We consider the Doppler ambiguity compensation problem for weak moving target detection in passive bistatic radar. Detecting an unknown high-speed weak target has a high probability of the presence of Doppler ambiguity, which will decrease the integration performance and accordingly make the target detection difficult under low signal-to-noise ratio (SNR) environments. Resorting to the well-known keystone transform (KT) method, an approach to compensate for the Doppler ambiguity within the batch is proposed for the first time. The proposed approach establishes a good coupling between the reference and echo signals by adding a frequency shift related to the Doppler frequency in the procedure of computing the cross ambiguity function (CAF). Simulation results show that the coherent integration gain of our approach is close to the theoretical upper bound even in the presence of Doppler ambiguity.

Differential factors, introduced by Tezcan and Özbudak at LightSec 2014, are properties of the S-boxes that equalize the counters of some guessed keys, thereby reducing the key space for the key guess process. Differential factors have been used to reduce the key space for the attacks on SERPENT, PRESENT, PRIDE, and RECTANGLE. In this paper, we demonstrate that some differential factors do not actually reduce the key space for the differential-linear attack on SERPENT and the related-key differential attack on RECTANGLE. Moreover, by comparing these instances with the differential attack on PRESENT, where differential factors do have an effect, we identify a sufficient condition for the practical use of differential factors. This condition enables preemptive identification of differential factors that could impact the key space for attacks on other ciphers.

Radio Frequency Identification (RFID) is crucial for the Internet of Things, with a key challenge being the efficient prevention of tag collisions for quick identification. This paper presents a novel approach for rapid tag recognition in small to medium-sized warehouses, combining a tag optimization feature set with a tail code recognition mechanism. To minimize the frequency of scanning for duplicate tags and reduce the occurrence of collisions, we construct an optimization feature set based on the reader's position. This set helps in assessing the likelihood of tag repetition through its linear variation. It also incorporates a tail code mechanism that recognizes only the last 22 digits of the tag's EPC code, significantly speeding up identification. The tail code length is dynamically adjusted based on the number of tags to maintain uniqueness. Simulation results indicate that our approach significantly reduces the identification of duplicate tags and minimizes the instances of collisions.