A well-organized and thorough discussion on the fundamental principles and recent progress in deterministic lateral displacement (DLD) is provided.The most updated designs and applications of DLD techniques for particle separation and detection are reviewed.The current limitations of DLD and its potential solutions for clinical and commercial applications are discussed. The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics. In this field, microfluidic deterministic lateral displacement (DLD) holds a promise due to the ability of continuous separation of particles by size, shape, deformability, and electrical properties with high resolution. DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes. DLD techniques have been previously reviewed in 2014. Since then, the field has matured as several physics of DLD have been updated, new phenomena have been discovered, and various designs have been presented to achieve a higher separation performance and throughput. Furthermore, some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection. This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection. Furthermore, current challenges and potential solutions of DLD are also discussed. We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications. In particular, the rapid, low-cost, and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics. The separation, isolation, and detection of particles in suspension are important for a wide spectrum of applications including biomedical research and clinical diagnostics. Typically, these tasks are performed by centrifugation, flow cytometry, gel electrophoresis, chromatography, etc. However, these techniques require a large volume of samples and have inevitable sample loss. Microfluidic has opened up the possibilities for miniaturizing these analytical devices through the precise control of small fluid volume on a microscale channel and has been widely used for particle manipulation such as focusing, fractionation, and sorting of micro- to nanoparticles . These mFor more than a decade, the theory of DLD has been studied and revised, new phenomena on DLD were discovered, and the various designs of DLD have been presented to achieve efficient and high-throughput particle sorting. Moreover, recent advances in DLD show its ability to sort particles based on their shapes, deformability, and electrical properties. Due to the high sensitivity of the separation with a resolution limit of 20 nm , 12, DLDHere, we will provide a comprehensive review on the fundamental and recent progress of DLD including the principle, device design, and factors influencing the critical diameter. Next, recent applications of DLD for particle separation and detection are presented. Lastly, the challenge of microfluidic DLD for various applications, its potential solutions, and the future directions are discussed.N) and suggested that the number of streamlines between each pillar corresponds to the periodicity of the DLD array. Particle flows in the DLD array are influenced by both the fluidic forces and the pillar obstacles effect. When the particle is located in the pillar gap, the particle with a radius smaller than the first width of the streamline will follow the initial streamline and travel in the zigzag mode, while the particle larger than the first streamline width will be bumped to the pillar and displace laterally to the next streamline as seen in Fig. Dc). To develop the DLD array with a desired critical diameter size, there are multiple design parameters to be considered on the DLD unit cell, which comprise the lateral and downstream pillar gap , the row shift fraction (D0) as illustrated in Fig. Microfluidic DLD uses tilted pillar arrays that generate a fluid bifurcation and a unique number of streamlines between the gaps. Initially, Huang et al.  discoverentclass1pt{minimaentclass1pt{minimaThe understanding of the critical diameter is essential to design a DLD device for desired applications. Hence, several theoretical and empirical models to predict the critical diameter have been proposed. Inglis et al.  developeThis model almost predicts the particle displacement between the zigzag and bumping mode, but it still underestimates the experimental critical diameter value from Davis et al. , who tesy effect , 26–29.Np is the structural periodicity, DL is the distance between pillars in the lateral direction, and DD is the distance between pillars in the downstream direction. If the pseudoperiodicity is larger than the structural periodicity, the resulting zigzag migration angle is more than zero, which is called the altered zigzag mode that occurs solely due to the fluidic streamline effect and not the particle–pillar interaction. Furthermore, this model also explains the influence of the pillar diameter on the particle separation. All else equal, the increase in the ratio of the pillar diameter to the downstream distance (D0/DD) increases the local periodicity of each cycle that leads to a higher migration angle. The nanoparticle separation experiments conducted in the nanoscale DLD with various design parameters support this DLD theory [Kim et al.  proposedD theory –34.Microfluidic DLD can be designed with different device layouts and array arrangements for binary or multiple particle size separations, which have been implemented for different applications. Generally, this DLD device design can be classified to single or multiple critical diameter design.Dc layout design is a mirrored concentrator array with one to three inlets. This device comprises two-mirrored pillar arrays that are typically separated by the center wall as seen in Fig. The single critical diameter design separates particles in a binary fashion, which is based on a single uniform arrays design parameter along the entire channel. The layout for this DLD design typically requires three inlet reservoirs that comprise a sample inlet and two buffer inlets, with the sample inlet being sandwiched between the buffer inlets as seen in Fig. Dc within the device. The multiple critical diameter design comprises a condenser and sorter region, which is able to concentrate the polydisperse particles in the condenser region and subsequently separate them in the sorter design  along the channel, with the observed highest peak of Dc1 on the wall area without the side pillars and lowest peak of Dc2 on the wall area with the side pillars as seen in Fig. Nc) across the channel width. As the Nc increases, the periodic variation in the cutoff diameter is reduced and the values of Dc1 and Dc2 are getting similar, which result in the reduced intermediate mode as seen in Fig. Without the sidewall correction, there exists an intermediate mode of particle trajectory displacement at the sidewall, which is comprised of the zigzag mode in the area next to the wall without the side pillars and gradually changes to a displacement mode at the area with the side pillars as seen in Fig. Wi = λu/DL, u is the average fluid velocity and λ is the shear relaxation time). The application of high flow rate increases the particle effective diameter due to the effect of the normal stresses that manipulate the particle trajectory to focus on the center of the channel as seen in Fig. Typically, DLD particle separation uses the Newtonian fluid as the buffer medium. The effect of a non-Newtonian fluid buffer medium on the DLD critical diameter has been studied theoretically, which suggests that the critical diameter can be tuned by using different polymer concentrations in the viscoelastic fluid buffer due to the fluid shear-thinning effect . The cha−1 range due to the high resistance of the pillar array. With the application of higher Reynolds number in DLD, the inertial forces become more dominant, which can change the fluidic streamline and eventually develop the microvortices behind the pillar structure [Re = 0.1, but start to form the microvortices behind the pillar structure at Re = 25, which fully mature at Re = 50. The microvortices then stop to develop after Re = 75 as seen in Fig. DLD is commonly operated at the Reynolds number of less than 1 with a flow rate in the µL mintructure . The alttructure . The simParticle concentration influences the particle separation in DLD because of the particle–particle interaction effect. Most of the reported DLD separation studies typically use a diluted sample concentration to deal with the particle crowding. This is because the non-dilute particle concentration may result in the disruption of the separation, which is mainly due to two reasons. First, the high particle concentration results in the particle collision that has a non-deterministic nature and the non-diluted concentration can also disrupt the streamline of the surrounding fluid, which changes the critical diameter of the separation , 53. TheL is the width of the channel, and k is the Boltzmann constant, T is the absolute temperature, a is the hydrodynamic radius of the particle. Generally, the particles travel without a significant diffusion if DLD is a passive microfluidic technique that does not rely on the external fields to overcome the diffusion of the particle. The presence of particle diffusion reduces the separation efficiency and precision, which is especially problematic for submicron particle separation . The dififfusion . Huang eiffusion . The expiffusion . Severaliffusion , 60. Heliffusion . The simiffusion .Fig. 8a s) can attain values that are orders of magnitude than the bare particle diffusivity [The consideration of both deterministic motion and Brownian fluctuations at the microscale should not be ignored when dealing with the prediction of separation resolution. Cerbelli et al.  proposedfusivity . By utilfusivity .The particle shape and deformability also determine the DLD separation performance since the critical diameter size of the non-spherical particle depends on its orientation in the lateral gap. This property is essential since biological particles range from spherical to non-spherical shapes as well as rigid to deformable membranes such as red blood cells and bacteria. Some bacteria have rod shapes, and the orientation of the bacteria in the pillar gap decides the trajectory in DLD arrays . SimilarDp) with the electric double-layer force length (dF-EDL) to become the apparent diameter Dapp as depicted in Fig. The separation of small particles in DLD is becoming more complex due to the emergence of the surface forces acting on the particles that can change the critical diameter . Zeming dF-EDL as seen in Fig. Vbulk is the relative velocity of the surrounding fluid. Based on this formula, it has been shown that the particle apparent diameter is enhanced for more than 150 nm on the oxygen plasma-activated PDMS surface as compared to the native PDMS due to the stronger electrostatic force in a highly negative charge of SiO− on the plasma-activated PDMS device [3, are shown to have a different apparent diameter of the separation in DLD, even though the actual sizes of the beads are the same.It has been shown that the DLD lateral displacement can be enhanced using the low ionic strength due to the larger electrostatic repulsion between the particle and pillars, which leads to a larger apparent diameter, while the lateral displacement is reduced at a high ionic concentration buffer due to the electrostatic shielding as seen in Fig. The first report on the modulation of the critical diameter using external forces was achieved by using the elastomeric stretching of the PDMS-based DLD device by a micrometer precision screw . The mecDc of 6 µm to 250 nm). Although this method provides a high dynamic range of the critical diameter tuning and improves the separation throughput due to the larger pillar gap, it requires additional external electrodes and AC electric field source with a high electric field strength that increases the complexity of the DLD experimental setup.Another method that has been proposed to change the critical diameter is using AC external electric fields to generate an insulator-based dielectrophoresis (iDEP) force. This iDEP is based on the electric field deformation by the pillar array between the electrodes located at both ends of the channel  or across the channel width  . The nonThe modulation of the critical diameter in DLD can also be performed with the external driving force that can control the forcing angle of the particle, virtually changing the row shift fraction of the pillar arrays. Several different external fields including gravity, centrifugation, and electrokinetic forces have been employed to change the critical diameter through the modulation of the forcing angle as seen in Fig. With the development of DLD technology, there are multiple modes of DLD applications that are mostly demonstrated for medical and biological research applications, including the particle separation, concentration, buffer exchange, and label-free detection. Initially, DLD uses the particle size parameter for the basis of the separation. Recent progress in DLD shows that several additional particle properties including shape, deformability, and electrical property can be used as the separation parameters. The recent applications of DLD and their corresponding designs and setups are summarized in Table In the size-based separation, the particles with different sizes have different trajectories and migration angles. This separation mode is the most widely implemented method and has been used for the separation and concentration of micro- to nanoparticles, including synthetic beads, droplets, and various biological samples like blood cells, bacteria, exosomes. Other modes of applications that rely on the size-based separation such as buffer exchange and on-chip washing have also been reported.One famous contribution of DLD separation is in the development of hematology tools. DLD brings the complex and expensive fractionation tools for blood cells to an affordable and efficient on-chip separation. For example, inexpensive plastic DLD microchips can directly separate leukocytes without any other manual handling of samples with a recovery rate of 88% of input leukocytes as seen in Fig. Besides the blood cells separation, DLD technology has a great contribution in cancer research, especially for the isolation of circulating tumor cells (CTC) . Au et aDLD has been popular for separation of microparticles, but there are only a few reports available for the nanoparticle separation. The separation of DNA, protein, and exosomes is currently still challenging due to the nanoscale size of the molecules. With a microfabricated DLD array, Santana et al.  designed−1.With the advances in the nanofabrication, DLD now can sort and enrich nanoparticles precisely and efficiently based on their size. Wunsch et al.  translatThe shape or morphology of particles influences the particle trajectory in DLD due to the dynamic of the particle orientation on the pillar array. Jiang et al.  reportedMost of the samples that are handled by DLD are biological samples, which are typically soft particles with deformable shapes, especially red blood cells. While the DLD model provides a high accuracy for predicting rigid spherical particle separation, the critical diameter of the deformable particles is difficult to be predicted by DLD empirical formula as the deformation of the particles leads to the smaller effective radius of the cells due to the flow shear as seen in Fig. Due to the change in the deformability of red blood cells induced by some diseases such as malaria which increases the cell stiffness, it is possible to separate and detect the diseased cells from the healthy cells. Kruger et al.  showed v3, as well as albumin-coated beads have different lateral displacement extents and apparent diameters in DLD [DLD can be used for the separation of particles based on their electrical properties, including the dielectric properties and charges. The dielectric property of the particle can be employed for the separation parameter in DLD with the use of the electric field to generate the dielectrophoresis (DEP) force. In a non-uniform electric field, the particle with less polarizability than the surrounding medium results in a negative DEP, which pushes the particle toward the region of low electric fields. Hence, the DLD combined with the DEP force could separate particles based on the particle polarizability parameter , 72. Alts in DLD .−1 or 150 pM of albumin protein concentration. On the other hand, the size-dominant detection employs the size-based displacement from the coating of nanobioparticles such as vesicles or exosomes that have a larger size of more than 50 nm. This is because the coating of the nanobioparticles could increase the size of the 1-µm bead to the size range of 1.05 to 1.4 µm. The polymer vesicle detection in DLD has been demonstrated based on the difference in the lateral displacement position of the vesicle-coated microbeads as compared to the control beads with a limit of detection of 3.75 µg mL−1. This label-free detection method provides a fast, inexpensive, and real-time detection of nanobioparticles with only requiring a standard laboratory microscope for measuring the lateral displacement of the beads on the outlet channel.DLD has been established and widely used as a label-free particle separation platform. Recently, a fluorescent label-free detection platform based on the DLD pillar array with a bead-based nanobioparticle coating method was reported. This method is performed by correlating the lateral shifts of the nanobioparticle-coated microbeads with the concentration of bioparticles bounded on the microbeads . There aDLD technology has been developed for more than a decade since its conception in 2004. Although DLD has been shown to be a versatile particle separation method with a high resolution for various applications, there are several limitations of using DLD, mainly due to the low throughput, pillar clogging issue, and bulky DLD experimental setup. These limitations have been continuously addressed to bring DLD as an ideal analytical tool for commercial applications.−1 [As DLD arrays use many pillar structures, the fluid resistance of the microfluidic DLD is comparatively higher compared to other microfluidics techniques, which limits the application of DLD for large sample volume separations. Several methods have been reported to improve the throughput rate. The use of sparse pillar arrays with only several pillar structures and the sieve-based lateral displacement is able to improve the throughput rate in DLD, but it requires an additional adjustment on the array design to balance the pressure to prevent the disruption of the particle separation –97. Stac−1 . The fab−1 in less than 45 min without clotting [Another challenge of DLD is the particle clogging issue due to the presence of multiple pillar arrays with the small gap between the pillars. The clogging could result in the inefficiency of the separation due to the disruption of the streamline around the clogged area that changes the critical diameter of the separation . To prevclotting . Anotherclotting . The bubclotting , 103. Toclotting , 104.DLD has been demonstrated for separation of parasites from blood, diseased RBCs, bacteria, exosomes, etc. However, there is a gap between the experiments of microfluidic DLD in the laboratory with the application in the field. Although the DLD chip designs typically fit on the size of a microscope glass slide and can be mass-produced at a low cost, they still require expensive, bulky, and high power consumption pressure controls to pump the fluid, laboratory microscopes for imaging, and computer systems for data analysis. These requirements hinder the implementation of DLD outside the laboratory settings for point-of-care applications in a remote area. Therefore, the simplification of the DLD setup is required to create a portable, less power consumption, and low-cost solution for point-of-care diagnostics in the low-resource setting. Several researchers have addressed this limitation by developing a portable pump for microfluidic DLD. Holm et al.  developeDLD has been widely used for particle separation due to its low cost, simplicity, robustness, and precise manipulation of critical diameter to separate particles based on their size, shape, deformability, and charge. Since its conception, there are various modifications on DLD designs, including the pillar size, shape, gap size, the variation in channel width, and device layout to improve the separation efficiency and throughput. Several methods to tune the critical diameters have also been demonstrated for various particle separations. Furthermore, the electrostatic phenomena in DLD provide new opportunities for nanoparticle separation and nanobioparticle detection in a microfabricated array, while the true nanoscale DLD array has been reported for exosomes isolation and DNA enrichment with high efficiency. DLD has also been applied for the high-throughput particle separation with a mirrored, highly sparse, parallel, and asymmetrical gap design to support a large-volume sample processing. DLD technology is likely to be further explored for various medical applications including stem cells research and biomolecules  separation and detection. As the limitations of DLD such as throughput and clogging issue have been addressed, DLD is considered to be a mature technology that can be implemented for the real-world application. However, the industrial applications based on DLD separation are still in an early stage. Recently, microfluidic DLD has been fabricated with plastic materials that have the potential to be mass-produced. With plastic-based microfluidics, commercialization of the DLD technology has been initiated for T cells isolation from the leukapheresis sample for front-end cell therapy manufacturing. Similarly, the commercialization of nanoscale DLD requires the adoption of more affordable materials and manufacturing cost. DLD technology is predicted to evolve toward the compact point-of-care testing (POCT) device for medical applications. To accelerate the POCT applications, the integration of both particle separation and further downstream analysis including particle detection needs to be established. Furthermore, the simplification of the setup including the portable pump and detection systems needs to be further explored to achieve a compact and portable solution. With this outlook, the rapid and low-cost particle separation and detection using microfluidic DLD have tremendous potential impact for point-of-care diagnostics in the future.


The long-term outcomes of osteosarcoma have improved; however, patients with metastases, recurrence or axial disease continue to have a poor prognosis. Computer navigation in surgery is becoming ever more commonplace, and the proposed advantages, including precision during surgery, is particularly applicable to the field of orthopaedic oncology and challenging areas such as the axial skeleton. Within this article, we provide an overview of the field of computer navigation and computer-assisted tumour surgery (CATS), in particular its relevance to the surgical management of osteosarcoma. Osteosarcoma (OS) is defined by neoplasms that have the histological characteristics of producing an osteoid and being associated with malignant mesenchymal cells . There aThe overall incidence of OS is 0.2–3/100,000 per year in Europe and its most common anatomical sites are the distal femur, proximal tibia and proximal humerus ,5,6. TenSurvivorship of OS in extremity localised, non-metastatic disease is 60–70%. However, in metastatic disease, the 5-year overall survival drops to 28–33% . CompletDue to the challenges of the surrounding anatomy and the complex three-dimensional structure of the pelvis, a complete resection in the pelvis can be more challenging than in the appendicular skeleton. It has been reported that the probability of an experienced surgeon achieving a 1 cm surgical margin in all three planes on a simulated tumour model of the pelvis was only 52% . The seqThe uptake in the use of computer navigation in the surgical management of OS is in response to these factors, in a bid to minimise the incidence of inadequate resection margins.Computer-navigated surgery has been used in neurosurgery since the 1980s, where improved accuracy in resections for cancer was achieved by mapping brain tumours pre-operatively and using this to plan surgical resection. It is only at the turn of the 21st century that it has been adopted within the orthopaedic community ,17, prinComputer navigation encompasses all techniques using computing to augment surgical procedures. The two main types of navigation currently used in orthopaedic surgery are “image-based” and “patient-specific instrumentation and reconstruction”.In recent years, CATS has been developed along with 3D printing technology, which represents the possibility to personalize reconstruction with custom-made prostheses ,19,20 anManufacturing of 3D-printed PSI jigs and custom-made prostheses is based upon the principle of rapid prototyping (RP). RP is a process that directly produces a physical object with a defined structure and shape on the basis of virtual/mathematical model data. Rapid prototyping was first used in the late 1980s and was developed to apply the precision and functionality of computer-assisted design (CAD) to manufacturing. With this technology, a prototype could be quickly produced and accurately represents the engineers draft ,24. ElecTraditional methods of manufacturing orthopaedic implants used subtractive machining, where the material is removed from a metal block until the planned shape has been achieved. The process of RP is different to this and uses the process of additive manufacturing. This is when a construct is made based upon a digital model by plastic or metal being deposited in layers. This provides versatility when constructing complex geometric shapes. Changes in design require no new equipment; therefore, it is ideally suited to low volume patient-specific instrumentation or implants ,26,27.Anatomical models.Patient-specific instrumentation (PSI).Custom-made implants and devices.In surgery, 3D printing is utilised in the production of the following ,27:AnatoAnatomical model fabrication is the most common use of 3D printing in surgery.In the planning stage, 3D models are used to represent the anatomy, simulate surgical procedure and test surgical tools . StudyinPSI technology is the most popular medical application of 3D printing and has been developed as an alternative to intraoperative navigation. PSI is designed on a 3D model based on CT and MRI fusion according to the surgical approach, desired resection margins and reconstruction method. The unique anatomy of the patient and the PSI shape allows to place the guide only in a pre-defined position. These contact surfaces must be defined by both surgeon and engineer, considering the surgical approach, the bone exposure and tumour extension. These features make the application of PSI more useful for bone deformities and tumours where the anatomy is often abnormal. The final PSI is commonly printed in nylon or polyamide and provided to the surgeon sterile or to be sterilized . Thus, tLimb salvage surgery has become the rule for most patients with bone tumours. However, resection of these tumours needs to achieve wide margins to limit local recurrence and at the same time preserve function ,33. In pThe performance and accuracy of PSI has been demonstrated; it improves alignment and surgical and operating theatre times ,41,42,43Custom-made implants have been used since the 1950s, when a complex reconstruction was performed in a limb salvage procedure . The phyMany studies have shown that the porosity of titanium is one of the most relevant features influencing bone ingrowth; Frosch et al. has demonstrated that 300–600 µm is the better porosity for osteoconduction .A more recent development is a particular configuration of titanium called trabecular titanium; the main advantage of the trabecular structure is the rough surface that amplifies the contact with host bone, increasing the integration.A good resolution of pre-operative images is needed in order to have the most accurate representation of the patient-specific anatomy and pathology. CT and MRI scans are essential to approach a complex bone tumour surgery.A 3D bone-tumour model was created to allow the surgeon to virtually perform the bone resection cuts of the tumour and then to design the cutting guides as well as prosthesis.To obtain the 3D model, DICOM files  were loaded into dedicated CAD software (computer-aided design) that allows segmentation and creation of STL files (surface triangulation language).In this phase, cooperation is needed between the surgeon and engineer in merging the MRI–CT scan images, selecting the tumour/defect area and setting the osteotomy planes.The STL file can be sent to the 3D printer (or the company that provides the guides or implants) and is the key to achieve the tri-dimensional model.Patient-specific instruments (PSIs) and implants are designed according to the planned resection strategy. The virtual custom prosthesis is created providing optimal fixation and reconstruction of the local osseous anatomy.The prosthesis is provided with tools to allow fixation to the host bone as well as plates or rods, while the screw positions and lengths were previously planned as well.For articular reconstruction, a surface layer  can be integrated in the titanium porous structure.The implant is fabricated through the deposition of few micron layers of titanium powder melted by electron beam technology, as previously described. The implant’s porosity is widely variable in order to increase its performance, such as the macro-porosity of body, to lighten the implant, till micro-porosity at the bone surface contact. These surfaces have pores with an average size of 0.7 millimetres, allowing the host bone to grow directly inside the implant spaces to achieve a stable biological fixation A,B.The major issue of the entire process is the absence of testing of the biomechanical properties. Due to the customization, the product is unique, and validation of the implant is up to the surgeon .-The therapeutic object; the target of treatment, e.g., OS of the distal femur.-The virtual object; the virtual representation of the patients’ anatomy involved in the surgery.-The navigator link; the connection between the two abovementioned objects.In “image-based” computer-assisted tumour surgery there are three principal areas :-The thePreoperative Navigation Planning. As per PSI planning, CT provides excellent visualisation of osseous anatomy and MRI provides supplementary information regarding soft tissue anatomy. These can be overlaid, combining the strengths of both imaging modalities. Further techniques, such as PET, can be used to help differentiate between malignant and benign tissues and assist in deciding upon resection sites. Once the resection levels have been planned, reconstruction can then be decided upon, whether that be allograft reconstruction or a custom prosthesis [Wong  has descosthesis .Intraoperative Execution. Most commonly in surgery for osteosarcoma, this is done manually using a navigation link. Predefined points on the cross-sectional imaging are matched with the patients’ anatomy. The more points detected the higher the accuracy. Any straight instrument with a pointed tip can be calibrated and tracked with navigation. Once the therapeutic and virtual object have been calibrated, bone resections using instruments such as osteotomes or burrs can then be performed [The second stage of CATS is erformed .Postoperative Validation; this being validation of the resection performed. Clinical methods for achieving this in an expeditious manner include frozen sections, and due to the dynamic reference markers still being in place once the resection has been completed, one can thus ensure that the resection was completed as planned [The final stage of CATS is  planned  (Figure Bosma et al.  performeThe implementation of CATS in the surgical management of OS has been reported in both the appendicular and axial skeleton. However, due to the unique anatomical challenges of the pelvis, it has gained the most traction in this area.It is known that surgical margins do predict local recurrence in osteosarcoma ,15. A reAbraham reports a series of 23 patients with all cases having a negative bone resection margin but in two patients there was a positive soft tissue margin . This isMargins of resection using PSI were studied in a comparative series by Docquier et al. . Nine coIn the management of pelvic OS, CATS has a role in reconstruction as well as resection. Complication rates in the endoprosthetic reconstruction of pelvic sarcoma is known to be high, with early reports quoting rates of 58.1% . PrecisiGuo et al.  reports Reconstruction of the appendicular skeleton post OS excision can be with either a prosthesis, allograft or autograft. Wong  has publFarfall  presentsThe work of Li et al.  further Due to the requirement for a link between the therapeutic and virtual object to be made by the surgeon in navigated surgery, inaccuracies in this step can lead to inaccuracy in resection . This riPatient-specific instrumentation does not account for the soft tissue extent of the tumour and, as this is a static, predetermined resection, there is no scope for altering the planning resection intraoperatively unless the PSI jig is discarded. Soft tissues and the soft tissue extent of the tumour may also impact on the jig, sitting correctly on bony landmarks and thereby potentially compromising the resection. Finally, due to the delay between the planning and jig manufacture, there is a possibility for disease progression and therefore a mismatch between the planned resection and tumour margins, resulting in intralesional resections .Surgeon error is also possible, and misplacement of the jigs can again result in intralesional resections . FinallyWith the adoption of any new therapeutic technique there is a learning curve attached to it. Computer navigation takes time both preoperatively for planning purposes and also intraoperatively for tracker insertion and registration. Mahendra et al.  describeComputer-assisted tumour surgery offers an innovation that can improve the accuracy of resection in osteosarcoma surgery and thus reduce the risk of recurrence. This is of particular use with complex resection and reconstructions, such as those in the pelvis and periarticular resections of the appendicular skeleton (see Although at present the cost of this technology is high , with tiCurrently published evidence principally relies on case series and basic science; therefore, going forwards, a more robust analysis of this technology and its application to the management of the surgical management of osteosarcoma is necessary.


Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation. Deep learning methods at present are playing an indispensable role in the field of computer vision. The relevant fruitful achievements also facilitate and change the fashion of synergy between clinical diagnosis and computerized assistance. The scope of application covers the demands of computer-guided pathological inspection ,2,3,4, bHowever, for pursuing the performance of model prediction, continuing to increase model depths and kernel numbers would become the burden of hardware architecture in the points of view of power control and the maintenance of computational complexity. For instance, the consumption of GPU memory and the computational complexity are all proportional to the size and number of convolutional kernels. Large numbers of parameters in training procedures also challenge the memory sizes of GPUs, the design of algorithmic schemes, and power requirements . AdditioTherefore, inspired by the merits of the physical framework of DDFT and also considering the advantages of deep learning methods, we introduced the Fermi–Dirac distribution to be a correction function in the following procedures. By employing the theoretical scheme of DDFT, we mapped the three-dimensional image space into a noninteracting physical system and treated each image voxel as a physical particle. Under this condition, the Fermi–Dirac distribution was used to analyze the particle behavior and classify these particle clusters according to their intrinsic energies. Thus, the particle clusters formed a specific morphological structure according to their energy property in the physical system. The estimates were then mapped back to the image space, and the image voxels were decomposed by considering their morphological heterogeneity. In the article, we compared the performance of the Fermi–Dirac correction function in Unet-based architectures. The experimental results validated the superiority of the proposed Fermi–Dirac correction function under the purpose of computational complexity reduction.For fair comparisons, we employed the open datasets from the multimodal brain tumor segmentation challenge (BraTS) 2019 ,45 to teAccording to the theoretical framework of DDFT, an arbitrary high-dimensional image space can be isomorphically mapped into a pseudo-physical space with the same dimensionality once the data length is fixed . Then, iUnder the DDFT framework, the pseudo-physical space is spanned onto a noninteracting (or weakly interacting) system so that we can establish required conditions by considering each cluster to be a subsystem and each particle (mapped from a voxel in the image space) to be in a single-particle state. Additionally, as the locations and the corresponding intensity distribution of voxels within an image space are all fixed, there is no interaction between voxels, and this space can be treated as a stationary and “frozen” system. In other words, the clusters will not exchange particles and energy with their surroundings in the noninteracting system; thus, we can exploit an additional condition of zero temperature into this system. Thus, the probability of observing the number of particles The grand potential Then, the corresponding particle distribution functions for each particle type can be expressed formally:The expressions of these distributions are the well-known Fermi–Dirac (FD) distribution and Bose–Einstein (BE) distribution, respectively. Equation (4) lists their representations in our proposed pseudo-physical system. It should be emphasized that the We found an interesting phenomenon by simultaneously comparing the theoretical form of the FD distribution to that of the sigmoid function and the z-score normalization. The coarse form of the FD distribution is exactly the sigmoid function, so the pseudo-particle distribution will be squeezed into the range of As the bosons are allowed to have the same energy value, it would lead to a consequence that all of the voxels are classified in the same cluster and become indistinguishable. On the other hand, the intrinsic mathematical form of the BE distribution also causes itself to be an infinite value while the voxel intensity approaches its mean value; thus, these inconvenient properties make the BE distribution hardly a correction function for data normalization and data filtering in the data processing. A similar situation would happen to the MB distribution. Additionally, it is also impracticable to estimate the mean intensity of each subsystem technically due to the undesired computational complexity; thus, we utilized the global mean intensity of the whole system to conquer this issue. Therefore, by considering both the theoretical and the technical merits, the FD distribution is more suitable as a correction function in the data preprocessing procedures.To test the performance of the proposed FD correction function, we employed the conventional preprocessing methods, a z-score normalization function, a Gamma correction function, and a three-dimensional (3D) global histogram equalization function , for theThus, five preprocessing scenarios were employed for the performance comparison under a specific neural network model. To achieve this purpose, we utilized the D-Unet-based structure for the brain tumor image segmentation. The number ng. Ref.  offers tAll of the experiments were executed with the hardware specification of i9-9980XE CPU @ 3 GHz, 18 cores, and one GPU of NVIDIA GeForce RTX 2080 Ti. By comparing to the results of other preprocessing methods, all of the proposed FD-type correction functions can remove the insignificant components of the brain tissue images, especially for the cases of T2 and T2-FLAIR images. Meanwhile, the The performance comparison of all preprocessing scenarios is listed in The factors TP, TN, FP, and FN are the true positive, true negative, false positive, and false negative, respectively. The total summation of these four factors is around 812,535 with a small residual error. Then, the total number of tumors is defined as Thus, we further compared the performance between the 3D global histogram equalization function and the proposed FD-type correction functions, wherein the tumor labels were categorized into WT, TC, and ET in detail. The output channels of the D-Unet became three branches. All types of image sets were processed with the 3D global histogram equalization function and the proposed FD-type correction functions, and the preprocessed images are illustrated in We theoretically reconstructed the Fermi–Dirac distribution function to be a correction function for brain tumor image segmentation. The skeleton form and the kernel of the proposed Fermi–Dirac correction function are respectively a sigmoid function and a z-score normalization function. Thus, the Fermi–Dirac correction function inherits the merits of these functions. It is suitable for dealing with intensity normalization and filtering insignificant image components simultaneously. We also explained the mathematical inconvenience of the Bose–Einstein and Maxwell–Boltzmann distribution functions in imaging processing.We then compared the performance of the proposed correction function with other conventional preprocessing methods, such as the standard z-score normalization, the Gamma correction, and the three-dimensional global histogram equalization. We also changed the normalization factor of the kernel to inspect the mathematical properties of the proposed Fermi–Dirac correction functions. The experimental consequences validated that the proposed correction function can have better computational cost than those of conventional and supervised methods. Then, the proposed correction function would inhibit the insignificant image components and enhance the filtered image segments. As the experimental results also showed that the three-dimensional global histogram equalization can offer a similar computational capability as the proposed correction method, we further fed those methods into a complete comparison. The experimental results validate the superiority of the proposed Fermi–Dirac correction function.In further performance comparison, the proposed Fermi–Dirac correction function exhibits its superior computational and recognizing capability for the brain tumor image segmentation with the multimodal types of ET, TC, and WT. Therefore, the proposed Fermi–Dirac correction function can not only reduce the computational complexity but also reinforce the image component recognition and segmentation.


K of the VMD is obtained by analyzing the EMD results. Then, the original signal is decomposed by VMD based on the number of IMFs K, and the Mutual Information Entropy (MIE) between IMFs is calculated to identify the noise dominant component and the information dominant component. Next, the noise dominant component is denoised by WTD. Finally, the denoised noise dominant component and all information dominant components are reconstructed to obtain the denoised signal. In this paper, a comprehensive comparative analysis of EMD, Ensemble Empirical Mode Decomposition (EEMD), Complementary Empirical Mode Decomposition (CEEMD), EMD-WTD, Empirical Wavelet Transform (EWT), WTD, VMD, and VMD-WTD is carried out, and the denoising performance of the various methods is evaluated from four perspectives. The experimental results show that the hybrid algorithm proposed in this paper has a better denoising effect than traditional methods and can effectively separate noise and clear signals. The proposed denoising algorithm is shown to be able to effectively recognize different cast speeds.The continuous casting process is a continuous, complex phase transition process. The noise components of the continuous casting process are complex, the model is difficult to establish, and it is difficult to separate the noise and clear signals effectively. Owing to these demerits, a hybrid algorithm combining Variational Mode Decomposition (VMD) and Wavelet Threshold denoising (WTD) is proposed, which involves multiscale resolution and adaptive features. First of all, the original signal is decomposed into several Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD), and the model parameter  In the modern steel industry, efficient continuous casting technologies, including key continuous casting equipment and new processes, have become the core technology. The continuous casting process is a complex and continuous phase change process. There are many factors that affect the quality of slabs. The research on the core technologies in the high-quality steel continuous casting process is mainly focused on the precision of the mold, the sector, and the composition control of molten steel .The mold is referred to as the heart of the continuous casting machine. The mold level control is the basis of stable production operation to avoid break out and steel overflow at the mold top. The fluctuation of the mold level is disturbed by various factors and has negative characteristics of nonlinearity, variation over time, and uncertainty. These include time-varying and nonlinear disturbances in production operations such as abrupt changes in casting speed and the slide gate owing to wear and clogging.Precise mold level monitoring is regarded as the key to improving continuous casting production quality as shown in In order to maintain the stability of the mold level, scholars have conducted much research on mold level control. The main methods include proportion integration differentiation (PID) control, fuzzy control, and adaptive control. Michel Dussud et al. developed a fuzzy controller based on expert knowledge for process control when the crystallizer level is disturbed . T. HeskIn the continuous casting production process, accurate modeling of process parameters usually requires noise-free data. However, due to the fact that the process of data collection and transmission is subject to various noises, accurate data cannot be obtained. As a result, an accurate system model is difficult to establish.In recent years, many signal processing methods have been applied to the signal denoising process. Many scholars have done much research on signal denoising. JS Smith presented the results of applying local mean decomposition (LMD) to a scalp electroencephalogram (EEG) visual perception dataset. The analysis suggests that there is a statistically significant difference between the theta phase concentrations of perception and no perception EEG data . M.G. FrRecent studies have shown that although there are many methods in the field of signal denoising processing, none of them are applicable to all data. The wavelet-based signal denoising method is widely used, but the wavelet denoising method is limited by the selection of the wavelet basis, which affects the generalization ability of wavelet denoising. Although the EMD-based method is widely used for the adaptability of its decomposition, the EMD method has serious mode aliasing and boundary effects, which seriously affect the signal decomposition. Particularly, in the process of noise reduction processing, the EMD method often directly removes high frequency components, resulting in the loss of valid information. The signal denoising technique based on the VMD method has been widely used in recent years. Compared with the EMD method, VMD effectively avoids mode aliasing and boundary effects and can adaptively split the frequency domain of the signal and effectively separate the components. The VMD-WTD hybrid algorithm combines the advantages of VMD and WTD. This method avoids modal aliasing and boundary effects and also has multiresolution and good time–frequency locality.K of the VMD is determined. Then, the mold level signal is decomposed into K IMFs by VMD, and analysis of the MIE between IMFs is used to identify the noise dominant components and the information dominant components. Next, in order to avoid the loss of effective information, the noise dominant components are denoised by the WTD, and the effective information is properly retained. Finally, all information components and the denoised noise dominant components are reconstructed to obtain the denoised signal. The remainder of this paper is organized as follows. The second section introduces the VMD algorithm and MIE. The third section introduces the VMD-WTD algorithm. This paper focuses on the application of the hybrid algorithm to signal denoising and applies it to the denoising of a mold level to ensure the quality of mold level data. First, the mold level signal is decomposed into several Intrinsic Mode Functions (IMFs) by EMD, and by analysis of the Mutual Information Entropy (MIE) between IMFs, the mode number kϕ(t) is a nondecreasing function, kA(t) is the instantaneous amplitude of ku(t), and kA(t) ≥ 0. kω(t) = kϕ’(t), which is the instantaneous frequency of ku(t).VMD is a new signal decomposition estimation method based on classical Wiener filtering, Hilbert transform, and mixing. This method determines the frequency center and bandwidth of each decomposition component by iteratively searching for the optimal solution of the variational model, and it can also decompose the signal into components with sparsity characteristics adaptively . The VMDt − δ, t + δ], ku(t) can be regarded as a harmonic signal with amplitude kA(t) and frequency ωk(t), and In the interval range of [The variational constraint model is as followsα is the penalty factor and λ is the Lagrange multiplier. We introduce the Lagrange function asThe problem of solving the original minimum value can be transformed into the saddle point of the extended Lagrange expression by the alternating direction method, which is the optimal solution of the above formula.K IMFs.Therefore, the original signal can be decomposed into Algorithm 1 The calculation process of the VMDn to zero;Step 1: Initialize n = n + 1, execute the entire loop;Step 2: k = k + 1 until k = K, update Step 3: Execute the loop k = k + 1, until k = K, update Step 4: Execute the loop λ;Step 5: Use K IMFs are obtained.Step 6: Given the discrimination condition ε > 0, if the iteration stop condition is satisfied, all the cycles are stopped and the result is output; The calculation process of the VMD Algorithm 1 is listed as follows.ku, the Hilbert transform is applied to calculate the correlation analysis signal in order to obtain a single sideband spectrum. (2) For each modality, the center frequency is estimated separately by mixed exponential tuning, and the modal frequency spectrum is transferred to the baseband. (3) The bandwidth is estimated by demodulating the H Gaussian smoothness of the signal.When obtaining the IMFs, the VMD method gets rid of the iterative screening process used by the EMD method. Instead, it processes the signal through the variational model principle and solves the adaptive decomposition of the signal by solving the optimal solution of the constrained variational model. In order to take the bandwidth of the modal function into account, the following provisions are required. (1) For each intrinsic mode function s(t) is as followsnr(t) represents the residual, representing the average trend of the signal.After VMD decomposition, the mold level signal p is the joint probability distribution; p(ix) and p(iy) are the edge probability distributions; X and Y represent different IMFs; and r and s represent the number of symbols of X and Y.MIE is used to measure the statistical dependence between two random variables. The expression is as followsK is the serial number of the high frequency and low frequency components decomposed into IMFs.We assume that the high frequency component is noise interference and the low frequency component is a valid signal. The MIE between the IMFs can be used to identify the boundary between the high frequency and low frequency IMFs. The IMF characteristics obtained by the VMD show that the dependence of the high frequency noise on each IMF is gradually reduced, and the dependence of the low frequency effective signal on each IMF component is gradually enhanced. Therefore, it can be assumed that the high frequency component and the low frequency component are partially statistically independent of each other. It is known from the characteristics of MIE that the MIE between two independent random variables should be equal to 0 . TherefoA single modal decomposition method has a certain effect on the signal-to-noise separation of noisy signals, but there are also various problems that may cause loss of valid information and result in incomplete noise removal. In this paper, a hybrid denoising algorithm which can separate the clear signal and noise and preserve the valid information as much as possible is proposed to further denoise the noise-dominated modal component generated by single mode decomposition. Besides this, it can also improve the denoising effect and performance indicators of the modal decomposition method. The VMD-WTD denoising flowchart is shown in K is determined.Since the mode number of the original signal decomposition in the VMD algorithm is an a priori knowledge estimation, there is a certain randomness which may cause errors in the mode decomposition. Based on the characteristics of EMD adaptive decomposition, without decomposing the mode number, the original signal is decomposed, the IMFs are observed and obtained, the MIE between IMFs is analyzed, and the effective mode number Algorithm 2 The variational mode decomposition (VMD)–wavelet thresholdStep 1. Decompose the original signal with EMD to obtain several IMFs.K.Step 2. Analyze the MIE between IMFs and determine the effective mode number K determined by the EMD.Step 3. Decompose the original signal by VMD based on the mode number Step 4. Analyze the MIE between IMFs and find the boundary line between high frequency IMFs and low frequency IMFs.Step 5. Denoise high frequency IMFs with WTD, retaining low frequency IMFs and retaining effective information as much as possible.Step 6. Perform VMD reconstruction on all low frequency IMFs and denoise high frequency IMFs to obtain a denoised signal.The variational mode decomposition (VMD)–wavelet threshold algorithm is listed as Algorithm 2. EMD: empirical mode decomposition; VMD: variational mode decomposition; IMF: intrinsic mode functions; WTD: wavelet threshold denoising; MIE: mutual information entropy.f1 = 4, f2 = 45, and f3 = 150; y1 is a clear signal with different amplitudes of different frequencies; and y is a noisy signal. The noisy signal and clear signal are shown in In order to verify the effect of the method described in this paper, the simulation data were tested. The original data included four impact signals. The equation is as followsK = 6. Then, the original data were decomposed by VMD based on K = 6.Results of EMD is shown in Results of VMD is shown in Results of WTD is shown in As shown in In order to show the applicability and superiority of the hybrid denoising method proposed, this paper adopts the actual process parameters collected from the continuous casting machine  developed by the China Heavy Machinery Research Institute for noise reduction. The parameter is the data of the mold level in the continuous casting process. The control of the mold level is very important in the continuous casting system and is of great significance for the quality and casting safety. Due to many uncertain disturbance factors in the mold level control process, the disturbance may change constantly, and most of the disturbances are nonlinear and nonstationary. Clear representation of the mold level data is of great significance for improving the process parameters of continuous casting production. The realization of the denoising method proposed in this paper has important practical significance.The EMD method was used to decompose the mold level signal into nine IMFs and one residual, as shown in K = 9. The mold level signal was decomposed using VMD based on K = 9.As shown in As shown in K = 9 as the mode component number for VMD decomposition we can clearly separate the original signals and avoid modal aliasing. It can be seen from the spectrum diagram that the IMF3–IMF7 frequency bandwidth is relatively long and the noise is serious, so WTD was performed on these IMFs. As shown in As shown in It can be seen from The performance of eight methods was verified using two statistical indicators in this paper, and the method that is most suitable for mold level was selected.RMSE):Root-Mean-Square Error (SNR):Signal–Noise Ratio  and (12), As shown in A single algorithm forcibly removing high frequency components cannot avoid losing valid information. The VMD-WTD algorithm is an improvement on the VMD algorithm and avoids the loss of valid information caused by forced removal of high frequency components. In comparison with the two algorithms in the hybrid, the denoising effect of VMD-WTD is better. The VMD-WTD denoising algorithm proposed in this paper shows improved denoising performance compared with the other seven algorithms analyzed, showing strong generalization ability and robustness.First, 40 groups of mold level signals under three different casting speeds were selected. After the signals were processed by the algorithm proposed in this paper, the maximum energy IMF in each set of data was calculated, and the center frequency of the maximum energy IMF was selected. As a feature, the center frequency distribution of the data before and after the denoising process is shown in (1)This is the first multimode decomposition denoising algorithm to be proposed.(2)It is a new denoising algorithm using multimode decomposition and WTD to be applied to mold level control.(3)In comparison with other algorithms, the proposed algorithm is a better denoising algorithm with higher SNR and lower RMSE.(4)By using the denoising algorithm and feature extraction method proposed in this paper, the center frequency information is determined. Compared with the feature extraction information without denoising, the experimental results show that the proposed denoising algorithm can effectively improve the recognition of the casting speed.This paper proposed a novel denoising algorithm which combines multiple mode decomposition algorithms, MIE, and the WTD algorithm, which is an adaptive denoising algorithm. The algorithm uses multiple mode decomposition methods, takes MIE as the threshold for identifying noise, and uses the WTD algorithm as the main denoising algorithm. As shown through the simulation and comparison of simulation data and measured signals, the denoising algorithm has the following advantages.


Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution. In the last decade, Automated Driving Systems (ADS) has shown significant advances, mainly from the acquisition, perception, control and actuation point of view . SeveralADS obtain information about the surroundings from different sensors, such as cameras, differential global positioning systems (GPS), Light Detection and Ranging (LiDAR), and Radio Detection and Ranging (RaDAR) systems . PerceptSome authors have considered sensor data fusion for more robust performance on different contexts: obstacles detection , perceptCurrently, the most widely used global localization approaches involve Global Navigation Satellite Systems (GNSS) such as GPS and Galileo . The impA fail-operational positioning system that comprises a UKF, a virtual sensor, and a monitor system, capable of remaining operative from degraded to total failure of the position reception and warns for fall-back triggering.A real-time trajectory planner that defines the lateral and longitudinal references for the DDT fall-back in degraded mode to achieve a minimal risk condition avoiding rear-end collisions.A vehicle motion control that executes the planned trajectory, including a lateral reference constraint avoiding undesirable lane departures.A case study resembles a real urban scenario demanding a DDT fall-back strategy due to a major positioning failure, working with minimum sensor interface bringing the vehicle to a safe place.However, a better assessment of fail-operational strategies for the functions of the dynamic driving task (DDT) is needed to achieve higher levels of automation on ADS (SAE J3016 ). This wThe rest of the paper is organized as follows. In The proposed work has been developed in the framework of the AutoDrive Project . This prPlease note that the required automation level must include, moreover the lateral and longitudinal motion control, a complete Object and Event Detection and Response (OEDR) system, and the capability to be robust enough to support fail-operational operation , which iThe control architecture proposed to achieve this goal is depicted in In the next subsections, the different stages that compose the proposed architecture are detailed.The database is composed of a list of waypoints that contain relevant information of the fixed route, such as global axis coordinates , odometer and steering sensors are considered. These devices provide the vehicle position  of the acquisition blocks.When the vehicle performance is highly compromised or a relevant sensor failure is detected, a fail-operational strategy is activated. Using the data from the positioning sensors , a virtual positioning sensor is switched on and employed to perform the fall-back strategy that leads the test platform to a safe state. This one of the contributions of this work, and its development is broadly detailed in The decision module creates the trajectories to be followed by the automated vehicle and is integrated by the trajectory planner and a collision-avoidance system.The trajectory planner, in the case of normal operation, will generate optimum trajectories for a specified scenario. However, in the case of system failure, such as the case study analyzed in On the other hand, the collision-avoidance system is focused on avoiding rear-end collisions with previous vehicles or objects. Please note that the objects detected by the perception system are first analyzed to evaluate if they are within the trajectory to be followed. Therefore, objects located outside the road borders do not represent a collision risk and adjustments are not necessary over the original trajectory, while objects within the trajectory will require adjustment of the trajectory by maintaining a safe distance to the objects ahead. The proposed approach, which will be detailed in The trajectory references estimated in the decision stage are followed by the vehicle motion control, providing reliable inputs to the vehicle interface. The velocity  and x). The lateral tire forces on the front and rear wheels  it is possible to estimate the cornering stiffness coefficients ications . This waAn open-loop test method for determining the steady-state circular driving behavior described in  ISO 413, so thatPlease note that the use of an off-line cornering stiffness estimator implies that the open-loop tests must cover the whole range of erations .An adaptive Unscented Kalman Filter (UKF) is used to attenuate the errors introduced in the GNSS-INS positioning measurement when the satellite signal quality is reduced. In contrast to other Kalman filtering techniques, the UKF frequently provides a lower estimation error and is preferable for implementations in automated driving applications . In thisX, Y, The development of the UKF requires the space-state transition model of the vehicle detailed in The process noise covariance matrix , a virtual positioning sensor is used to provide an indirect position measurement by combining information from the remaining physical sensors.The velocity from odometer  approach has been implemented, generating the velocity references to be followed by a low-level control, attempting to maintain a safe relative distance (H). The references for The state parameters The state parameters weighting matrix  diagram , which dThe maximum deceleration permitted , as depicted in The selected test route covers a challenging environment with static objects in addition to difficult vehicle motion maneuvers as roundabouts, merging streets and intersections, as seen in In case of system failures, the ADS must respond without driver intervention to achieve a minimal risk condition bringing the vehicle to a safe state. In this sense, permitted and non-permitted stops are considered to be portrayed in The test case analyzed in this work is delimited to the evaluation zone depicted in As an additional issue, the case is considered in which the emergency shoulder cannot be used, as another vehicle is already parked, requiring driving of a longer distance to the next permitted stop while performing dead reckoning. Three different failure scenarios are analyzed, as shown in When a failure of the GNSS occurs, the distance required , the output of the UKF filter and the real position of the vehicle are measured, and compared with the trajectory reference, to calculate the lateral positioning error Results for the four signal quality scenarios are shown in From the results, the decrease in the quality of the GNSS signal increases significantly the lateral error if the raw data is used (raw case). It could be fatal in an automated vehicle operation such as the one analyzed. Moreover, the errors could introduce instability in the controllers, depending on the nature of the noise. This emphasizes the need for providing robust solutions to positioning measurements in automated vehicles.Results also demonstrate the positive performance of the proposed UKF approach (UKF case), which can reduce in more than 90% the error associated with In this section, the proposed fall-back strategy performance is evaluated in the three different scenarios depicted in In all three scenarios, the same GNSS failure sequence will be evaluated, as depicted in Reference) and the real speed of the vehicle (Ego-vehicle) is depicted. In the second one (ObjectDistance) and to the next emergency shoulder (SpaceAvailable) is shown. These distances are calculated with the position of these items in the planned trajectory. Also, the longitudinal distance required for performing the lane-change maneuver is shown (SpaceRequired). This calculation is detailed in the Equation (raw), the output of the UKF (UKF) and the real position of the vehicle . Finally, the computational cost of the high and low-level controllers is shown.The performance data in the three scenarios are shown in Equation . In the From these graphs, several conclusions can be drawn. First, the robustness of the proposed UKF-based position estimator is demonstrated in all scenarios. If real), creating a drift. This effect is better seen in the third scenario, in which the nearest emergency shoulder is not available (is full) and therefore, the vehicle needs to move to the next one, operating more time in degraded mode. Hence, the degraded mode is intended to be used in emergencies for limited amounts of time or small distances, which is a valid assumption in urban environments such as the ones analyzed in the case study.Second, the proposed fail-operational Positioning System proves an effective approach in a total failure case. When total failure happens , the data provided by the GNSS remains constant and no longer can be used to estimate the position. At this point, the Positioning Monitor of the fail-operational positioning system switches to the Virtual Positioning Controller, entering degraded mode while making use of the odometer and the steering wheel to estimate the position of the vehicle. Please note that due to the nature of the selected sensors, estimation errors in Third, the longitudinal speed (SpaceRequired) which depends on the current velocity and compares it with the distance to the nearest object/vehicle parked (ObjectDistance) and the available emergency shoulder distance (SpaceAvailable). Only if both are higher than the required distance to maneuver, the trajectory planner modifies the original route to start the lane-change maneuver. Please note that the object detection distance limit is 50 m and that the emergency shoulder-distance limit detection is 60 m, hence higher distances are limited to the maximum value.In this state, the trajectory planner searches for available spaces on the next emergency shoulder. For that purpose, the planner calculates the required space for the emergency parking maneuver (The first scenario (parking before an object/vehicle in a shoulder), is the simplest one. It can be seen that when the degraded state is activated (97 s), the required space is less than the available shoulder distance, and the distance to the next vehicle, activating the lane-change maneuver (which implies a peak in Finally, if the computational cost graphs are considered, it can be seen that the proposed approach is computationally efficient, requiring less than 1ms to execute in an Intel Core i7-6600u CPU, 2.60GHz and 16GB RAM. This demonstrates that the approach can be implemented in real time.Comfort is a key issue when considering automated driving solutions. Traditionally, comfort has been related to the magnitude of the lateral and longitudinal accelerations, being higher ones less comfortable for passengers.In As can be seen, even before the failure, the differences in the lateral acceleration are important due to the noise that the GNSS presents in lower qualities. Lateral accelerations are an order of magnitude higher in these cases, resulting in more uncomfortable driving. Therefore, sensor quality directly can affect passenger comfort.Please note that the longitudinal acceleration is not affected in this case, due to the odometry is used to estimate it. When failure occurs, similar behavior is achieved. However, the vehicle speed and accelerations are reduced when the positioning monitor switches to the virtual positioning sensor.Although the research and development in automated driving has considerably helped the implementation of higher SAE automation levels, current control architectures rely on the driver as a backup in case of system failures. Moreover, hardware redundancy is the usual action plan to ensure fail-operational systems, as few software solutions exist in the literature.The present work targets the issue of the vehicle bringing itself to a safe state in degraded mode after a major failure in the position receiver. Instead of a progressive deceleration on the current lane, the system focuses on seeking a permitted space on the route, performing a lane-change maneuver to the emergency shoulder, and then executes a safe stop.The fail-operational control architecture and systems proposed here are explained in depth. They include basic ADS features to achieve a minimal risk condition along a route, according to a realistic case study presented for bus shuttling services as: fail-operational positioning system, real-time trajectory planner, collision-avoidance system and vehicle motion controller.The fail-operational positioning system comprises a UKF to improve the vehicle location due to lack of quality in the position receiver, an issue very common in urban scenarios where the satellite line-of-sight would be constantly obstructed. A virtual sensor switches on by a positioning monitor in case of total failure in the position sensor is detected, then a DDT fall-back strategy is possible performing dead reckoning with database information. A previous cornering stiffness estimation through open-loop tests provides useful information for the vehicle model employed.The real-time trajectory planner is capable of comfortably slow-down the velocity reference after the failure, expecting an available and permitted space to perform a lane-change maneuver and safely locating the vehicle on the emergency shoulder. The benefits of having an object parked in advance are considered, hence the available space to initiate the parking maneuver is contrasted constantly with a required space calculation. A rear-end collision-avoidance system is activated at all times adapting the velocity reference to remain a safe distance to objects ahead.Both the collision-avoidance system and the vehicle motion controller are based on MPC. It is possible to optimize the trajectory bearing in mind safety and comfort in maneuvers. The vehicle motion controller includes a lateral position restriction aiming to improve the lane-keeping performance, being possible to enhance it on one side when lane-change maneuvers are required avoiding that the vehicle goes out the road boundaries.As this paper was focused on presenting a fail-operational control architecture approach in case of positioning failures, future works will consider in depth the maximum time–distance travel capacity in dead-reckoning circumstances under the influence of real instrumentation and the urban scenario presented in this article.