LEADER 05099nam 22015373a 450 001 9910367757403321 005 20250203235433.0 010 $a9783039213764 010 $a3039213768 024 8 $a10.3390/books978-3-03921-376-4 035 $a(CKB)4100000010106143 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/52517 035 $a(ScCtBLL)5ddaff7b-c58a-4283-a2a5-6041e89b403d 035 $a(OCoLC)1163849944 035 $a(oapen)doab52517 035 $a(EXLCZ)994100000010106143 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)$fJohn Ball, Bo Tang 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (344 p.) 311 08$a9783039213757 311 08$a303921375X 330 $aThis book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book. 606 $aHistory of engineering and technology$2bicssc 610 $aFPGA 610 $arecurrence plot (RP) 610 $aresidual learning 610 $aneural networks 610 $adriver monitoring 610 $anavigation 610 $adepthwise separable convolution 610 $aoptimization 610 $adynamic path-planning algorithms 610 $aobject tracking 610 $asub-region 610 $acooperative systems 610 $aconvolutional neural networks 610 $aDSRC 610 $aVANET 610 $ajoystick 610 $aroad scene 610 $aconvolutional neural network (CNN) 610 $amulti-sensor 610 $ap-norm 610 $aocclusion 610 $acrash injury severity prediction 610 $adeep leaning 610 $asqueeze-and-excitation 610 $aelectric vehicles 610 $aperception in challenging conditions 610 $aT-S fuzzy neural network 610 $atotal vehicle mass of the front vehicle 610 $aelectrocardiogram (ECG) 610 $acommunications 610 $agenerative adversarial nets 610 $acamera 610 $aadaptive classifier updating 610 $aVehicle-to-X communications 610 $aconvolutional neural network 610 $apredictive 610 $aGeobroadcast 610 $ainfinity norm 610 $aurban object detector 610 $amachine learning 610 $aautomated-manual transition 610 $ared light-running behaviors 610 $aphotoplethysmogram (PPG) 610 $apanoramic image dataset 610 $aparallel architectures 610 $avisual tracking 610 $aautopilot 610 $aADAS 610 $akinematic control 610 $aGPU 610 $aroad lane detection 610 $aobstacle detection and classification 610 $aGabor convolution kernel 610 $aautonomous vehicle 610 $aIntelligent Transport Systems 610 $adriving decision-making model 610 $aGaussian kernel 610 $aautonomous vehicles 610 $aenhanced learning 610 $aethical and legal factors 610 $akernel based MIL algorithm 610 $aimage inpainting 610 $afusion 610 $aterrestrial vehicle 610 $adriverless 610 $adrowsiness detection 610 $amap generation 610 $aobject detection 610 $ainterface 610 $amachine vision 610 $adriving assistance 610 $ablind spot detection 610 $adeep learning 610 $arelative speed 610 $aautonomous driving assistance system 610 $adiscriminative correlation filter bank 610 $arecurrent neural network 610 $aemergency decisions 610 $aLiDAR 610 $areal-time object detection 610 $avehicle dynamics 610 $apath planning 610 $aactuation systems 610 $amaneuver algorithm 610 $aautonomous driving 610 $asmart band 610 $athe emergency situations 610 $atwo-wheeled 610 $asupport vector machine model 610 $aglobal region 610 $abiological vision 610 $aautomated driving 615 7$aHistory of engineering and technology 700 $aBall$b John$0660667 702 $aTang$b Bo 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367757403321 996 $aMachine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)$94323257 997 $aUNINA LEADER 06358nam 2201957z- 450 001 9910367743403321 005 20210211 010 $a3-03921-789-5 035 $a(CKB)4100000010106283 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/41042 035 $a(oapen)doab41042 035 $a(EXLCZ)994100000010106283 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplication of Bioinformatics in Cancers 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 online resource (418 p.) 311 08$a3-03921-788-7 330 $aThis collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. 606 $aBiotechnology$2bicssc 610 $aactivation induced deaminase 610 $aAID/APOBEC 610 $aalternative splicing 610 $aanti-cancer 610 $aartificial intelligence 610 $abioinformatics 610 $aBioinformatics tool 610 $abiomarker discovery 610 $abiomarker signature 610 $abiomarkers 610 $abiostatistics 610 $abrain 610 $abrain metastases 610 $abreast cancer 610 $abreast cancer detection 610 $abreast cancer prognosis 610 $aBufadienolide-like chemicals 610 $acancer 610 $acancer biomarker 610 $acancer biomarkers 610 $acancer CRISPR 610 $acancer modeling 610 $acancer prognosis 610 $acancer treatment 610 $acancer-related pathways 610 $acell-free DNA 610 $achemotherapy 610 $acirculating tumor DNA (ctDNA) 610 $aclassification 610 $aclinical/environmental factors 610 $acolorectal cancer 610 $acomorbidity score 610 $aComputational Immunology 610 $aconcatenated deep feature 610 $acopy number aberration 610 $acopy number variation 610 $acuration 610 $acurative surgery 610 $adatasets 610 $adecision support systems 610 $adeep learning 610 $adenoising autoencoders 610 $adifferential gene expression analysis 610 $adiseases genes 610 $aDNA 610 $aDNA sequence profile 610 $adrug resistance 610 $aepigenetics 610 $aerlotinib 610 $aestrogen 610 $aextreme learning 610 $afalse discovery rate 610 $afeature extraction and interpretation 610 $afeature selection 610 $afirehose 610 $afunctional analysis 610 $agefitinib 610 $agene expression analysis 610 $agene inactivation biomarkers 610 $agene loss biomarkers 610 $agene signature extraction 610 $agenomic instability 610 $aGEO DataSets 610 $ahead and neck cancer 610 $ahealth strengthening herb 610 $ahierarchical clustering analysis 610 $ahigh-throughput analysis 610 $ahistopathological imaging 610 $ahistopathological imaging features 610 $aHNSCC 610 $ahormone sensitive cancers 610 $aHP 610 $ahTERT 610 $aimaging 610 $aindependent prognostic power 610 $ainteraction 610 $aintratumor heterogeneity 610 $aknockoffs 610 $aKRAS mutation 610 $alocoregionally advanced 610 $amachine learning 610 $ameta-analysis 610 $amethylation 610 $amicroarray 610 $amiRNA 610 $amiRNAs 610 $amitochondrial metabolism 610 $amixture of normal distributions 610 $amolecular mechanism 610 $amolecular subtypes 610 $aMonte Carlo 610 $amortality 610 $amultiple-biomarkers 610 $amutable motif 610 $amutation 610 $aNeoantigen Prediction 610 $anetwork analysis 610 $aNetwork Analysis 610 $anetwork pharmacology 610 $anetwork target 610 $aneurological disorders 610 $anext generation sequencing 610 $aobserved survival interval 610 $aomics 610 $aomics profiles 610 $aoral cancer 610 $aovarian cancer 610 $aoverall survival 610 $apancreatic cancer 610 $apathophysiology 610 $aPD-L1 610 $aprecision medicine 610 $apredictive model 610 $aprotein 610 $aR package 610 $aRNA 610 $aself-organizing map 610 $asingle-biomarkers 610 $asingle-cell sequencing 610 $askin cutaneous melanoma 610 $asomatic mutation 610 $aStAR 610 $asteroidogenic enzymes 610 $asurvival analysis 610 $aTCGA 610 $aTCGA mining 610 $atelomerase 610 $atelomeres 610 $aThe Cancer Genome Atlas 610 $atraditional Chinese medicine 610 $atranscriptional signatures 610 $atreatment de-escalation 610 $atumor 610 $atumor infiltrating lymphocytes 610 $atumor microenvironment 610 $avariable selection 615 7$aBiotechnology 700 $aBrenner$b J. Chad$4auth$01292380 906 $aBOOK 912 $a9910367743403321 996 $aApplication of Bioinformatics in Cancers$93022234 997 $aUNINA