Porta ET seu porta AND (ex Anglica) est porta logica digitalis quae coniunctionem logicalem efficit. Gerit ut tabula veritatis quae ad dextram videre potest. Datum eductum est SUPERNUM (1) solum si uterque data inducta portae sunt SUPERNA. Si nullum aut solum unum datum inductum portae est SUPERNUM, data eductum est SUBMISSUM (0). In sensu alio, functio ET minimum duorum digitorum educit, sicut functio AUT maximum educit.
Index
1Symbola
2Aequatio Booleana
3Forma
3.1Forma alterna
4Notae
5Nexus interni
Symbola |
Sunt tria symbola pro portis ET: symbolum ANSI ("Americanum" aut "militare"), symbolum IEC ("Europaeum" aut "rectangulare") aut IEEE, et obsoletum symbolum DIN. Lex IEEE et symbola "formarum distinctarum" et "formarum rectangularum" pro portis logicis simplicis sinit. Si vis, vide etiam Symboli Portae Logicae.
Symbolum MIL/ANSI [1]
Symbolum IEC/IEEE [2]
Symbolum DIN
Aequatio Booleana |
Porta ET cum inducta A et B, et eductum C hunc aequationem logicam efficit:
C=A⋅B{displaystyle C=Acdot B}
Forma |
Porta ET, NMOS facta.
Forma alterna |
Si portae ET propriae non adsunt, porta ET ex portis NON-ET aut NON-AUT creare potest, quia illae portae sunt portae universales,[3] quod portas NON-ET aut NON-AUT creare portas omnes alias posse significat. Quamquam portae XAUT portas alias creare possunt, est rarum.
Notae |
↑Michael H. Tooley, Mike Tooley, David Wyatt (2008) .mw-parser-output .existinglinksgray a,.mw-parser-output .existinglinksgray a:visited{color:gray}.mw-parser-output .existinglinksgray a.new{color:#ba0000}.mw-parser-output .existinglinksgray a.new:visited{color:#a55858} (Anglice). Aircraft Electrical and Electronic Systems. Butterworth-Heinemann. p. 59
↑John F. Wakerly (2005). Digital Design Principles and Practices (4a ed.). Prentice Hall. ISBN 0-13-186389-4
↑M. Morris Mano et Charles R. Kime (2004). Logic and Computer Design Fundamentals (3a ed.). Prentice Hall. p. 73.
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
1
$begingroup$
This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
1
$begingroup$
I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
