Perancangan Peraga LED Terprogram Berbasis Mikrokontroler AT89C52

Dengan tersedianya mikrokontroler yang memiliki berbagai fasilitas serta murahnya harga PC yang ditawarkan, maka peluang untuk merancang peralatan pengendalian LED untuk berbagai keperluan menjadi sangat terbuka. Tujuan dari penelitian ini adalah merancang dan menguji peraga LED terprogram berbasis mikrokontroler AT89C52. Alat penampil LED matrik dirancang data-nya bisa dimasukan secara otomatis, sehingga LED dapat menampilkan data dengan segera. Perancangan sistem penampil LED matrik yang dibuat meliputi dua bagian utama yaitu bagian perangkat keras dan bagian perangkat lunak. Perangkat keras meliputi mikrokontroler AT89C52 sebagai pusat pengontrol, penggerak kolom dan penggerak baris serta LED matrik untuk menampilkan data. Perangkat lunak meliputi bahasa mesin mikrokontroler dan untuk berhubungan dengan PC menggunakan Borland Delphi. Pengujian dilakukan dengan membandingkan tampilan LED matrik dengan data masukan dari PC. Alat ini dapat mengendalikan dan menampilkan LED sesuai data yang diberikan. LED matrik dapat menampilkan empat baris teks yang masing-masing baris maksimal enam karakter, gambar serta animasi running text (teks berjalan)

Map locations of QTLs associated with resistance to root-knot nematode.

<p>Single-marker analysis conducted using nonparametric mapping (Kruskal-Wallis analysis) test equivalent of the one-way analysis of variance (Van Ooijen 2004). Red bars - QTL influencing root-galling index phenotype; Green bars - QTL influencing egg production phenotype (For Experiment I).</p

QTLs associated by nonparametric mapping with root-galling index (GI) and nematode egg production (EGR) in TM-1 x Pima 3–79 RIL population.

a<p>QTL - Root-galling index (GI) phenotype;</p>b<p>QTL- Data for nematode egg production were transformed to Log₁₀(x+1) for analysis (Log EGR);</p>c<p>Chr: Cotton chromosome designation;</p>d<p><i>Mi-GI_h-C03₁</i>: The name of first (1) identified QTL for GI on chr 3 from <i>G. hirsutum</i> (h) to root-knot nematode <i>Meloidogyne incognita</i> (<i>Mi</i>): <i>Mi-EGR_b-C23</i>: The name of QTL for EGR on chr 23 from <i>G. barbadense (b)</i> to root-knot nematode <i>Meloidogyne incognita</i> (<i>Mi</i>);</p>e<p>K*: Kruskal-Wallis analysis test regarded as the nonparametric equivalent of the one-way analysis of variance (Van Ooijen 2004);</p>f<p><i>P</i>-value: P-values are designated as P<0.05 (**), 0.01 (***), 0.005 (****), 0.001 (*****), 0.0005 (******);</p>g<p>TM-1 <b>allele</b>: Mean value of phenotype associated with the TM-1 allele; Pima 3–79 allele, Mean value of phenotype associated with the Pima 3–79 allele.</p

Root galling index (GI) and egg production (LogEGR) of <i>Meloidogyne incognita</i> on susceptible (TM-1, Pima 3–79, Pima S-7 and Acala SJ-2) and resistant (Acala N901 and Acala NemX) cotton cultivars.

<p>Data were collected 60 days after inoculation. The error bars show standard error.</p

Effect of the combinations of QTLs for galling index and nematode egg production on mean phenotypic value (± standard error) based on genotypic classes carrying one to four favorable alleles (+, ++, +++, ++++ blue bars) and null genotypes (−,−−,−−−,−−−−, red bars) for Experiment I.

<p>Effect of the combinations of QTLs for galling index and nematode egg production on mean phenotypic value (± standard error) based on genotypic classes carrying one to four favorable alleles (+, ++, +++, ++++ blue bars) and null genotypes (−,−−,−−−,−−−−, red bars) for Experiment I.</p

The UPGMA dendrogram of 288 <i>G</i>. <i>barbadense</i> accessions, constructed using the genotype of 301 polymorphic SSR alleles.

<p>Horizontal lines denote thresholds of genetic distances. Groups A and B are obtained on the basis of differences in > 50%, whereas subgroups G1, G2 and G3 obtained based on the upper boundary distinctions in 40%, and the subgroups G5 and G4—the upper bound of 20%.</p

Pair-wise comparisons of F_st values specific to each ecotype.

<p>Pair-wise comparisons of F_st values specific to each ecotype.</p

Correlations between fiber traits of <i>G</i>. <i>barbadense</i> cultivars in the Tashkent and California environments.

<p>Correlations between fiber traits of <i>G</i>. <i>barbadense</i> cultivars in the Tashkent and California environments.</p

Summary of SSR markers showed significant association with each of the trait studied in the Uzbekistan (UZB) and USA environments.

<p>Summary of SSR markers showed significant association with each of the trait studied in the Uzbekistan (UZB) and USA environments.</p

Differentiation of 288 <i>G</i>. <i>barbadense</i> accessions based on genetic and principal component analysis.

<p>Differentiation of 288 <i>G</i>. <i>barbadense</i> accessions based on genetic and principal component analysis.</p