画像は参照用のみです。
NN03-320
DUO MXTEND MULTIBAND ANTENNA
- メーカー
- Mfr。パート #NN03-320
- パッケージ
- データシート
- 在庫中2395
100% オリジナル &新しい
出荷の準備ができている24時間
365日の保証
RFQおよびもっと割引を得る
仕様
| Supplier | Ignion |
| Package | Tape & Reel (TR),Cut Tape (CT) |
| Series | DUO mXTEND™ |
| ProductStatus | Active |
| RFFamily/Standard | Navigation |
| FrequencyGroup | Ultra Wide Band |
| Frequency(Center/Band) | 1.561GHz, 1.575GHz, 1.606GHz, 2.45GHz, 3.5GHz, 3.7GHz, 3.4Ghz, 4.0GHz, 4.5GHz, 5.0GHz , 5.5GHz, 6.1GHz, 6.6GHz, 7.1GHz, 7.7GHz, 8.2GHz, 8.7GHz, 9.2GHz, 9.8GHz, 10.3GHz |
| FrequencyRange | 1.56GHz ~ 10.6GHz |
| AntennaType | Chip |
| NumberofBands | 3 |
| VSWR | 2.5 |
| Gain | 2.6dBi |
| Termination | Solder |
| MountingType | Surface Mount |
| Height(Max) | 0.079 (2.00mm) |
概要
Description
NN03-320 is likely a course code rather than a standard name. If it’s an Introduction to Neural Networks course, the exact syllabus depends on the institution. A typical outline might include:
- Basic concepts: neuron model, activation functions, feedforward nets
- Training: backpropagation, gradient descent, loss functions
- Architectures: MLPs, CNNs, RNNs
- Learning issues: overfitting, regularization, hyperparameter tuning
- Data handling: preprocessing, normalization, datasets
- Evaluation: metrics, validation, cross-validation
- Tools: introductory use of ML frameworks (e.g., TensorFlow, PyTorch)
- Projects: small NN design/implementation on a dataset
Prereqs often include linear algebra, calculus, and programming.
For exact details (learning outcomes, credits, schedule), check your institution’s catalog or syllabus for NN03-320, or provide the university name and term and I can help locate the specific description.
- Basic concepts: neuron model, activation functions, feedforward nets
- Training: backpropagation, gradient descent, loss functions
- Architectures: MLPs, CNNs, RNNs
- Learning issues: overfitting, regularization, hyperparameter tuning
- Data handling: preprocessing, normalization, datasets
- Evaluation: metrics, validation, cross-validation
- Tools: introductory use of ML frameworks (e.g., TensorFlow, PyTorch)
- Projects: small NN design/implementation on a dataset
Prereqs often include linear algebra, calculus, and programming.
For exact details (learning outcomes, credits, schedule), check your institution’s catalog or syllabus for NN03-320, or provide the university name and term and I can help locate the specific description.
Package
NN03-320 could refer to multiple items from different manufacturers. Please specify the product category or vendor. I can then state the exact package type (e.g., reel, tray, pouch, bottle) and its dimensions.
Equivalent
I don’t recognize NN03-320 unambiguously. Please tell me the manufacturer or the chip’s function (e.g., regulator, MOSFET, TVS, amplifier), package markings, or share the datasheet/image. With that I can list direct equivalents (matching electrical specs, pinout and package) quickly.
出荷
出荷方法私たち
DHL、FedEx、TNT、UPS、またはあなたの選択の他の運送業者を通じてグローバルな出荷サービスを提供します。
出荷料参照(DHL/FedEx):
DHL の: 配送費は25ドル〜45ドル(0.5kg)の範囲で、推定配送時間は2〜5営業日です。
フェデックス: 配送費は25ドル〜40ドル(0.5kg)の範囲で、推定配送時間は3〜7営業日。
UPS の: 配送費は25ドル〜45ドル(0.5kg)の範囲で、推定配送時間は3〜7営業日。
TNT社: 配送費は25ドル〜65ドル(0.5kg)の範囲で、推定配送時間は3〜7営業日。
EMS: 配送料金は30〜50ドル(0.5kg)の範囲で、推定配送時間は7〜15営業日です。
登録航空郵便: 配送費は2〜4ドル(0.1kg)で、推定配送時間は5〜20営業日です。
支払い
Payment Methods
支払い期間は100%前払いです。
現在、下記の支払い方法のみを受け入れています。:
1. ペイパル
2. クレジット・デビットカード
3. ワイヤー 転送
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