Journal articles on the topic 'Compressive camera identification'

AbstractEVA foams, like all other polymers, also exhibit strain-rate effects and hysteresis. However, currently available approaches for predicting the mechanical response of polymeric foam subjected to an arbitrarily imposed loading history and strain-rate effect are highly limited. Especially, the strain rates in the intermediate rate domain (between 100and 102s–1) are extremely difficult to study. The use of data generated through the drop tower technique for implementation in constitutive equations or numerical models has not been considered in past studies. In this study, an experiment including a quasi-static compression test and drop impact tests with a high speed camera was conducted. An inverse analysis technique combined with a finite element model for material parameter identification was developed to determine the stress–strain behavior of foam at different specific strain rates. It was used in this study to simulate multiple loading and unloading cycles on foam specimens, and the results were compared with experimental measurements.

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

The Photo Response Non-Uniformity pattern (PRNU-pattern) can be used to identify the source of images or to indicate whether images have been made with the same camera. This pattern is also recognized as the “fingerprint” of a camera since it is a highly characteristic feature. However, this pattern, identically to a real fingerprint, is sensitive to many different influences, e.g., the influence of camera settings. In this study, several previously investigated factors were noted, after which three were selected for further investigation. The computation and comparison methods are evaluated under variation of the following factors: resolution, length of the video and compression. For all three studies, images were taken with a single iPhone 6. It was found that a higher resolution ensures a more reliable comparison, and that the length of a (reference) video should always be as high as possible to gain a better PRNU-pattern. It also became clear that compression (i.e., in this study the compression that Snapchat uses) has a negative effect on the correlation value. Therefore, it was found that many different factors play a part when comparing videos. Due to the large amount of controllable and non-controllable factors that influence the PRNU-pattern, it is of great importance that further research is carried out to gain clarity on the individual influences that factors exert.

With the development of artificial intelligence, image recognition has seen wider adoption. Here, a novel paradigm image recognition system is proposed for detection of fires owing to the compression of lithium-ion batteries at recycling facilities. The proposed system uses deep learning method. The SparkEye system is proposed, focusing on the early detection of fires as sparks, and is combined with a sprinkler system, to minimize fire-related losses at affected facilities. Approximately 30,000 images (resolution, 800 × 600 pixels) were used for training the system to >90% detection accuracy. To fulfil the demand for dust control at recycling facilities, air and frame camera protection methods were incorporated into the system. Based on the test data and realistic workplace feedback, the best placements of the SparkEye fire detectors were crushers, conveyors, and garbage pits.

It is difficult to apply existing exposure methods to a resource-constrained platform. Their pyramidal image processing and quality measures for interesting areas that need to be preserved require a lot of time and memory. The work presented in this paper is a DCT-based HDR exposure fusion using multiexposed image sensors. In particular, it uses the quantization process in JPEG encoding as a measurement of image quality such that the fusion process can be included in the DCT-based compression baseline. To enhance global image luminance, a Gauss error function based on camera characteristics is presented. In the simulation, the proposed method yields good quality images, which balance naturalness and object identification. This method also requires less time and memory. This qualifies our technique for use in resource-constrained platforms.

The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.

Omnidirectional, or 360°, cameras are able to capture the surrounding space, thus providing an immersive experience when the acquired data is viewed using head mounted displays. Such an immersive experience inherently generates an illusion of being in a virtual environment. The popularity of 360° media has been growing in recent years. However, due to the large amount of data, processing and transmission pose several challenges. To this aim, efforts are being devoted to the identification of regions that can be used for compressing 360° images while guaranteeing the immersive feeling. In this contribution, we present a saliency estimation model that considers the spherical properties of the images. The proposed approach first divides the 360° image into multiple patches that replicate the positions (viewports) looked at by a subject while viewing a 360° image using a head mounted display. Next, a set of low-level features able to depict various properties of an image scene is extracted from each patch. The extracted features are combined to estimate the 360° saliency map. Finally, bias induced during image exploration and illumination variation is fine-tuned for estimating the final saliency map. The proposed method is evaluated using a benchmark 360° image dataset and is compared with two baselines and eight state-of-the-art approaches for saliency estimation. The obtained results show that the proposed model outperforms existing saliency estimation models.

In the production of coconut oil for consumption, cleanliness and safety are the first priorities for meeting the standard in Thailand. The presence of color, sediment, or impurities is an important element that affects consumers’ or buyers’ decision to buy coconut oil. Coconut oil contains impurities that are revealed during the process of compressing the coconut pulp to extract the oil. Therefore, the oil must be filtered by centrifugation and passed through a fine filter. When the oil filtration process is finished, staff inspect the turbidity of coconut oil by examining the color with the naked eye and should detect only the color of the coconut oil. However, this method cannot detect small impurities, suspended particles that take time to settle and become sediment. Studies have shown that the turbidity of coconut oil can be measured by passing light through the oil and applying image processing techniques. This method makes it possible to detect impurities using a microscopic camera that photographs the coconut oil. This study proposes a method for detecting impurities that cause the turbidity in coconut oil using a deep learning approach called a convolutional neural network (CNN) to solve the problem of impurity identification and image analysis. In the experiments, this paper used two coconut oil impurity datasets, PiCO_V1 and PiCO_V2, containing 1000 and 6861 images, respectively. A total of 10 CNN architectures were tested on these two datasets to determine the accuracy of the best architecture. The experimental results indicated that the MobileNetV2 architecture had the best performance, with the highest training accuracy rate, 94.05%, and testing accuracy rate, 80.20%.

Abstract Background Approximately 30% of patients attend primary care consultations for pain, in which the presence of myofascial pain syndrome (MPS) has been confirmed, provoked by myofascial trigger points (MTrPs). Unfortunately, the treatment of this syndrome is resistant to medication and entails an erroneous diagnosis. Novel methods such as sonoelastography and elastography using magnetic resonance, have recently enabled non-invasive images of trigger points. However, both are costly and difficult to access. Therefore, the identification of MTrPs is still based on the palpable diagnosis criteria defined by Travell and Simons. Interestingly, MPS has been defined as nociceptive pain, however, currently the sympathetic nervous system (SNS) is considered increasingly important for the widespread pain related to MPS and this is indicated with greater frequency. The use of the infrared camera is accepted as an objective method for the diagnosis of patients with pain, especially if activity of the SNS is involved. Thermography has high reliability for muscle exam. Hyperthermic images appear when inflammatory reactions are present, increasing the blood flow due to a greater cell activation. In contrast, hypothermic activation is found when there is compression or degenerative processes. Dry needling (DN) is considered to be a safe and effective method for reducing pain and improving muscle function, provoking a local contraction response in the muscle of myofascial trigger points. Aims The main aim of this study was to evaluate the physiological changes that take place in the medial gastrocnemius (MG) with DN treatment using thermography. Material and Methods In total, 20 basketball players participated in this study, aged between 21 and 39 years old. Initially, before performing DN, pre-intervention images were taken. Subsequently, DN was performed on the MG with the maximum of local twitch responses, evaluating both MG of both legs. Images were taken immediately after DN, and at 15 and at 30 minutes. For this study, a thermographic camera was used (FLIR TUR E60) and the images were analyzed using FLIR TOOLS software. For the DN technique, needles were used measuring 0.30 × 40. The variables studied were the maximum temperature (Tmax), minimum temperature (Tmin) and mean temperature (Tavg), in degrees Celsius. Results A decreased temperature exists (T°) both for Tmax, Tmin and Tavg before and after the DN intervention. We observed a significant difference (p = 0.035) between Tmin pre-intervention and immediately after the intervention in the left leg. The other significant difference found was between Tmin pre-intervention on the left leg and after 30 minutes (p = 0.009). We found significant differences between Tmin pre and immediately post intervention on the left leg (p = 0.021), Tmin pre and after 15 minutes on the left leg (p = 0.007) and pre intervention and after 30 minutes on the left leg (p = 0.002). Other significant differences on the right leg were Tmin pre-intervention and immediately after (p = 0.019), and after 15 minutes (p = 0.008). Conclusions Dry needling may be a good method for reducing inflammation of the trigger point for its ability to decrease temperature, and therefore may allow us to decrease the maximum, minimum and average temperature of the muscle belly.

Abstract Production plants wishing to become competitive on the local and global market should offer their products at the lowest possible price and, at the same time, in the highest possible quality. One of the key tasks for the engineering and technical staff working on them is improving the organization of work and the degree of use of means of production, allowing to increase work efficiency. The latest technological achievements focused on the concept of the Industry 4.0 create new opportunities for further improvement of production processes, their optimization, reduction of costs, or reduction of the nuisance of physical work and mental effort of employees. Many industries use both renewable and nonrenewable natural resources available on earth. Acquiring some of them requires considerable resources and is very burdensome for people. Especially regarding resources deep underground. In the near future, we will also use material resources obtained from outside the Earth, which is an additional challenge. Therefore, the integration of the latest technologies, especially in the field of automation and autonomous robots, IoT and Big data is becoming an essential research area. The paper presents an overview of technological methods and solutions of material identification that can be used in the processes of automated sourcing of materials on an industrial scale. Particular attention was paid to techniques that can be used by specialized, autonomous robots working in conditions that are hard and unsafe for humans. Discussed recognition systems can use advanced methods and the latest technologies. They are most often implemented using special sensors installed on or close to the executive element of a machine. The differences in rocks hardness and compressive strength allowed for the development of methods using acoustic waves and also methods using the measurement of vibration of the cutting tools and machine parts on which they are installed. Another method uses the natural features of rocks adjacent to the seam being extracted - it measures differences in the level of radioactivity. On this basis, a method was developed based on the recognition of low gamma radiation activity. Methods based on the use of radars or thermal imaging cameras testing the temperature of cutting knives installed on the cutting body were also proposed. Another interesting solution is the optical touch sensor that recognizes different types of rock layers. Difficult working conditions, also for machines, in which there is a high concentration of stone dust limiting visibility, the presence of water, and high noise generated by working devices, reveals a number of advantages and disadvantages in the above methods to use them in autonomous, robotic solutions.

The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

OBJECT Approximately 250 million surgical procedures are performed annually worldwide, and data suggest that major complications occur in 3%–17% of them. Many of these complications can be classified as avoidable, and previous studies have demonstrated that preoperative checklists improve operating room teamwork and decrease complication rates. Although the authors’ institution has instituted a general preoperative “time-out” designed to streamline communication, flatten vertical authority gradients, and decrease procedural errors, there is no specific checklist for transnasal transsphenoidal anterior skull base surgery, with or without endoscopy. Such minimally invasive cranial surgery uses a completely different conceptual approach, set-up, instrumentation, and operative procedure. Therefore, it can be associated with different types of complications as compared with open cranial surgery. The authors hypothesized that a detailed, procedure-specific, preoperative checklist would be useful to reduce errors, improve outcomes, decrease delays, and maximize both teambuilding and operational efficiency. Thus, the object of this study was to develop such a checklist for endonasal transsphenoidal anterior skull base surgery. METHODS An expert panel was convened that consisted of all members of the typical surgical team for transsphenoidal endoscopic cases: neurosurgeons, anesthesiologists, circulating nurses, scrub technicians, surgical operations managers, and technical assistants. Beginning with a general checklist, procedure-specific items were added and categorized into 4 pauses: Anesthesia Pause, Surgical Pause, Equipment Pause, and Closure Pause. RESULTS The final endonasal transsphenoidal anterior skull base surgery checklist is composed of the following 4 pauses. The Anesthesia Pause consists of patient identification, diagnosis, pertinent laboratory studies, medications, surgical preparation, patient positioning, intravenous/arterial access, fluid management, monitoring, and other special considerations (e.g., Valsalva, jugular compression, lumbar drain, and so on). The Surgical Pause is composed of personnel introductions, planned procedural elements, estimation of duration of surgery, anticipated blood loss and fluid management, imaging, specimen collection, and questions of a surgical nature. The Equipment Pause assures proper function and availability of the microscope, endoscope, cameras and recorders, guidance systems, special instruments, ultrasonic microdoppler, microdebrider, drills, and other adjunctive supplies (e.g., Avitene, cotton balls, nasal packs, and so on). The Closure Pause is dedicated to issues of immediate postoperative patient disposition, orders, and management. CONCLUSIONS Surgical complications are a considerable cause of death and disability worldwide. Checklists have been shown to be an effective tool for reducing preventable errors surrounding surgery and decreasing associated complications. Although general checklists are already in place in most institutions, a specific checklist for endonasal transsphenoidal anterior skull base surgery was developed to help safeguard patients, improve outcomes, and enhance teambuilding.

The article deals with certain medico-legal aspects of trauma in the salon of bus as one of the types of road traffic accidents with a large number of dead and injured. Are shown the typical causes of such incidents and the nature of the victims injury. Was developed and proposed a modern approach to optimization of expert research in case of appearance a large number of victims in the bus. Circumstances of injury in case of personal injury people in the bus are very diverse:− rollover of the bus when transporting a large number of people while driving;− bus falling from height;− a massive collision with a fixed roadside objects; − collision with other vehicles; among the latter is the most fatal bus collision with a moving train.Naturally, in these cases, the massive injuries have affected depends on the intensity of injury to passengers in the bus, and the mechanism of damage is determined by the specific form of an accident involving a bus. In such cases, the experts faced, usually with mechanical trauma inside the cabin, and mixed types of injuries passengers (e.g. in case of fire). For in-car trauma characterized by formation damage from the following mechanisms:− shock bodies on the inner part of the interior (interior);− injuries from the shards of broken glass.Basically, the nature of injury is determined by the structural features of the bus, the presence of foreign objects, the location of the victims. If the vehicle rolls over, the occupants people are numerous additional impact. Formed characteristic for the driver damage to the hands, fractures of the sternum fractures of the hips, legs and feet. For passengers is characteristic fractures of the lower limbs, bruised head wounds, fractures and dislocations of the cervical spine when using the seat belts − stripe-like bruises and abrasions ofthe chest and abdomen, broken ribs, collarbone, sternum, in the projection of the belts. Shards of broken glass caused by the multiple linear abrasions and (or) surface or deep cut wounds mainly in the face and upper extremities. In the case of deformation of the bus body can be compression of the bodies are formed by damage to several areas, primarily the chest, abdomen, extremities, accompanied by multiple bilateral rib fractures, ruptures of internal organs. If in the future there is a fire or explosion of the vehicle, the nature of thedamage detected on the bodies will correspond to the combined injury.In cases of injuries in the bus to work with the bodies of the victims begins at the scene. Thus, the Protocol of inspection of the scene and of the corpse in the first place should reflect the data about the mutual position of bodies and (or) their fragments relative to the vehicle and other parts, the distance between them; the condition of clothing, odors from it, the presence of different overlays, damage; contamination of the skin; localization and nature of the injuries on the bodies, the presence of deformations of its individualparts; the presence of traces of biological origin on the vehicle in comparison with the nature of the deformation (damage) of the body.Be sure to note the results of the inspection of the road where there was a traffic accident, a bus traces of blood, and fragments of various things, etc. Despite the small percentage of bus injured in world General statistics of fatal injuries, it presents certain difficulties in planning, organization, execution and coordination of forensic work on multi-step liquidation of medical consequences of the accident, usually associated with a large number of victims, gross impact of factors affecting on the bodies of the victims, the need to quickly address some specific issues: establishing at autopsy pathological symptoms that indicate the status of the health of drivers in the period priorto the tragic event; the existence of facts pointing to the use of intoxicating and medicinal substances that depress the nervous system and many others), early identification of all victims. According to the results of the analysis made it impossible to offer modern, optimal, evidence-informed, and until only itremains to be reliable in practice the system approach to the organizational model of forensic activities, while ensuring the interests of the investigation of an accident involving a bus and a large number of victims:1. The preliminary stage of organization expert services. It can conditionally enough be divided into 2 phases:− advance (pre-) phase;− the immediate phase.To the basic questions of the early phases include: early development, coordination and approval of the optimal legislative and other regulatory framework; preliminary methodological, administrative and organizational, theoretical-practical, logistical, software and applied training; exercise reasonable estimates of projected short and long term needs and costs with regard to the peculiarities specified by the tragic events; creation, storage, use and replenishment of the trust reserves, logistical and financial resources areinviolable, is intended solely for use in such emergencies. It also includes the creation, maintenance and continuous improvement of a Single centralized situation center on a temporary or permanent basis, with a good system of departmental and interdepartmental cooperation, primarily containing a - operational information-Supervisory and analytical center for the collection, processing, storage, information exchange and joint action with the threat, occurrence and prevention of emergencies with a large number of victims.Immediately with the receipt of the news of the accident involving a bus and a large number of victims for forensic services begin immediate phase, the main elements of which include:− prompt notification and collection of employees and expert institutions;− an emergency conference call to discuss the organizational, theoretical and practical questions and short specialized trainingon occupational safety, including use of personal protective equipment depending on the nature of the accident and actions are potentially dangerous to health and life of employees and expert institutions factors.All plans of measures are necessarily coordinated and agreed with appropriate representatives of structures of fast reaction, especially when conducting urgent investigative actions in the emergency areas, primarily the inspection of the scene. 2. The inspection of the crime scene it is advisable to start with a preliminary review («intelligence»), which finally determined the necessity of application of those or other technical means, and the number of specialists who will participate in the inspection.The static phase of scene examination with the participation of forensic doctors is accompanied by clear mapping; mapping, photo - and video fixing of vehicle, various objects; it is noted the exact relative positions of the bus (its parts) and discovered the corpses, fragments of human remains and other biological material. During dynamic examination of the scene produce a detailed external examination of the human remains, their fragments, biological material, perform primary medical sorting, their careful packaging,clear detailed marking. Then performed the proper loading, transportation and unloading. In case of need in a temporary Deposit of biological material, can be used in railway wagons refrigerators, refrigerated trailers, mobile camera with a refrigeration unit, and in the absence or lack of volume for the total number of remains and the biomaterial deploys heat-resistant boxes, fit the space with the use of outdoor mobile air conditioning systems, large amounts of ice obtained from specialized industrial ice makers, etc., which is especially important for braking processes of rotting corpses, their fragments and biomaterial in the warm season.3. After the initial registration and a secondary sort examine corpses, their fragments and biological material collection for postmortem identification of significant information, determine the cause of death, nature, mechanism and prescription of formation damage and address other special issues. At this stage also produce the identification of fragmented body parts and (or) tissues that or another body. In expert identification work on the fragments of human remains or biological material, preference is given to genetic research providing highly accurate results. Depending on the extent of influence of damaging factors on the bodies of the victims and their degree of preservation, only after the completion of the necessary is judicial-medical research with a full range of fence material for additional research, producing restoration of the exterior, embalming, sanitary and cosmetic processing of human remains and give them to relatives (relatives, authorized representatives, etc.) for burial. 4. Issued the final results of examinations; establishes data that may be useful for later investigative and judicial actions aimed at gathering and verification of evidence in a criminal case.5. The penultimate stage consists of conducting sanitary-and-hygienic, treatment-and-prophylactic, rehabilitation (including a full psychological) of interventions for physical and mental health of employees and expert institutions involved in this work.6. After the conclusion of the criminal proceedings in general, with the official opening of data access, it is advisable to analyze the material, and publish the relevant data in the scientific literature, with the goal of widespread study and use of gained experience.CONCLUSIONS.1. Research platform forensic activities in cases of accidents involving buses and a large number of victims to date have not been developed.2. The effectiveness of forensic medical groups in this situation is in direct proportion to the degree of readiness for quick response and timely quality completion of tasks.3. Based on this, very urgent is the development of modern optimal evidence-based systemic approach to the organizational model of forensic activities in the presence of a large number of injured persons in the bus; the solution to this problem and sent the above recommendations.4. The recommendations, in principle, can be applied not only in cases of injuries in the bus, but also to similar situations in which there is a massive injury and loss of life.5. It is necessary to continue scientific and practical research aimed at improving this algorithm works experts.

AbstractRecent years have witnessed a substantial increase in the deep learning (DL) architectures proposed for visual recognition tasks like person re-identification, where individuals must be recognized over multiple distributed cameras. Although these architectures have greatly improved the state-of-the-art accuracy, the computational complexity of the convolutional neural networks (CNNs) commonly used for feature extraction remains an issue, hindering their deployment on platforms with limited resources, or in applications with real-time constraints. There is an obvious advantage to accelerating and compressing DL models without significantly decreasing their accuracy. However, the source (pruning) domain differs from operational (target) domains, and the domain shift between image data captured with different non-overlapping camera viewpoints leads to lower recognition accuracy. In this paper, we investigate the prunability of these architectures under different design scenarios. This paper first revisits pruning techniques that are suitable for reducing the computational complexity of deep CNN networks applied to person re-identification. Then, these techniques are analyzed according to their pruning criteria and strategy and according to different scenarios for exploiting pruning methods to fine-tuning networks to target domains. Experimental results obtained using DL models with ResNet feature extractors, and multiple benchmarks re-identification datasets, indicate that pruning can considerably reduce network complexity while maintaining a high level of accuracy. In scenarios where pruning is performed with large pretraining or fine-tuning datasets, the number of FLOPS required by ResNet architectures is reduced by half, while maintaining a comparable rank-1 accuracy (within 1% of the original model). Pruning while training a larger CNNs can also provide a significantly better performance than fine-tuning smaller ones.

AbstractWe present a systematic evaluation of JPEG2000 (ISO/IEC 15444) as a transport data format to enable rapid remote searches for fast transient events as part of the Deeper Wider Faster programme. Deeper Wider Faster programme uses ~20 telescopes from radio to gamma rays to perform simultaneous and rapid-response follow-up searches for fast transient events on millisecond-to-hours timescales. Deeper Wider Faster programme search demands have a set of constraints that is becoming common amongst large collaborations. Here, we focus on the rapid optical data component of Deeper Wider Faster programme led by the Dark Energy Camera at Cerro Tololo Inter-American Observatory. Each Dark Energy Camera image has 70 total coupled-charged devices saved as a ~1.2 gigabyte FITS file. Near real-time data processing and fast transient candidate identifications—in minutes for rapid follow-up triggers on other telescopes—requires computational power exceeding what is currently available on-site at Cerro Tololo Inter-American Observatory. In this context, data files need to be transmitted rapidly to a foreign location for supercomputing post-processing, source finding, visualisation and analysis. This step in the search process poses a major bottleneck, and reducing the data size helps accommodate faster data transmission. To maximise our gain in transfer time and still achieve our science goals, we opt for lossy data compression—keeping in mind that raw data is archived and can be evaluated at a later time. We evaluate how lossy JPEG2000 compression affects the process of finding transients, and find only a negligible effect for compression ratios up to ~25:1. We also find a linear relation between compression ratio and the mean estimated data transmission speed-up factor. Adding highly customised compression and decompression steps to the science pipeline considerably reduces the transmission time—validating its introduction to the Deeper Wider Faster programme science pipeline and enabling science that was otherwise too difficult with current technology.

This paper aims at the identification of a standard security system for commercial and personal vehicles installed on a remote-controlled unlocking device that promises a high accuracy without compromising on the response time. The proposed technology combines three bio-metric security systems on the basis of their performance and response times to make vehicles more secured. The paper compares the efficiencies of different bio-metric security systems based upon their mean accuracy and response times. Primary data have been collected using existing devices and technology, a detailed statistical comparison is done using computational tools like IBM SPSS 2.0 and Microsoft Excel using statistical concepts like ANOVA, Square of means, Descriptive statistics, Central tendencies, etc. The hardware required for this proposed security system is already available at reasonable cost and can be implemented in the field of automobile and a standard security system can be identified for use across all variants of vehicles universally for all the manufacturers. The performance of the bio-metric devices was measured using a 16-megapixel Sony camera IMX371 Exmor RS sensor with a pixel size of 1.0 micro-meter, mounted on a OnePlus 5T mobile phone for face recognition and a fingerprint sensor with a claimed unlock speed of 0.2 seconds mounted on the same device. Mantra MI S100 single iris scanner was used with a high-resolution sensor (CMOS) and captures images with a JPEG2000 compression format.

Abstract The changed security scenario of the modern time has necessitated increased and sophisticated vigilance of the countries' borders. The technological challenges involved in accomplishing such feat of automated security system are many and require research at the components-and-algorithms as well as the architectural levels. This paper proposes an architectural framework for automated video surveillance comprising a network of sensors and closed circuit television cameras as well as proposing algorithmic/component research of software and hardware for the core functioning of the framework, such as: communication protocols, object detection, data-integration, object identification, object tracking, video compression, threat identification, and alarm generation. In this paper, we are addressing some general topological and routing features that would be adopted in our system. There are two types of data with regard to data communication – video stream and object detection. The network is broken down into several disjoint, almost equal zones. A zone have one or more one cluster. A zone manager is chosen among the cluster heads depending on their relative residual energies. There are several levels of control that could be implemented with this arrangement with localized decision made, to get distributed effect at all levels. A cell tracks each target in its zone. If the target moves out of the range of a cell, the cell manager will send the target description to estimated next cell. The next cell starts tracking the target. If the estimated cell is wrongly chosen, corrections will be made by the cluster heads to get the new target-tracking. We also propose bitrate reduction algorithms to accommodate the limited bandwidth. One of the main feature of this paper is introducing a Low-Power Low-Bit rate video compression algorithm to accommodate the low power requirements at sensor nodes, and the low bit rate requirement for the communication protocol. We proposed two algorithms the ALBR and LPHSME. ALBR is addressing low bit rate required for sensors network with limited bandwidth which achieves a reduction in Average number of bits per Iframe by approximately 60% in case of low motion video sequences and 53% in case of fast motion video sequences . LPHSME addresses low power requirements of multi sensor network that has limited power batteries. The performance of the proposed LPHSME algorithm versus full search and three step search indicates a reduction in motion estimation time by approximately 89% in case of low motion video sequences (e.g., Claire ) and 84% in case of fast motion video sequences. The reduced complexity of LPHSME results in low power requirements.