Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› ieee-transactions-on-signal-processing
ieee-transactions-on-signal-processing
GitHub用于判断信号处理手稿是否适合IEEE TSP期刊，或进行投稿前适配性检查。涵盖期刊定位、理论加分析的门槛、基线对比要求、栏目区分及拒稿启发式规则，辅助作者优化框架与选题。
Trigger Scenarios
作者针对估计、检测或阵列/图信号处理稿件寻求适配性检查
需将算法描述重构为包含信号模型、分析和正确基线的学术框架
在TSP与应用型SP或机器学习会议间选择投稿 venue
需要特定的信号处理理论门槛和desk-reject启发式规则
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ieee-transactions-on-signal-processing -g -y
SKILL.md
Frontmatter
{
"name": "ieee-transactions-on-signal-processing",
"description": "Use when targeting IEEE Transactions on Signal Processing or deciding whether a signal-processing methods manuscript fits this venue. Encodes the journal's fit, the algorithm-plus-analysis bar, the baseline-and-guarantee expectation, the regular-paper-vs-correspondence framing, house style, official-submission re-check, and desk-reject heuristics."
}
IEEE Transactions on Signal Processing (ieee-transactions-on-signal-processing)
Journal positioning
IEEE Transactions on Signal Processing is the archival venue for the theory and methods of signal processing: estimation, detection, sampling and reconstruction, filtering, spectral analysis, and array, graph, and statistical signal processing, together with the optimization machinery that underpins them. The defining expectation is a new signal-processing method with analysis — an algorithm whose behavior is characterized (consistency, convergence, performance bounds, identifiability) or that demonstrably outperforms correct, current baselines under a clear model. A generic machine-learning paper, or an application study that merely runs an existing pipeline on new data, is a poor fit. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current official author guidelines. Before submitting, re-check the live IEEE Transactions on Signal Processing author information and submission system.
When to trigger
- The author names this journal for an estimation, detection, sampling, or array/graph signal-processing manuscript and wants a fit/framing check.
- A method must be re-framed from "our algorithm gives good results" into a result with a signal model, analysis, and the right baselines.
- The author is choosing between this Transactions and an applications-oriented SP
venue, a machine-learning venue, or
ieee-transactions-on-communications. - The author needs the SP-theoretic framing bar and desk-reject heuristics specific to signal-processing methods.
Scope & topic fit
- Statistical signal processing: parameter estimation, detection and hypothesis testing, Cramér–Rao-type bounds, Bayesian and robust estimation.
- Sampling, reconstruction, and sparse/compressive methods; sampling theory beyond Nyquist; dictionary and subspace methods with recovery guarantees.
- Array and sensor-array processing: beamforming, direction-of-arrival, source localization, distributed and networked sensing.
- Graph signal processing and signal processing over networks: spectral methods, filtering, and sampling on graphs.
- Optimization for signal processing: convex/nonconvex methods, ADMM and proximal algorithms, with convergence or optimality analysis tied to an SP problem.
Method & evidence bar
- The contribution is a method with analysis: an estimator/detector/algorithm whose properties (bias/variance, consistency, convergence rate, identifiability, recovery conditions) are characterized, or a clearly superior empirical result.
- Baselines must be the correct, current competitors under the same signal model and conditions; beating a strawman or an outdated method is not evidence.
- When the claim is empirical, experiments must use realistic models, report variance across trials, and isolate the source of improvement; when the claim is theoretical, proofs must be complete.
- The signal/observation model and assumptions must be explicit; performance claims must hold under those assumptions, with sensitivity to model mismatch discussed.
- Position precisely against prior SP results: tighter bound, weaker assumptions, lower complexity, or a genuinely new processing principle.
Structure & house style
- IEEE format; the journal publishes full Regular Papers and shorter Correspondence items — match the article type to the contribution and re-check current definitions on the live guide.
- The signal model and problem statement come early and precisely; the proposed method and its analysis are the core, with derivations in-text or in appendices.
- The introduction motivates the SP gap, not an application novelty; relate the method to classical SP theory.
- Figures are analytical and comparative: MSE-vs-SNR curves, ROC curves, convergence plots, and benchmark comparisons under matched conditions.
Official-submission checklist
- Before giving submission-ready advice, read
../../resources/source-basis.mdand../../resources/official-source-map.md; start from the IEEE Author Center anchors, then cite the current Signal Processing page you checked. - Search the live site for "IEEE Transactions on Signal Processing information for authors" and follow the current submission-system version.
- Re-check article types (Regular Paper vs. Correspondence), length/overlength policy, and the IEEE template.
- Confirm reproducibility expectations: code/data availability and reproducible-research practices for any empirical claims.
- Re-check ORCID, competing-interests, funding, author-contribution, and AI-use disclosure requirements, and IEEE open-access options.
- If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
- The contribution is an SP method with analysis or a clearly superior result under a stated signal model — not a generic ML pipeline on new data.
- Baselines are the correct, current competitors under matched conditions.
- Theoretical claims have complete proofs; empirical claims report variance and isolate the improvement's source.
- The signal/observation model and assumptions are explicit, with model-mismatch sensitivity discussed.
- Novelty is pinned to a specific SP-theoretic gain (tighter bound / weaker assumptions / lower complexity / new principle).
- Article type and length fit current limits.
Common desk-reject triggers
- A generic machine-learning or deep-learning method with no signal-processing theory or model-based framing.
- Application-only study running an existing SP pipeline on a new dataset with no methodological contribution.
- Empirical gains over weak or outdated baselines, or with no reported variance across trials.
- Algorithm proposed with no analysis and no guarantee, where the analysis was the expected contribution.
- Scope mismatch: a communications-system or control paper using SP vocabulary without an SP-theoretic result.
Re-routing decision
- Communication-system design/performance is the core →
ieee-transactions-on-communications. - Wireless PHY/MAC and resource allocation →
ieee-transactions-on-wireless-communications. - Information-theoretic limits rather than estimators/detectors →
ieee-transactions-on-information-theory. - Control/estimation as a dynamical-systems theorem →
ieee-transactions-on-automatic-control/automatica. - Broad tutorial synthesis of an SP area →
proceedings-of-the-ieee.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] IEEE Transactions on Signal Processing
[Topic tags] <2–3 closest SP subtopics>
[Method + analysis] <algorithm and the guarantee/analysis that anchors it>
[Baselines] <are competitors correct, current, and matched?>
[Top risk] <the single most likely reason for rejection>
[Article type] Regular Paper / Correspondence
[Official items to re-check] <article type / length / reproducibility / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>
Version History
- 1839142 Current 2026-07-05 12:55
