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Conventional maximum power point tracking algorithms are usually fall into local peak power point under non-uniform solar irradiance. So an improved butterfly optimization algorithm for photovoltaic maximum power point tracking is proposed. In global updating phase, the introduction of previous position change and the rewriting of butterfly position update equation are used to improve convergence speed and search accuracy. In local updating phase, the best position of individual butterfly is introduced to improve search efficiency. The simulation and experimental results show that IBOA can track the maximum power point quickly and accurately, and the tracking speed and the tracking efficiency are better than PSO.

To alleviate the spread of the epidemic, most public places have begun to limit the number of trips. Therefore, this article proposes a pedestrian counting scheme based on YOLOv5 and DeepSORT for multi-target detection and tracking. Using the network weights trained by the coco data set and combining the YOLOv5 detector and the DeepSORT tracker, the pedestrians are detected and tracked, and the number of people entering and leaving is calculated, thereby realizing the control of the number of floating people. Through experiments on streets and subway stations, it is proved that this algorithm is suitable for tracking and counting high-density people, and based on ensuring the real-time performance of the system, it provides high system accuracy and robustness.

In this paper, the imaging characteristics and motion characteristics of small targets in infrared images are analyzed from the perspective of space domain and time domain respectively, and a real-time infrared small target search and tracking algorithm based on adaptive track correlation is proposed. From the perspective of spatial domain, the curvature filtering algorithm is used to suppress the background according to the feature that the small target usually presents a sharp peak on the 3D surface with gray value as Z-axis. From the perspective of time domain, according to the difference of motion state between the target and the false alarm point, the track correlation method is used to further suppress the false alarm rate and realize the search and tracking of the target. In this paper, the concept of cosine similarity is introduced to judge the matching degree between suspected target and track on the basis of the traditional track correlation algorithm based on the idea of "nearest neighborhood", so as to reduce the probability of wrong track correlation. At the same time, based on residual analysis, the correlation gate size is selected adaptively and the observation results are smoothing by adaptive first-order low-pass filtering. Experiments show that the search and tracking algorithm proposed in this paper has real-time performance and high accuracy, and has good adaptability to different scenes.

Hardware in the loop simulation test technology of electro-optic system includes video injection technology and dynamic projection technology. Firstly, the dynamic infrared scene simulation technology is used to generate missile attack and complex jamming scene images; Secondly, the digital video is injected to simulate the front-end sensor and stimulate the integrated processor to detect and track the target; Finally, the large field of view medium wave infrared dynamic target simulator is used to build the missile threat environment to carry out the functional performance test of electro-optic system under the threat of single/multi-target attack. The experimental results show that the technology can build complex scenes according to the battlefield scenario, test the processing capacity of electro-optic system under the threat of single/multi-target attack, support the verification of search, acquisition, tracking and other performance indicators of electro-optic system, and provide support conditions for the development and debugging of core algorithms of electro-optic system.

A kind of frequency tracking circuit based on phase-locked loop (PLL) is studied for the resonant infrared sensor in the paper. The operating principle of the PLL is analyzed. 74HC4046 as the core component of the circuit is selected; starting the lock circuit is researched; a closed-loop frequency tracking circuit based on 74HC4046 is built. Software simulation proves that the frequency tracking of the circuit can achieve the order of MHz and has good frequency stability and tracking performance, which lays a foundation for the construction of the follow-up resonant sensor excitation/detection circuit.

Air-to-air missile is the main source of air confrontation, which plays an important role in air confrontation. The attack ability of air-to-air missile is one of the key factors of air combat. Establishing the models of motion and seeker tracking and introducing the greedy algorithm to improve the tracking ability, while regarding the third flight as attack target and establishing the model of motion and radiation. Simulating the infrared radiation characteristic of the target, calculating the attack region of missile and the optimization performance of algorithm It’s concluded that attack region of the simulation matches the reality and the greedy algorithm can usefully improve the recognition efficiency of seeker.

Under the environment of MATLAB, a closed-loop feedback laser tracking system was established based on the dynamic prediction with Kalman filter and some other filtering processes. Different motion states of the tracked target are simulated to test the tracking performance. The following conclusions are obtained through simulations. After adding the dynamic prediction with Kalman filter, the tracking hysteresis can effectively be avoided when tracking the dynamic object. Taking advantage of the high frequency of PSD, the coordinate value can be read for several times in a feedback loop and then filtered to obtain a more accurate spot coordinate. Under static condition, the instability due to noise can be reduced through segmenting the feedback coefficient by distinguishing between the dynamic and static state of the object. According to the above designs, the results of the laser tracking system simulations show that the dynamic tracking performance is better than 100°/s, and static stability has an order of magnitude improvement than before.

Most virtual reality (VR) headsets nowadays use conventional, rotationally symmetric optics to create a wide field of view (FOV > 90°) virtual scene enabling the required “immersion” or “presence” feeling. These optics require a long total track length (TTL, distance between the actual panel displaying the contents and user’s pupil) to work well, and headsets become very bulky. The so-called CLOVER is an optic, compatible with VR and video-see-through mixed reality (MR) able to work around the TTL problem by using a freeform multi-channel, light folding approach. In its simplest version, it can reduce the TTL down to a half, compared to conventional solutions, for the same FOV and angular resolution. Along with a review of the original 4-channel CLOVER, this work shows recent results of upscale versions of the optic that utilize myopia and color correction, pupil tracking and staggered surfaces to, respectively, avoid the need of prescription lenses, improve the image quality for all colors, rise the resolution (by a 20%) and reduce the size (20% TTL reduction) of the precursor.

Currently, there is a significant increase in the number of violations of the airspace by small aircrafts in civilian protected areas, such as airports, crowded areas, industrial enterprises, etc. In this regard, the task of detecting, determining the coordinates and tracking air objects crossing the guarded perimeter and used both for video filming and for the delivery of goods becomes especially urgent. Often used locating tools located on the territory have blind spots, or observation of objects is difficult, which is mainly observed in dense urban development. The paper presents the advantages and disadvantages of radar and optical monitoring systems allowing for real-time observation. The problem of processing frames of the video stream of optical and radar images for the purpose of tracking, as well as extrapolating the coordinates of the position of unmanned aerial vehicles in the event of a temporary loss of visibility, including full shading, partial shading or other types of natural interference. To solve this problem, a method is proposed based on the use of a set of adaptive filters tuned to various types of motion of objects in order to predict their position.

The full convolution Siamese network for object tracker formulate tracking as convolutional feature cross-correlation between a target template and a search region. This tracker realizes real-time object tracking. However, when there are interference factors similar to the target object, Siamese trackers still have an accuracy gap compared with state-of-theart algorithms. Therefore, we proposes an object tracking based on response maps fusion Siamese network(Siam-RMF ). Different from the full convolution Siamese network for object tracker, when the Siam-RMF tracker performs similarity learning, it no longer uses the features extracted by the last layer of the network, but extracts the features of the last three-layer network. Moreover, we propose a new model architecture to perform layer-wise and depth-wise aggregations, the depth-wise separable convolution is used to learn the similarity respectively to obtain the effective fusion of the corresponding depth cross-correlation response map. The fusion response maps can effectively avoid the loss of spatial information after multi-layer feature extraction. Experimental results on TB50 and UAV123L demonstrate the effectiveness of the proposed tracker without decreasing the tracking speed, and show stronger robustness and better tracking performance in complex environments.

Different from object tacking on the ground, underwater object tracking is challenging due to the image attenuation and distortion. Also, challenges are increased by the high-freedom motion of targets under water. Target rotation, scale change, and occlusion significantly degenerate the performance of various tracking methods. Aiming to solve above problems, this paper proposes a multi-scale underwater object tracking method by adaptive feature fusion. The gray, HOG (Histogram of Oriented Gradient) and CN (Color Names) features are adaptively fused in the background-aware correlation filter (BACF) model. Moreover, a novel scale estimation method and a high-confidence model update strategy are proposed to comprehensively solve the problems caused by the scale changes and background noise influences. Experimental results demonstrate that the success ratio of the AUC criterion is 64.1% that is better than classic BACF and other methods, especially in challenging conditions.

The detection of mobile robot performance often requires costly and high-precision trajectory tracking equipment, which is often difficult for testing laboratories to afford. Therefore, it becomes a difficult problem to find the equipment that is cheap and can meet the detection requirements. HTC's Vive Tracker, as an inexpensive virtual reality (VR) accessory, provides good measurement accuracy. In this study, the Vive Tracker is used for the purpose of mobile robot performance detection, mainly focusing on the ability of the device to capture and analyze the mobile robot's movement trajectory. Through the test and analysis of Vive Tracker, it is verified that Vive Tracker can meet the accuracy requirements of mobile robot detection, and can well capture the mobile robot's movement trajectory and be used for mobile robot performance analysis.

The whole dynamic process of blood coagulation can be characterized by tracking the MSD of optical vortices with our previous coagulation measurement system. To develop a portable prototype of the coagulation detection system, we use an embedded system for whole blood laser speckle image acquisition, and apply deep learning methods for the temporal and spatial interpolation of the acquired images and the fast localization of optical vortex. The prototype implementation with a compact optical design and experimental validation provide a feasible idea and method for the miniaturization of blood coagulation devices.

In recent years, there has been a great progress on object-tracking task. Most of them are based on Joint Detection and Embedding (JDE) benchmark, which accomplish detection and Re-ID task in a single module and thus could reduce the time cost and help to gain a higher processing FPS. However, large computation requirement of existing JDE-based method, which usually demand several expensive GPU devices, is still an obstacle to wide application for industry. In this paper, we propose a new lightweight structure named ShuffleXnet, and further build a simple module named Pyramid-ShuffleXnet (PSXnet) for Multiple-Object Tracking (MOT) task. The motivation of this work is to reduce the amount of calculation and make the network easier to be employed for online and real-time applications. Experimental results show that our method could achieve nearly 28% higher FPS than FairMOT with just 6.7% less by Multi-Object Tracking Accuracy (MOTA) score on MOT17 dataset.

The target tracking method based on RGB image is affected by many factors such as light and haze weather, so it is difficult to distinguish the tracking target from the background, and it is easy to lead to the drift or even loss of the tracking target. Target tracking based on infrared images is not affected by light, haze and other illumination factors, but the target's color, texture and other characteristic information will be missing. Therefore, in order to obtain the target's color, texture and other characteristic information in a poorly illuminated environment, while achieving accurate and fast tracking of the object, this paper proposes a RGB and infrared image fusion tracking method based on a deep convolutional network. Firstly, the fusion method of RGB and infrared images is studied; secondly, a target tracking network based on the Siamese network is established to extract the image convolution features of the target template and the current target; finally, the response map is calculated by the deep cross-correlation module. At last, the performance test of the target tracking algorithm is carried out on the VOT2019RGBT data set. Experimental results show that the algorithm can effectively solve the problem of target tracking when the target is partially occluded, the tracking scene has no suitable illumination or the light changes strongly, and it is of great significance to improve the accuracy of target tracking under complex backgrounds.

In order to further improve the robustness of the ambiguity suppression related filtering algorithm (ARCF) tracking in many complex scenes such as rapid motion and occlusion, an adaptive normal punishment coefficient is proposed based on the relevant filter algorithm of automatic space-time normalization. At the same time, in order to make the tracking results more accurate, a object peak detection and selection algorithm is proposed specifically for multi-peak situation, which can determine the peak condition of the response map of the current frame and select the peak that best meets the object in multi-peak situation. In the template update stage, in order to keep the updated template from the pollution of similarities and masks, this paper also proposes a situation to detect whether the object is obscured, as a condition for the template update, so that the object template can better match the object.

Surveillance is an important task during naval operations. This task can be performed with a combination of different sensors, including camera systems and radar. To obtain a consistent operational picture of possible threats in the vicinity of a ship, the information from the different sensors need to be combined into one overview image, in which all information related to one object is assigned to this object. In this paper, we present a new dataset for maritime surveillance applications and show two examples of combining information from different sensors. We have recorded data with several camera systems, automatic identification system (AIS) and radar in the Rotterdam Harbor. From all sensors we can obtain tracking information from the different objects. We present a method to associate the tracks and describe how snippets of the ships in the cameras can be used to enrich the information of the objects. Next to that, we show the combined information from AIS and imagery.

Many applications such as ground to satellite optical communications or astronomy require precise knowledge of cloud cover, turbulence and absorption. In the case of telecoms, this data is critical for the initial ground station sites survey; during ground station operation to inform link availability and bandwidth; and finally, to predict atmospheric conditions over different ground stations for network planning. Historically the turbulence by night time has been measured by astronomers with research class solutions installed on observatories sites. Many implementations exist using either the moon or the stars as reference target. One of them is the Differential Image Motion Monitor (DIMM) from M. Sarazin and F. Roddier with the first implementations back in the 80’s for the ESO. All these turbulence monitors have in common the integration of a small telescope in the 20 to 40cm aperture range with various aperture masks on an automatic tracking mount within a protective dome. This form factor and cost is not in line with the requirement of a more industrial utilization as expected by telecom operators or for atmospheric studies. Since 2018, Miratlas has been using a simpler implementation of the image motion monitor (NSM) with a fixed outdoor system using a single aperture aiming at Polaris. Nevertheless this single aperture system requires a very stable fixture which is not always available and doesn’t apply in southern hemisphere. Therefore, Miratlas has developed a small outdoor implementation of a legacy DIMM named the C(ompact)-DIMM. It uses two different optical assemblies and two identical synchronized cameras to fulfil the same features. The C-DIMM is small enough to be installed anywhere, is not sensitive to vibration and therefore can be installed either on a fixed mount aiming at Polaris, or on a small outdoor tracking mount to operate on any sufficiently bright star and therefore under any latitude.

Unmanned aerial vehicles (UAV:s) have become an increasing threat in both civilian and military arenas. While military UAV:s often are relatively large and complex, the supply in the civilian hobby market is characterized by small and cheap systems with the capacity to stream high-definition video, carry a variety of other sensors and transport critical goods (eg food or medicine) to hard-to-reach places. The criminal world has quickly realized how UAV:s can be used to smuggle weapons or drugs, for example. Militarily, UAV:s are established for reconnaissance, fire control and electronic warfare operations. Laser-guided weapons from a UAV, is an example of a widely used system for precision operations during later conflicts. This paper examines and summarizes various laser functions and their role for detecting, recognizing, tracking and combating a UAV. The laser can be used as a support sensor to others like radar or IR to detect end recognise and track the UAV and it can dazzle and destroy its optical sensors. A laser can also be used to sense the atmospheric attenuation and turbulence in slant paths, which are critical to the performance of a high power laser weapon aimed to destroy the UAV.

A line scanning ladar can generate detailed three-dimensional images of a scene, so-called point clouds, by emitting individual laser pulses in quick succession in various directions and measuring the time before arrival of return pulses. As a typical mode of operation, the pulses are emitted along horizontal lines, starting from bottom of the field of view, before gradually increasing the elevation angles of subsequent scanning lines. This paper aims to address an inherent problem with object recognition within point clouds acquired by a line scanning ladar. If some of the scene objects are moving, their position will change slightly between each sweep of a horizontal scanning line. This causes the shape of the moving objects to deform in the resulting point cloud. The problem becomes more severe for wide view angles, slow scan speeds and fast moving objects. An object recognition algorithm is proposed that corrects for shape deformations caused by the delay between individual point measurements. In addition, the algorithm is able to estimate the velocity of the recognized object. The algorithm matches observed objects against a 3D model of the object of interest, by optimally aligning them with each other while simultaneously estimating the optimal shape deformation caused by motion during acquisition. If the observed object and 3D model aligns sufficiently well, according to a certain recognition confidence measure, the observed object is regarded as recognized and its velocity is induced from the estimated shape deformation. To solve the underlying optimization problem, the ”Iterative Closest Point” (ICP) algorithm is modified by incorporating an additional substep, where the shape deformation – and thereby the corresponding velocity - is updated incrementally each iteration. Experiments on simulated and real world data indicate that moving objects can be recognized with high confidence and their velocities can be estimated with high accuracy.

A spaceborne LIDAR with laser precision tracking technology was used for a Rendezvous and Docking Mission. In the ground experiment, the laser echo energy attenuation, beam jitter, spot drift, intensity fluctuation, beam expansion and other changes are caused by the effects of atmospheric attenuation and atmospheric turbulence on the light path, and the tracking performance of the lidar has significantly deteriorated compared with the actual application in vacuum environment. In this paper, the atmospheric turbulence effect noise model of laser band of the LIDAR is calculated and simulated. Experiments are carried out in atmospheric turbulence and vacuum environment respectively, and the tracking measurement performance of lidar in non-vacuum with atmospheric turbulence and vacuum environment was verified. The variation of tracking performance of lidar in space and ground is analyzed and some results are also discussed in this paper.

This effort characterizes proper sampling of laser speckle in wave-optics simulations, with an emphasis on active imagers in outdoor environments. Modeling of performance degradations induced by speckle is critical in the design of such devices. We expose tradeoffs between sampling conditions in multiple planes of interest, namely the object, pupil and focal planes of an imaging system. The goal of our analysis is to develop an optimized numerical tradespace that models the underlying physics of speckle and turbulence with high fidelity. We begin by showing that speckle statistics are relatively straightforward to produce in the case of vacuum propagation. Then by propagating through different strengths of turbulence, we demonstrate how sampling requirements can become much more difficult to satisfy. We pay particular attention to the problem of sufficiently sampling a target object without subjecting it to anisoplanatism. As a way of overcoming such challenges, we propose and test an optimization routine that defines acceptable simulation parameters based on user-defined physical parameters. Successful implementation of this approach streamlines the design process for applications that involve active target tracking and coherent imaging through turbulence.

An algorithm for the recognition and tracking of several objects in color image sequences is presented. First, each three-channel color input image are encoded into a single-channel complex-valued image. Next, a set of prespecified targets are recognized and located in the scene by a composite matched filtering with complex constraints. Afterwards, the set of targets are tracked by adapting the matched filtering to each input image and by processing small image fragments extracted at the predicted coordinates of the targets in the scene. Results obtained with the proposed algorithm in a test image sequence are presented and analyzed in terms of efficiency of target recognition and accuracy of target tracking.

Viscosity is an important property of out-of-equilibrium systems such as active biological materials and driven non-Newtonian fluids, and for fields ranging from biomaterials to geology, energy technologies and medicine. Non-invasive viscosity measurements typically require integration times of seconds. Here, we demonstrate measurement speeds reaching 20 μs, with uncertainty dominated by thermal molecular collisions for the first time. We achieve this using the instantaneous velocity of a trapped particle in an optical tweezer. To resolve the instantaneous velocity we develop a structured-light detection system that allows particle tracking over femtometre length scales and 16-ns timescales. Our results translate viscosity from a static averaged property to one that may be dynamically tracked on the timescales of active dynamics. This opens a pathway to new discoveries in out-of-equilibrium systems, from the fast dynamics of phase transitions to energy dissipation in motor molecule stepping and to violations of fluctuation laws of equilibrium thermodynamics. A structured-light-based detection of a particle trapped in optical tweezers enables ultrafast velocity and viscosity determination.

Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is wellsuited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.

The past several years have seen new developments in tracking-integrated solar concentrators, including new concepts in optics design and tracking methods, increasing clarity regarding high-value target applications, improved performance modeling methods and the development of pre-commercial tracking-integrated concentrator photovoltaic modules. We survey these recent developments and also present preliminary measurements of a tracking-integrated concentrator photovoltaic module under outdoor conditions in Abu Dhabi, United Arab Emirates. We incorporate the data into a semi-empirical model to estimate annual energy yields and assess its technical and economic potential relative to competing technologies.

MODIS instruments have been operating over 21 and 19 years on the Terra and Aqua spacecraft, respectively. As both instruments continue to operate beyond their design lifetime of 6 years, relying on the on-board calibrators alone is proving a challenge to accurately characterize their response at all scan angles, especially for the reflective solar bands. This study presents an alternative approach that relies on Earth view responses at multiple scan angles from a single site over Dome Concordia (Dome C), Antarctica, one of the most homogeneous land surfaces uniformly covered by snow. This approach has advantages of more frequent overpasses over the entire scan angle range than the desert sites.

A key component of establishing free-space optical communication between ground stations, satellites, and lunar nodes consists of accurate pointing, tracking, and beam acquisition. This investigation explores the use of sensor arrays external to the receiver aperture for sampling the incoming transmitted beam for the purpose of estimating the pointing error. Different arrays of sensors and different combinations of sensors processed with the traditional quadrant detector algorithm were evaluated based on the ability to accurately predict the direction of pointing error under a variety of beam widths and turbulence conditions. MATLAB simulations demonstrate that different configurations perform best under different conditions, and thus a combination of configurations with flexible processing capabilities provides the most promising approach. Beam width proved to be the most important factor in prediction accuracy.

The process of cell regeneration is a field of study and analysis that has grown in recent years in the field of biology. For its study, 4D confocal microscopy images are acquired that allow the visualization of cell regeneration over time. However, the process of recognition and tracking of cells is done in many cases by manual techniques, making this task complex, biased and time consuming. In addition, the very low S/N ratio of this type of images makes it necessary to implement smoothing filters that do not affect the quality of the edges, making them more diffuse, and allowing a better detection of the number of cells over time. Although a freely available semi-automatic tracking technique has been implemented, such as the Track-Mate tool, which facilitates the user's work, it only has a median filter for the smoothing process. Therefore, this paper presents the study, development and implementation of the image smoothing methods A trous, anisotropic diffusion, bilateral, guided, enhanced propagated, K-SVD, non local means, bilateral enhanced propagated, ROF and TVL, as integrated filters within the Track-Mate tool, to analyze their behavior in practical cases of progenitor cell detection and tracking, taking as criteria the noise attenuation in an optimal way with the lowest loss of information and the highest cell count in 4D images of Parhyale hawaiensis, to find the most efficient and accurate techniques for cell tracking and, thus, improve this analysis tool, allowing the user to improve the results of the studies performed in confocal microscopy images.

Aiming at realizing the precise control of pneumatic muscle (PM) driven by high-speed on/off valves, a sliding mode control (SMC) approach based on the three-element model of PM is proposed. Firstly, the dynamic model of PM is established based on the three-element model, and the dynamic property experimental apparatus of PM is established to identify parameters of the three-element model. Secondly, the trajectory tracking control scheme of PM is established based on the SMC method. Finally, the control scheme is realized by means of the MATLAB/Simulink and the simulations of trajectory tracking control of PM is implemented. The experimental results indicate that the control accuracy and property of disturbance rejection is satisfactory, which provides an effective method for the trajectory tracking control of PM actuated with the high-speed on/off valves.

Multiple target tracking (MTT) or multiple object tracking (MOT), which is a key stage operation for many computer vision applications, has relied and it relies on detecting and identifying targets within videos. The objects of these videos may be, for instance, pedestrians, vehicles or animals regardless of the number of targets or their appearance. The main goal of multi object tracking is to follow paths or trajectories of multiple targets in a sequence. Multiple object tracking is a field of computer vision that has a wide range of applications, starting from video surveillance and human computer interaction to robotics. The aim of visual object tracking is to maintain objects’ identities. Developing an effective and accurate visual tracking system is very challenging due to the enormous number of problems, such as Illumination variation or background, the famous Clutters meaning the overlap between objects. Low resolution may be due to cameras problems, scale variation, occlusion, and change of targets’ positions within video. These challenges have a wide range of approaches and methods in this evolving research area with several algorithms in each subfield. This paper covers the crucial research area for Multiple Object Tracking (MOT) and this study will help researchers accomplish their scientific projects relying on the wide range of algorithms mentioned on this review.

Measuring the mechanical properties of living tissue is a challenging task due to the small sizes and the fragility of the living organisms. A promising method, which works best on small scales, is the passive microrheology, which observes the motion of tracing beads within the sample. The video imaging method observes this motion by imaging the tracer particles with suitable optics (e.g. a microscope). As living tissue is a complex material, the viscoelastic properties are highly frequency dependent; therefore, a fast high-speed camera is needed to resolve the important frequencies in the 100 to 1000 Hz regime. As the data rate of high-speed cameras exceed the storage speed, only short burst of measurements can be carried out. This leads to a limited dynamic range of frequencies and missed measurement opportunities. It normally is not possible to track all the particles in real time to avoid the storage requirement of the video, as the tracking needs to be very precise and thus has a high computing demand. In this presentation, a combination of a CMOS imaging sensor with an FPGA is presented, which, in combination, allows for virtually unlimited long high-speed tracking of up to eight particles at up to 10 kHz. First, the sensor and the FPGA combinations are laid out. Secondly, the used particle tracking algorithm and its implementation is explained and benchmarked with a known state-of-the-art algorithm. Finally, this integrated sensor solution is mounted on a standard microscope and hour long tracking experiments on living 3T3 fibroblasts are carried out, studying the impact of blebbistatin on the mobility of polystyrene beads within the cell.

Holographic Tomography (HT) is an emerging label-free technique for microscopic bioimaging applications, that allows reconstructing the three-dimensional (3D) refractive index (RI) distribution of biological specimens. Recently, an in-flow HT technique has been proposed in which multiple digital holograms are recorded at different viewing angles around the sample while it flows and rotates within a microfluidic channel. However, unlike conventional HT methods, there is no a priori information about cell 3D orientations, that are instead requested to perform any tomographic algorithm. Here we investigate a tracking-based rolling angles recovery method, showing robustness against the sample’s features. It is based on a phase images similarity metric recently demonstrated, that exploits the local contrast phase measurements to recognize a full cell rotation within the microfluidic channel. Hence, the orientations of the flowing cells are retrieved from their positions, which are in turn computed through the 3D holographic tracking. The performances of the rolling angles recovery method have been assessed both numerically, by simulating a 3D cell phantom, and experimentally, by reconstructing the 3D RI tomograms of two cancer cells. Both the numerical and the experimental analysis have been performed at different spatial resolutions. This rolling angles recovery method, not depending on the cell shapes, the RI contents, and the optical experimental conditions, could pave the way to the study of circulating tumor cells (CTCs) in the challenging tool of liquid biopsy.

The wireless sensor network includes a large number of intelligent sensor nodes, which can provide users with accurate information by monitoring, sensing, and collecting data related to the environment and detection objects in the network area in almost real time. With the development of wireless sensor network, it has been widely used in various fields by taking advantage of its high detection accuracy, strong flexibility and low cost. In wireless sensor networks, a single sensor node cannot solve large-scale and complex problems. For this reason, we need to connect various nodes through cooperative signals, and information processing technology based on cooperative signals has also been developed. Through resource coordination and signal coordination, it can be ensured that wireless sensor networks can carry out multiple tasks at the same time and handle large-scale and complex problems. This article focuses on the research of the wireless sensor network tracking target cooperative signal and information processing, mainly from the tracking scheme and the data observation and storage in the target tracking for analysis and discussion.

High-speed tracking of nano-objects is a gateway to understanding biological processes at the nanoscale. Here we will present our results on tracking single or ensembles of nano-objects inside optofluidic fibers via elastic light scattering. The nano-objects diffuse inside a channel of a microstructured fiber and the light scattered by the nano-object is detected transversely via a microscope. We will present the fundamentals of this approach and focus on selected results including retrieval of the full 3D trajectory of a diffusing nano-sphere, the simultaneous detection of hundreds of nano-objects in hollow core anti-resonant fibers and first results on inactivated SARS-CoV-2.

In recent years, photoacoustic imaging, as a new type of biomedical imaging method, combines the advantages of high selectivity in pure optical tissue imaging and deep penetration in pure ultrasound tissue imaging to obtain high-resolution and high-contrast tissue images. The use of photoacoustic imaging technology to deal with complex medical tissue problems is still a new research direction. How to compress large amounts of data and quickly transmit and store important value information has become a problem waiting for optimization. This paper uses the StagewiseOMP and tracking algorithm to combine it with the photoacoustic imaging of the k-wave simulation toolbox to rebuild a virtual simulation platform for blood vessel imaging. On the one hand, compressed sensing can reduce the sampling rate and speed up imaging. On the other hand, it can modify the demand for hardware equipment to facilitate data transmission and storage. A simulation model of photoacoustic field propagation, photoacoustic signal recording and image reconstruction was established using the k-wave simulation toolbox. We have used the excellent performance of the simulation platform through imaging technology to complete the imaging restoration of part of the blood area tube tissue.

In radar target tracking, knowledge of the true dynamics of target motion is paramount for accurate state estimates. In this paper, we propose a method of target maneuver detection utilizing symbolic dynamics. We demonstrate its ability to compete with other commonly used maneuver detectors. This is done through simulations performing target maneuver detection.

A novel original method is presented to detect and track laser damage sites on vacuum windows of the Laser MegaJoule (LMJ) facility. The method is based on spatial registration by Digital Image Correlation (DIC). It also involves corrections for gray level variations induced by variable lighting conditions. Using the present method, an efficient way is achieved to detect and follow laser damage sites as soon as they appear on the optical component. The developed tools offer the possibility of characterizing and predicting damage growth as a function of laser shot features.

People recognition is a relevant subset of the generic image based recognition task with many possible application areas such as security, surveillance, human-robot interaction or recently the social security in a pandemic context. In this work we present a light-weight recognition pipeline for time-of-flight cameras based on deep learning techniques tailored to this specific type of camera with registered infrared and depth images. By combining the maturity of the 2D image based recognition techniques with the custom depth sensing we achieved effective solutions for a number of relevant industrial applications. In particular, our focus was on automatic door-control and people counting applications.

Tracking the motion of held objects is becoming increasingly important in smart home applications. MicroDoppler radar has proven beneficial for tracking the motion of people, as various parts of the body, such as the arms and legs, move with different velocities as a function of time, thereby generating different Doppler frequency sidebands in the received response. The time-frequency signature of these responses can be used to classify activities. Since natural objects are generally linear, the back-scattered signals are collected at the same frequency as the transmitted signals, thus the micro-Doppler frequency sidebands are observed around the transmitted center frequency. In home settings, clutter can thus become a challenge in the detection of small movements. In this work, we demonstrate an approach for tracking held objects in high-clutter environments using harmonic Doppler and harmonic tags to detect the micro-motion signatures of held objects. While previous works have investigated harmonic radar for target tracking, this work uniquely focuses on detection of the timevarying Doppler responses from micro-motion signatures of held objects. By placing a passive harmonic tag on various parts of the human body, the motion of individual body parts and/or individual held objects can be discerned. In this work we characterize the harmonic micro-Doppler signatures of tags held on different parts of the body. We present expected results and compare them to measurements conducted using a 2.51 GHz/5.02 GHz harmonic Doppler radar

Over the past few years, Unmanned Aerial Vehicles (UAVs) have known important progress in their technology, spreading their adoption and their use in various types of applications. More recently, researchers have become more interested in the use of multiple UAVs and UAV swarms. In this work, we are interested in the use of vision-based deep learning algorithms for UAVs tracking and pursuit. The goal here is to use recent deep learning object detection, coupled with a ‘Search Area’ prediction approach, to detect and track a target UAV from images captured by another UAV. The detected position outputs the necessary controls for real-time maneuvering and tracking. The proposed architecture was tested on different simulated conditions. The approach was able to process videos at high frame rates and get a mean average precision above 90%. The obtained results show the possibility of using vision-based deep learning for detecting and tracking UAVs.