade the performance of state of the art detectors, due to the strong changes in the person appearance caused by the position-dependent distortion. The proposed GSAC satisfactorily addresses this problem by distributing the detection task among all spatial-aware classifiers, so that each classifier has only to deal with a subrange of appearances and distortions. Lastly, a thorough evaluation has been performed on two databases of omnidirectional images: a well-known one and one specifically designed to assess the people detection performance.

Hypersensitive Tunable Josephson Escape Sensor for Gigahertz Astronomy

Sensitive photon detection in the gigahertz band constitutes the cornerstone to study different phenomena in astronomy, such as radio burst sources, galaxy formation, cosmic microwave background, axions, comets, gigahertz-peaked spectrum radio sources and supermassive black holes. Nowadays, state of the art detectors for astrophysics are mainly based on transition edge sensors and kinetic inductance detectors. Overall, most sensible nanobolometers so far are superconducting detectors showing a noise equivalent power (NEP) as low as 2x10-20 W/Hz1/2. Yet, fast thermometry at the nanoscale was demonstrated as well with Josephson junctions through switching current measurements. In general, detection performance are set by the fabrication process and limited by used materials. Here, we conceive and demonstrate an innovative tunable Josephson escape sensor (JES) based on the precise current control of the temperature dependence of a fully superconducting one-dimensional nanowire Josephson junction. The JES might be at the core of future hypersensitive in situ-tunable bolometers or single-photon detectors working in the gigahertz regime. Operated as a bolometer the JES points to a thermal fluctuation noise (TFN) NEP_TFN 1x10-25 W/Hz1/2, which as a calorimeter bounds the frequency resolution above 2 GHz, and resolving power below 40 at 50 GHz, as deduced from the experimental data. Beyond the obvious applications in advanced ground-based and space telescopes for gigahertz astronomy, the JES might represent a breakthrough in several fields of quantum technologies ranging from subTHz communications and quantum computing to cryptography and quantum key distribution.

Beam impedance minimization for accelerator beamline insertion devices.

Use of complex state-of-the art detectors and monitors is essential to carry out high-energy and nuclear physics experiments at accelerator/collider facilities. The detectors are used to monitor charged particle beam parameters at large accelerator facilities such as coherent light sources and to develop new state-of-the art accelerators. Improvements in beam quality and lifetime necessitate the advancement of the instrumentation for successful operation of the accelerator facilities. Minimization of the beam-line-inserted devices' influence on the beam is therefore one of the essential considerations during the design of such facilities and the preparation of experiments. In this paper, we suggest and discuss a roadmap to minimize this influence. It is developed using fundamental concepts and numerical modeling, and we show that this is a multi-stage and multi-parametric problem that needs careful consideration. To illustrate the roadmap, the vacuum vessel for the vertex locator detector (CERN) is used. The results are discussed and, using them, the steps and stages of the design optimization are suggested. The suggested procedure can be applied to optimize the design of any beamline insertion device and will contribute to the development of next generation particle/accelerator detectors and monitors.

Performance Benchmark in Febrile Mass Screening Detection

Recently, detecting and tracking people using infrared sensors in public surveillance has been given attention by many researchers since the global outbreak of severe acute respiratory syndrome, and machine vision is bound to play an important role. In machine vision, sliding window approach has appeared as most promising approach. Since Papageorgious et al who proposed the first sliding window detetectors until Dalal et al who have come out with a large gains detector based on histogram adoption, the ideas of sliding in human detections continue to serve as a root for modern detectors. While there is increasing research in thermal spectrum, very little research focus on the effectiveness of state of art detectors in febrile mass screening application. The exposed area of head-to-shoulder is an important body part for region of interest detection priorly before the temperature of febrile person is measured by the thermal camera. In addition, some of the thermal datasets used in previous research are more fit to surveillance or safety applications where the targets are mostly far-scales. One of the challenges in detecting the pedestrian in thermal images is the nature and quality of image in infrared spectrum as well as the real crowd situation in public area that cause occlusion. Therefore in this paper, we are interested to evaluate the top three detectors’ performance on thermal images taken during fever screening in Kuala Lumpur International Airport. We also analyze the best approach to be adopted in the detection using a new context of training and evaluation.

A computationally efficient detector for MIMO systems

<p>In this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques.</p>

A binocular image fusion approach for minimizing false positives in handgun detection with deep learning

Object detection models have known important improvements in the recent years. The state-of-the art detectors are end-to-end Convolutional Neural Network based models that reach good mean average precisions, around 73%, on benchmarks of high quality images. However, these models still produce a large number of false positives in low quality videos such as, surveillance videos. This paper proposes a novel image fusion approach to make the detection model focus on the area of interest where the action is more likely to happen in the scene. We propose building a low cost symmetric dual camera system to compute the disparity map and exploit this information to improve the selection of candidate regions from the input frames. From our results, the proposed approach not only reduces the number of false positives but also improves the overall performance of the detection model which make it appropriate for object detection in surveillance videos.

FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization.

In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.

The arms race: Adversarial search defeats entropy used to detect malware

Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. At the string level, information theory and, specifically, entropy play an important role related to detecting patterns altered by concealment strategies, such as polymorphism or encryption. Controlling the entropy levels in different parts of a disk resident executable allows an analyst to detect malware or a black hat to evade the detection. This paper shows these two perspectives into two scalable entropy-based tools: EnTS and EEE. EnTS, the detection tool, shows the effectiveness of detecting entropy patterns, achieving 100% precision with 82% accuracy. It outperforms VirusTotal for accuracy on combined Kaggle and VirusShare malware. EEE, the evasion tool, shows the effectiveness of entropy as a concealment strategy, attacking binary-based state of the art detectors. It learns their detection patterns in up to 8 generations of its search process, and increments their false negative rate from range 0–9%, up to the range 90–98.7%.

The sPHENIX Experiment

Our understanding of QCD under extreme conditions has advanced tremendously in the last 20 years with the discovery of the Quark Gluon Plasma and its characterisation in heavy ion collisions at RHIC and LHC. The sPHENIX detector planned at RHIC is designed to further study the microscopic nature of the QGP through precision measurements of jet, upsilon and open heavy flavor probes over a broad pT range. The multi-year sPHENIX physics program will commence in early 2023, using state-of-the art detector technologies to fully exploit the highest RHIC luminosities. The experiment incorporates the 1.4 T former BaBar solenoid magnet, and will feature high precision tracking and vertexing capabilities, provided by a compact TPC, Si-strip intermediate tracker and MAPS vertex detector. This is complemented by highly granular electromagnetic and hadronic calorimetry with full azimuthal coverage. In this document I describe the sPHENIX detector design and physics program, with particular emphasis on the comprehensive open heavy flavour program enabled by the experiment’s large coverage, high rate capability and precision vertexing.

A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors

Since local feature detection has been one of the most active research areas in computer vision during the last decade and has found wide range of applications (such as matching and registration of remotely sensed image data), a large number of detectors have been proposed. The interest in feature-based applications continues to grow and has thus rendered the task of characterizing the performance of various feature detection methods an important issue in vision research. Inspired by the good practices of electronic system design, a generic framework based on the repeatability measure is presented in this paper that allows assessment of the upper and lower bounds of detector performance and finds statistically significant performance differences between detectors as a function of image transformation amount by introducing a new variant of McNemar’s test in an effort to design more reliable and effective vision systems. The proposed framework is then employed to establish operating and guarantee regions for several state-of-the art detectors and to identify their statistical performance differences for three specific image transformations: JPEG compression, uniform light changes and blurring. The results are obtained using a newly acquired, large image database (20,482 images) with 539 different scenes. These results provide new insights into the behavior of detectors and are also useful from the vision systems design perspective. Finally, results for some local feature detectors are presented for a set of remote sensing images to showcase the potential and utility of this framework for remote sensing applications in general.

A synchronous Gigabit Ethernet protocol stack for high-throughput UDP/IP applications

State of the art detector readout electronics require high-throughput data acquisition (DAQ) systems. In many applications, e. g. for medical imaging, the front-end electronics are set up as separate modules in a distributed DAQ. A standardized interface between the modules and a central data unit is essential. The requirements on such an interface are varied, but demand almost always a high throughput of data. Beyond this challenge, a Gigabit Ethernet interface is predestined for the broad requirements of Systems-on-a-Chip (SoC) up to large-scale DAQ systems. We have implemented an embedded protocol stack for a Field Programmable Gate Array (FPGA) capable of high-throughput data transmission and clock synchronization. A versatile stack architecture for the User Datagram Protocol (UDP) and Internet Control Message Protocol (ICMP) over Internet Protocol (IP) such as Address Resolution Protocol (ARP) as well as Precision Time Protocol (PTP) is presented. With a point-to-point connection to a host in a MicroTCA system we achieved the theoretical maximum data throughput limited by UDP both for 1000BASE-T and 1000BASE-KX links. Furthermore, we show that the random jitter of a synchronous clock over a 1000BASE-T link for a PTP application is below 60 ps.

Discrete area filters in accurate detection of faces and facial features

This paper introduces a new method for detection of faces and facial features. Proposed algorithm denies the thesis that bottom-up solutions can't work at reasonable speed. It introduces fast detection – about 9 frames per second for a 384×256 image – while preserving accurate details of the detection. Main experiments focus on the detection of the eye centers — crucial in many computer vision systems such as face recognition, eye movement detection or iris recognition, however algorithm is tuned to detect 15 fiducial face points. Models were trained on nearly frontal faces. Bottom-up approach allows to detect objects under partial occlusion — particularly two out of four face parts (left eye, right eye, nose, mouth) must be localized. Precision of the trained model is verified on the Feret dataset. Robustness of the face detection is evaluated on the BioID, LFPW, Feret, GT, Valid and Helen databases in comparison to the state of the art detectors.

Cosmic Ray Electrons and Protons, and Their Antiparticles

Cosmic rays are a sample of solar, galactic, and extragalactic matter. Their origin, acceleration mechanisms, and subsequent propagation toward Earth have intrigued scientists since their discovery. These issues can be studied via analysis of the energy spectra and composition of cosmic rays. Protons are the most abundant component of the cosmic radiation, and many experiments have been dedicated to the accurate measurement of their spectra. Complementary information is provided by electrons, which comprise about 1 % of the cosmic radiation. Because of their low mass, electrons experience severe energy losses through synchrotron emission in the galactic magnetic field and inverse Compton scattering of radiation fields. Electrons therefore provide information on the local galactic environment that is not accessible from the study of the cosmic ray nuclei. Antiparticles, namely antiprotons and positrons, are produced in the interaction between cosmic ray nuclei and the interstellar matter. They are therefore intimately linked to the propagation mechanisms of the parent nuclei. Novel sources of primary cosmic ray antiparticles of either astrophysical (e.g., positrons from pulsars) or exotic origin (e.g., annihilation of dark matter particles) may exist. The nature of dark matter is one of the most prominent open questions in science today. An observation of positrons from pulsars would open a new observation window on these sources. Several experiments equipped with state-of-the art detector systems have recently presented results on the energy spectra of electrons, protons, and their antiparticles with a significant improvement in statistics and better control of systematics. The status of the field will be reviewed, with a focus on these recent scientific results.

Alternative particle identification techniques to Cherenkov detectors

Alternative particle identification methods to Cherenkov techniques are reviewed. Particular focus is given to recent advances in Transition Radiation Detectors (TRDs), improvements in dE/dx ionization loss by cluster counting, and Time of Flight (ToF) techniques. In each case several state of the art detectors are highlighted. For advances in ToF techniques, the status of fast photon detectors and electronics developments is summarized.

Precision measurements ofe+e−in Cosmic Ray with the Alpha Magnetic Spectrometer on the ISS

One hundred years after their discovery by Victor Hess, Cosmic Rays are nowadays subject of intense research from space based detectors, able to perform for the first time high precision measurement of their composition and spectra as well as of isotropy and time variability. On May 2011, the Alpha Magnetic Spectrometer (AMS-02), has been installed on the International Space Station, to measure with high accuracy the Cosmic Rays properties searching for rare events which could be indication of the nature of Dark Matter or presence of nuclear Antimatter. AMS-02 is the result of nearly two decades of effort of an international collaboration, to design and build a state of the art detector capable to perform high precision Cosmic Rays measurement. In this paper I will briefly report on the first results of AMS-02 two years after the beginning of the operations in space.

Cosmic ray physics in space: from fundamental physics to applications

One hundred years after their discovery by Victor Hess, cosmic rays are nowadays subject of intense research from space-based detectors, able to perform for the first time high precision measurement of their composition and spectra as well as of isotropy and time variability. On May 2011, the alpha magnetic spectrometer (AMS-02) has been installed on the International Space Station, to measure with high accuracy the cosmic ray properties searching for rare events which could be an indication of the nature of dark matter or presence of nuclear antimatter. AMS-02 is the result of nearly two decades of effort of an international collaboration, involving in particular Chinese and Italian scientists, to design and build a state of the art detector capable to perform high precision cosmic rays measurement. In this paper, I will briefly report on the first results of AMS-02 as well as about two cosmic rays researches related researches which are spinoffs of the AMS technology development: utilization of superconductivity in space to develop magnetic shields capable to protect the astronauts from the intense dose of radiation collected during an interplanetary mission and study of the lithospheric–magnetospheric interactions linking seismology to cosmic rays in the context of the Sino-Italian collaboration on the China seismo-electromagnetic satellite.

A GLANCE BEYOND THE STANDARD MODEL: LATEST RESULTS FROM THE MEG EXPERIMENT

The MEG experiment started taking data in 2009 looking for the Standard Model suppressed decay μ → e + γ, which, if observed, can reveal Beyond Standard Model physics. It makes use of state-of-the art detectors optimized for operating in conditions of very high intensity, rejecting as much background as possible. The data taking ended August 2013 and an upgrade R&D is started to push the experimental sensitivity. The present upper limit on the decay Branching Ratio (BR) is presented, obtained with the subset of data from 2009 to 2011 run, together with a description of the key features of the upgraded detector.