Motorola Shows Off Future Tech

摩托罗拉在其技术创新展示中预览了其对于未来计算中心——手机的愿景。其中包括使用面部识别提供客户服务的虚拟形象、带有RFID跟踪技术的零售产品包装、采用环保材料的手机外壳等。此外,还展示了电视顶盒、社交电视概念及PC到手机的VoIP系统。

PC Magazine (10/23/06) Segan, Sascha

Motorola's "Technology Innovation Showcase" in Chicago offered a first look at what the company has been working on. Motorola sees the cell phone at the center the next generation of computing. "We're taking a broader view of the cell phone...finally, the Internet business models, the experimental lab of the Internet can come to mobile devices. The technology world is beginning to get the protocols and standards together for this," says Motorola CTO Rob Shaddock. A customer service avatar was on display, which uses a camera for facial recognition that identifies repeat customers. Retail shelves of the future may be filled with boxes that illuminate when picked up, and are able to track how many times, and for how long, they are picked up by customers, thanks to an RFID chip. Motorola has decided to comply with a European Union directive to make electronics more recyclable, and will create phone casings made from recycled and biodegradable materials as part of its ECOMOTO initiative. Motorola also plans to release TV set-top cable boxes, as part of its Connected Home project, since the FCC has mandates a new standard for CableCard that will force cable providers to more high-tech boxes. Social TV, also being developed, is a way for people to talk about a TV show they are watching from different locations, utilizing a technology similar to instant messaging. Also on display was "Motorola Messenger Modem," a PC-to-phone system that uses a PC's modem to route VoIP calls over a land line to your cell phone.
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【多变量输入超前多步预测】基于CNN-BiLSTM的光伏功率预测研究(Matlab代码实现)内容概要:本文介绍了基于CNN-BiLSTM模型的多变量输入超前多步光伏功率预测方法,并提供了Matlab代码实现。该研究结合卷积神经网络(CNN)强大的特征提取能力与双向长短期记忆网络(BiLSTM)对时间序列前后依赖关系的捕捉能力,构建了一个高效的深度学习预测模型。模型输入包含多个影响光伏发电的气象与环境变量,能够实现对未来多个时间步长的光伏功率进行精确预测,适用于复杂多变的实际应用场景。文中详细阐述了数据预处理、模型结构设计、训练流程及实验验证过程,展示了该方法相较于传统模型在预测精度和稳定性方面的优势。; 适合人群:具备一定机器学习和深度学习基础,熟悉Matlab编程,从事新能源预测、电力系统分析或相关领域研究的研发人员与高校研究生。; 使用场景及目标:①应用于光伏电站功率预测系统,提升电网调度的准确性与稳定性;②为可再生能源并网管理、能量存储规划及电力市场交易提供可靠的数据支持;③作为深度学习在时间序列多步预测中的典型案例,用于科研复现与教学参考。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注数据归一化、CNN特征提取层设计、BiLSTM时序建模及多步预测策略的实现细节,同时可尝试引入更多外部变量或优化网络结构以进一步提升预测性能。
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